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| author | Jan Eilers <jan.eilers@arm.com> | 2021-02-25 17:44:00 +0000 |
|---|---|---|
| committer | Jan Eilers <jan.eilers@arm.com> | 2021-02-25 18:27:49 +0000 |
| commit | fd627ffaec8fd8801d980b4c91ee7c0607ab6aaf (patch) | |
| tree | eb4bc8f9b411f30c7655616142b5a4bdd3a1acd0 /21.02/_quantizer_test_8cpp_source.xhtml | |
| parent | fb14ebbd68e04876809145296af96f6f41857418 (diff) | |
| download | armnn-fd627ffaec8fd8801d980b4c91ee7c0607ab6aaf.tar.gz | |
IVGCVSW-5687 Update Doxygen Docu
* Update Doxygen Documentation for 21.02 release
Signed-off-by: Jan Eilers <jan.eilers@arm.com>
Change-Id: I9ed2f9caab038836ea99d7b378d7899fe431a4e5
Diffstat (limited to '21.02/_quantizer_test_8cpp_source.xhtml')
| -rw-r--r-- | 21.02/_quantizer_test_8cpp_source.xhtml | 300 |
1 files changed, 300 insertions, 0 deletions
diff --git a/21.02/_quantizer_test_8cpp_source.xhtml b/21.02/_quantizer_test_8cpp_source.xhtml new file mode 100644 index 0000000000..cb53b1bf3b --- /dev/null +++ b/21.02/_quantizer_test_8cpp_source.xhtml @@ -0,0 +1,300 @@ +<!-- Copyright (c) 2020 ARM Limited. --> +<!-- --> +<!-- SPDX-License-Identifier: MIT --> +<!-- --> +<!-- HTML header for doxygen 1.8.13--> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> +<html xmlns="http://www.w3.org/1999/xhtml"> +<head> +<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> +<meta http-equiv="X-UA-Compatible" content="IE=9"/> +<meta name="generator" content="Doxygen 1.8.13"/> +<meta name="robots" content="NOINDEX, NOFOLLOW" /> +<meta name="viewport" content="width=device-width, initial-scale=1"/> +<title>ArmNN: src/armnn/test/QuantizerTest.cpp Source File</title> +<link href="tabs.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="jquery.js"></script> +<script type="text/javascript" src="dynsections.js"></script> +<link href="navtree.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="resize.js"></script> +<script type="text/javascript" src="navtreedata.js"></script> +<script type="text/javascript" src="navtree.js"></script> +<script type="text/javascript"> + $(document).ready(initResizable); +</script> +<link href="search/search.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="search/searchdata.js"></script> +<script type="text/javascript" src="search/search.js"></script> +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + extensions: ["tex2jax.js"], + jax: ["input/TeX","output/HTML-CSS"], +}); +</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> +<link href="doxygen.css" rel="stylesheet" type="text/css" /> +<link href="stylesheet.css" rel="stylesheet" type="text/css"/> +</head> +<body> +<div id="top"><!-- do not remove this div, it is closed by doxygen! --> +<div id="titlearea"> +<table cellspacing="0" cellpadding="0"> + <tbody> + <tr style="height: 56px;"> + <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/> + <td style="padding-left: 0.5em;"> + <div id="projectname"> +  <span id="projectnumber">21.02</span> + </div> + </td> + </tr> + </tbody> +</table> +</div> +<!-- end header part --> +<!-- Generated by Doxygen 1.8.13 --> +<script type="text/javascript"> +var searchBox = new SearchBox("searchBox", "search",false,'Search'); +</script> +<script type="text/javascript" src="menudata.js"></script> +<script type="text/javascript" src="menu.js"></script> +<script type="text/javascript"> +$(function() { + initMenu('',true,false,'search.php','Search'); + $(document).ready(function() { init_search(); }); +}); +</script> +<div id="main-nav"></div> +</div><!-- top --> +<div id="side-nav" class="ui-resizable side-nav-resizable"> + <div id="nav-tree"> + <div id="nav-tree-contents"> + <div id="nav-sync" class="sync"></div> + </div> + </div> + <div id="splitbar" style="-moz-user-select:none;" + class="ui-resizable-handle"> + </div> +</div> +<script type="text/javascript"> +$(document).ready(function(){initNavTree('_quantizer_test_8cpp_source.xhtml','');}); +</script> +<div id="doc-content"> +<!-- window showing the filter options --> +<div id="MSearchSelectWindow" + onmouseover="return searchBox.OnSearchSelectShow()" + onmouseout="return searchBox.OnSearchSelectHide()" + onkeydown="return searchBox.OnSearchSelectKey(event)"> +</div> + +<!-- iframe showing the search results (closed by default) --> +<div id="MSearchResultsWindow"> +<iframe src="javascript:void(0)" frameborder="0" + name="MSearchResults" id="MSearchResults"> +</iframe> +</div> + +<div class="header"> + <div class="headertitle"> +<div class="title">QuantizerTest.cpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_quantizer_test_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "../Graph.hpp"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "../Network.hpp"</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include "../NetworkQuantizerUtils.hpp"</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include "../OverrideInputRangeVisitor.hpp"</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_tensor_8hpp.xhtml">armnn/Tensor.hpp</a>></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_types_8hpp.xhtml">armnn/Types.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <<a class="code" href="_ignore_unused_8hpp.xhtml">armnn/utility/IgnoreUnused.hpp</a>></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <<a class="code" href="_polymorphic_downcast_8hpp.xhtml">armnn/utility/PolymorphicDowncast.hpp</a>></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <<a class="code" href="_i_network_quantizer_8hpp.xhtml">armnnQuantizer/INetworkQuantizer.hpp</a>></span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <<a class="code" href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a>></span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include <boost/test/unit_test.hpp></span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> </div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include <unordered_map></span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> {</div><div class="line"><a name="l00025"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a997e96288bdb106c922202e3f33d5d7b"> 25</a></span> <span class="keyword">using</span> <a class="code" href="namespacearmnn.xhtml#a997e96288bdb106c922202e3f33d5d7b">MinMaxRange</a> = std::pair<float, float>;</div><div class="line"><a name="l00026"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#ac757baefa4b72b54c38f713f86418f8a"> 26</a></span> <span class="keyword">using</span> <a class="code" href="namespacearmnn.xhtml#ac757baefa4b72b54c38f713f86418f8a">MinMaxRanges</a> = std::vector<MinMaxRange>;</div><div class="line"><a name="l00027"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a061aafb62b3769f55369845c3990ec7a"> 27</a></span> <span class="keyword">using</span> <a class="code" href="namespacearmnn.xhtml#a061aafb62b3769f55369845c3990ec7a">MinMaxRangeMap</a> = std::unordered_map<LayerGuid, MinMaxRanges>;</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> </div><div class="line"><a name="l00029"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc"> 29</a></span> <span class="keyword">const</span> <span class="keywordtype">float</span> <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a> = 255.0f;</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="comment">// Coinciding with calcution which for AsymmS8 which calculates scale on an unsigned basis</span></div><div class="line"><a name="l00031"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95"> 31</a></span> <span class="keyword">const</span> <span class="keywordtype">float</span> <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a> = 255.0f;</div><div class="line"><a name="l00032"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b"> 32</a></span> <span class="keyword">const</span> <span class="keywordtype">float</span> <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a> = 127.0f;</div><div class="line"><a name="l00033"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4"> 33</a></span> <span class="keyword">const</span> <span class="keywordtype">float</span> <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a> = 32767.0f;</div><div class="line"><a name="l00034"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a1a9a6dea47de10df8e3c76dd45df56fb"> 34</a></span> <span class="keyword">const</span> <span class="keywordtype">float</span> <a class="code" href="namespacearmnn.xhtml#a1a9a6dea47de10df8e3c76dd45df56fb">g_TestTolerance</a> = 0.000001f;</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> </div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="keyword">class </span>TestConnectionPreservation : <span class="keyword">public</span> <a class="code" href="classarmnn_1_1_layer_visitor_base.xhtml">LayerVisitorBase</a><VisitorNoThrowPolicy></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> {</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  TestConnectionPreservation(<a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>* network)</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  : <a class="code" href="classarmnn_1_1_layer_visitor_base.xhtml">LayerVisitorBase<VisitorNoThrowPolicy></a>()</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  , m_Network(network)</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  {}</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keywordtype">void</span> VisitAdditionLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer, <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  CheckLayerName(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">GetConnection</a>()-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ad0c3555b126975ad6b3e250fe2a59534">GetOwningLayerGuid</a>(), <span class="stringliteral">"reLU1"</span>);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  CheckLayerName(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">GetConnection</a>()-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ad0c3555b126975ad6b3e250fe2a59534">GetOwningLayerGuid</a>(), <span class="stringliteral">"reLU2"</span>);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  }</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keywordtype">void</span> CheckLayerName(<a class="code" href="classarmnn_1_1profiling_1_1_profiling_guid.xhtml">LayerGuid</a> guid, std::string expectedName)</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  {</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keyword">auto</span> graph = m_Network->pNetworkImpl->GetGraph();</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordtype">bool</span> guidFound = <span class="keyword">false</span>;</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordflow">for</span> (<a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* layer : graph)</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  {</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">if</span> (layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afb5e65c770f6cee222db8af7581541a6">GetGuid</a>() == guid)</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  {</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  BOOST_CHECK_EQUAL(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>(), expectedName.c_str());</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  guidFound = <span class="keyword">true</span>;</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  }</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  }</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordflow">if</span> (!guidFound)</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  {</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  BOOST_FAIL(<span class="stringliteral">"No layer matching the GUID was found"</span>);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  }</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  }</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>* m_Network;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> };</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div><div class="line"><a name="l00073"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1"> 73</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>* inputNetwork, <a class="code" href="classarmnn_1_1_i_strategy.xhtml">IStrategy</a>& visitor)</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> {</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keyword">auto</span> graph = inputNetwork-><a class="code" href="classarmnn_1_1_i_network.xhtml#a4353fa80ece13e3b1664881c27f5a67c">pNetworkImpl</a>->GetGraph().TopologicalSort();</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> </div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="namespacearmnn.xhtml#a929027fc5caf6a6e20d3b9ac6fcd128b">ApplyStrategyToLayers</a>(graph, visitor);</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> }</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#adb77d7a922e1a8dee8490f50a5ae4bac"> 80</a></span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#adb77d7a922e1a8dee8490f50a5ae4bac">GetInputTensorInfo</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>* network)</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> {</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& inputLayer : network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a4353fa80ece13e3b1664881c27f5a67c">pNetworkImpl</a>->GetGraph().GetInputLayers())</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  {</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(inputLayer->GetNumOutputSlots() == 1, <span class="stringliteral">"Input layer should have exactly 1 output slot"</span>);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordflow">return</span> inputLayer->GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  }</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"Network has no input layers"</span>);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> }</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> </div><div class="line"><a name="l00090"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a4bdbaadf6d646207d326386034135d58"> 90</a></span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#adb77d7a922e1a8dee8490f50a5ae4bac">GetInputTensorInfo</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_network_impl.xhtml">NetworkImpl</a>* network)</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> {</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& inputLayer : network-><a class="code" href="classarmnn_1_1_network_impl.xhtml#afe0a4f719f9752a405e71878da7012ba">GetGraph</a>().<a class="code" href="classarmnn_1_1_graph.xhtml#a919fb58873ef3a6549e4490e226f2eae">GetInputLayers</a>())</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  {</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(inputLayer->GetNumOutputSlots() == 1, <span class="stringliteral">"Input layer should have exactly 1 output slot"</span>);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordflow">return</span> inputLayer->GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  }</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"Network has no input layers"</span>);</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> }</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> <a class="code" href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a>(Quantizer)</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> </div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> <span class="keyword">class </span>TestQuantization : <span class="keyword">public</span> <a class="code" href="classarmnn_1_1_i_strategy.xhtml">IStrategy</a></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> {</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  TestQuantization(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> &inputShape, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> &outputShape)</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  : m_InputShape(inputShape), m_OutputShape(outputShape), m_QuantizerOptions(<a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a>())</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  {}</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> </div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  TestQuantization(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a>& options, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape)</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  : m_InputShape(inputShape)</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  , m_OutputShape(outputShape)</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  , m_QuantizerOptions(options) {}</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> </div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keywordtype">void</span> ExecuteStrategy(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *layer,</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">BaseDescriptor</a> &descriptor,</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor> &constants,</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> *name,</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span>)<span class="keyword"> override</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(<span class="keywordtype">id</span>, name);</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> </div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keywordflow">if</span> (layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>)</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  {</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">GetConnection</a>()-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  BOOST_TEST(m_OutputShape == info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  }</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> </div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordflow">switch</span> (layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">GetType</a>())</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  {</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6ee06c6045d0c5b6565a247955ef0fc2">armnn::LayerType::BatchToSpaceNd</a> :</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">armnn::LayerType::Permute</a> :</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad662867a41bfb30b9f75dda2b5849001">armnn::LayerType::Pooling2d</a> :</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa7c59ccedc6a3bd90c17f3b990afefad">armnn::LayerType::Reshape</a> :</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a9d723d04c40bfd81835c0766a698cf63">armnn::LayerType::Resize</a> :</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a337c392144dca0d18290c6b4711a2279">armnn::LayerType::SpaceToBatchNd</a> :</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a41cb9b797ebc6f6f6314e3ded935f4cf">armnn::LayerType::Splitter</a> :</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa31904f2b3479b5a00137fd985974b4d">armnn::LayerType::StridedSlice</a> :</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  {</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  CheckDefaultQuantizationSettings(info);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  }</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f">armnn::LayerType::Addition</a> :</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  {</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> </div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="comment">// Based off default static range [-20.0f, 20.0f]</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  TestQuantizationParams(info, {40.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128},</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  {40.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  {20.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  {20.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0});</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  }</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">armnn::LayerType::Activation</a> :</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  {</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a>& activationDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> </div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keywordflow">switch</span> (activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a>)</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  {</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">ActivationFunction::BoundedReLu</a> :</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  {</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="comment">// Based off default static range [0.0f, 3.5f]</span></div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  TestQuantizationParams(info, {3.5f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 0},</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  {3.5f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, -128},</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  {3.5f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  {3.5f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0});</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  }</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaed67cf7d54c570e4c4891800f085f41d">ActivationFunction::Elu</a> :</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  {</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  TestQuantizationParams(</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  info, {30.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128},</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  {30.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  {15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  {15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0});</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  }</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186">ActivationFunction::HardSwish</a> :</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  {</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  TestQuantizationParams(info, {30.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128},</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  {30.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  {15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  {15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0});</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  }</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaacb7667f5ec2f6e8a5943b781ba6c2735">ActivationFunction::LeakyReLu</a> :</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  {</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="comment">// Based off default static range [-5.0f, 15.0f]</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  TestQuantizationParams(info, {20.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 64},</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  {20.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>,-64},</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  {15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a> , 0},</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  {15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0});</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  }</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">ActivationFunction::TanH</a> :</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  {</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  TestQuantizationParams(info, {2.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128},</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  {2.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  {1.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a> , 0},</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  {1.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0});</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  }</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  {</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="comment">// Based off default static range [0.0f, 15.0f]</span></div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  TestQuantizationParams(info, {15.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 0},</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  {15.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, -128},</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  {15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  {15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0});</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  }</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  }</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  }</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2139684546b147c106b329f41547640c">armnn::LayerType::ArgMinMax</a> :</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  {</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml">ArgMinMaxDescriptor</a>& argMinMaxDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml">ArgMinMaxDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span> </div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keywordflow">if</span>(argMinMaxDescriptor.<a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml#ab1ae6f520bb1a4da191a0ae907477f23">m_Function</a> == <a class="code" href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">ArgMinMaxFunction::Max</a>)</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  {</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  }</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  TestQuantizationParams(info,</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  { 30.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128 },</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  { 30.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  { 15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  { 15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0 });</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  }</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae4743c3ec15d1d84169b17264634692e">armnn::LayerType::BatchNormalization</a> :</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  {</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> </div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="comment">// Based off default static range [-15.0f, 15.0f]</span></div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  TestQuantizationParams(</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  info, {30.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128},</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  {30.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  {15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  {15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0});</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> </div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="comment">// Test constants</span></div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  TestConstantQuantizationParams(constants[0].GetInfo(), {3.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 85});</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  TestConstantQuantizationParams(constants[1].GetInfo(), {3.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 85});</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  TestConstantQuantizationParams(constants[2].GetInfo(), {3.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 85});</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  TestConstantQuantizationParams(constants[3].GetInfo(), {3.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 85});</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  }</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af6c0e3a1c3cfabd32ae8d3ae741fcf0a">armnn::LayerType::Comparison</a> :</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  {</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> </div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qAsymmU8Params{ 30.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128 };</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qAsymmS8Params { 30.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0};</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qSymmS8Params { 15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0};</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qSymmS16Params{ 15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0 };</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> </div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  TestQuantizationParams(info, qAsymmU8Params, qAsymmS8Params, qSymmS8Params, qSymmS16Params);</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> </div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  }</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255">armnn::LayerType::Constant</a> :</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  {</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> </div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="comment">// Based off the range of values in the const tensor used for the test: [-2.0f, 6.0f]</span></div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  TestQuantizationParams(info, {8.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 64},</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  {8.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, -64},</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  {6.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  {6.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0});</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span> </div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  }</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a">armnn::LayerType::Convolution2d</a> :</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  {</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keywordflow">if</span> (constants.size() == 1)</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  {</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  TestQuantizationOnLayersWithBiases(layer, constants[0], <a class="code" href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a>());</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  }</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (constants.size() == 1)</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  {</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  TestQuantizationOnLayersWithBiases(layer, constants[0], constants[1]);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  }</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  }</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af97adbfc88b7012a0243215b1076e7e7">armnn::LayerType::DepthwiseConvolution2d</a> :</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  {</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="keywordflow">if</span> (constants.size() == 2)</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  {</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  TestQuantizationOnLayersWithBiases(layer, constants[0], constants[1]);</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  }</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (constants.size() == 1)</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  {</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  TestQuantizationOnLayersWithBiases(layer, constants[0], <a class="code" href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a>());</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  }</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  }</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a731729ad1b2c0eb9399b62c770b3482d">armnn::LayerType::DepthToSpace</a> :</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  {</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qAsymmU8Params{30.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128};</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qAsymmS8Params{30.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0};</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qSymmS8Params{15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0};</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qSymmS16Params{15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0};</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> </div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  TestQuantizationParams(info, qAsymmU8Params, qAsymmS8Params, qSymmS8Params, qSymmS16Params);</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  }</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acab78faff25393e9defd1911cb58133e">armnn::LayerType::FullyConnected</a> :</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  {</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="keywordflow">if</span> (constants.size() == 2)</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  {</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  TestQuantizationOnLayersWithBiases(layer, constants[0], constants[1]);</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  }</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (constants.size() == 1)</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  {</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  TestQuantizationOnLayersWithBiases(layer, constants[0], <a class="code" href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a>());</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  }</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span> </div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  }</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4adb3e3f51c9107e26c9bccf9a188ce2ed">armnn::LayerType::Fill</a> :</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  {</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qAsymmU8Params{ 30.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128 };</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qAsymmS8Params { 30.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0};</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qSymmS8Params { 15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0};</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qSymmS16Params{ 15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0 };</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span> </div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  TestQuantizationParams(info, qAsymmU8Params, qAsymmS8Params, qSymmS8Params, qSymmS16Params);</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  }</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a> :</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  {</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  BOOST_TEST(m_InputShape == info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <span class="comment">// Based off current default [-15.0f, 15.0f]</span></div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  TestQuantizationParams(info, {30.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128},</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  {30.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  {15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  {15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0});</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  }</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a21baa4498161d195f5bb2e3627344ba4">armnn::LayerType::InstanceNormalization</a> :</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  {</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qAsymmU8Params{ 30.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128 };</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qAsymmS8Params { 30.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0};</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qSymmS8Params { 15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0};</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qSymmS16Params{ 15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0 };</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span> </div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  TestQuantizationParams(info, qAsymmU8Params, qAsymmS8Params, qSymmS8Params, qSymmS16Params);</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  }</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ac21dbda57d88c21ec9857f5d1522c488">armnn::LayerType::LogSoftmax</a> :</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  {</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qAsymmU8Params{ 30.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128 };</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qAsymmS8Params { 30.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0};</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qSymmS8Params { 15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0};</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qSymmS16Params{ 15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0 };</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> </div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  TestQuantizationParams(info, qAsymmU8Params, qAsymmS8Params, qSymmS8Params, qSymmS16Params);</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  }</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad140d37ad98c12ccd8e1c432f548bcdb">armnn::LayerType::Slice</a> :</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  {</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qAsymmU8Params{ 30.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128 };</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qAsymmS8Params{ 30.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0 };</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qSymmS8Params { 15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0 };</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qSymmS16Params{ 15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0 };</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> </div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  TestQuantizationParams(info, qAsymmU8Params, qAsymmS8Params, qSymmS8Params, qSymmS16Params);</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  }</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a31d953b9d49a6b4378f45097047976d0">armnn::LayerType::Softmax</a> :</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  {</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="comment">// Based off default static range [0.0f, 1.0f]</span></div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  TestQuantizationParams(info, {1.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 0},</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  {1.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, -128},</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  {1.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  {1.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0});</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  }</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a5e7ff12da912dc79e7e547281823fa4a">armnn::LayerType::SpaceToDepth</a> :</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  {</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  TestQuantizationParams(info,</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  { 30.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128 },</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  { 30.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0 },</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  { 15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0 },</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  { 15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0 });</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> </div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  }</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2187e1021a911b3807cc1bea2eb1a9ca">armnn::LayerType::Stack</a> :</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  {</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> </div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  TestQuantizationParams(outputInfo,</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  { 30.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128 },</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  { 30.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  { 15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  { 15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0 });</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  }</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a71b23d26c0f5d20416d6c77754f9806a">armnn::LayerType::TransposeConvolution2d</a> :</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  {</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keywordflow">if</span> (constants.size() == 2)</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  {</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  TestQuantizationOnLayersWithBiases(layer, constants[0], constants[1]);</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  }</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (constants.size() == 1)</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  {</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  TestQuantizationOnLayersWithBiases(layer, constants[0], <a class="code" href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a>());</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  }</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  }</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  {</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_unimplemented_exception.xhtml">UnimplementedException</a>(<span class="stringliteral">"Unimplemented layer encountered"</span>);</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  }</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  }</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  }</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span> </div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span> </div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> <span class="keyword">protected</span>:</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> </div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keywordtype">void</span> CheckDefaultQuantizationSettings(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  {</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  TestQuantizationParams(info, {20.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 64},</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  {20.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>,-64},</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  {15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  {15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0});</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  }</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span> </div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <span class="keywordtype">void</span> TestQuantizationParams(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& info,</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a>& qAsymmU8Params,</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a>& qAsymmS8Params,</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a>& qSymmS8Params,</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a>& qSymmS16Params)</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  {</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <span class="keywordflow">switch</span> (m_QuantizerOptions.m_ActivationFormat)</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  {</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>:</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  TestQuantizationParamsImpl(</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  info, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>, qAsymmU8Params.first, qAsymmU8Params.second);</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>:</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  TestQuantizationParamsImpl(</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  info, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>, qAsymmS8Params.first, qAsymmS8Params.second);</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>:</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  TestQuantizationParamsImpl(</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  info, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>, qSymmS8Params.first, qSymmS8Params.second);</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>:</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  TestQuantizationParamsImpl(</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  info, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>, qSymmS16Params.first, qSymmS16Params.second);</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"Unsupported quantization target"</span>);</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  }</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  }</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span> </div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <span class="keywordtype">void</span> TestDifferentQuantizationScale(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& info0, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& info1)</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  {</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  BOOST_TEST(info0.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>() != info1.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>());</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  }</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span> </div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keywordtype">void</span> TestConstantQuantizationParams(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& info,</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a>& params,</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dataType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>)</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  {</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(dataType);</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  TestQuantizationParamsImpl(info, dataType, params.first, params.second);</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  }</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span> </div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <span class="keywordtype">void</span> TestBiasQuantizationParams(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& info,</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a>& qAsymmU8Params,</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a>& qAsymmS8Params,</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a>& qSymmS8Params,</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a>& qSymmS16Params,</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dataType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>)</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  {</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <span class="keywordflow">switch</span> (m_QuantizerOptions.m_ActivationFormat)</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  {</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>:</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  TestQuantizationParamsImpl(info, dataType, qAsymmU8Params.first, qAsymmU8Params.second);</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>:</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  TestQuantizationParamsImpl(info, dataType, qAsymmS8Params.first, qAsymmS8Params.second);</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>:</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  TestQuantizationParamsImpl(info, dataType, qSymmS8Params.first, qSymmS8Params.second);</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>:</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  TestQuantizationParamsImpl(info, dataType, qSymmS16Params.first, qSymmS16Params.second);</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"Unsupported quantization target"</span>);</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  }</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  }</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span> </div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <span class="keywordtype">void</span> TestQuantizationOnLayersWithBiases(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer,</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>& weights,</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>& biases)</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  {</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> info = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <span class="keywordtype">float</span> inputScaleQAsymmU8 = 30.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>;</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  <span class="keywordtype">float</span> inputScaleQAsymmS8 = 30.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>;</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <span class="keywordtype">float</span> inputScaleQSymmS8 = 15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>;</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  <span class="keywordtype">float</span> inputScaleQSymmS16 = 15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>;</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  <span class="keywordtype">float</span> weightsScale = 3.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>;</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span> </div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <span class="comment">// Based off default static range [-15.0f, 15.0f]</span></div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  TestQuantizationParams(info, {inputScaleQAsymmU8, 128},</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  {inputScaleQAsymmS8, 0},</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  {inputScaleQSymmS8, 0},</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  {inputScaleQSymmS16, 0});</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span> </div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  TestConstantQuantizationParams(weights.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>(), {weightsScale, 85});</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span> </div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <span class="keywordflow">if</span> (biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>())</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  {</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  TestBiasQuantizationParams(biases.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>().GetInfo(),</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  {inputScaleQAsymmU8 * weightsScale, 0},</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  {inputScaleQAsymmS8 * weightsScale, 0},</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  {inputScaleQSymmS8 * weightsScale, 0},</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  {inputScaleQSymmS16 * weightsScale, 0},</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>);</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  }</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  }</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span> </div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> m_InputShape;</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> m_OutputShape;</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span> </div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <span class="keywordtype">void</span> TestQuantizationParamsImpl(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& info, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dataType, <span class="keywordtype">float</span> scale, int32_t offset)</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  {</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  BOOST_TEST((info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == dataType));</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  BOOST_TEST(info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>() == offset);</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  BOOST_CHECK_CLOSE(info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(), scale, <a class="code" href="namespacearmnn.xhtml#a1a9a6dea47de10df8e3c76dd45df56fb">g_TestTolerance</a>);</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  }</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span> </div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a> m_QuantizerOptions;</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span> };</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span> </div><div class="line"><a name="l00540"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb"> 540</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(<a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>* network, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inShape, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outShape)</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span> {</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a> qAsymmU8Options(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantizedNetworkQAsymmU8 = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network, qAsymmU8Options)->ExportNetwork();</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  TestQuantization validatorQAsymmU8(inShape, outShape);</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(quantizedNetworkQAsymmU8.get(), validatorQAsymmU8);</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span> </div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a> qAsymmS8Options(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>);</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantizedNetworkQAsymmS8 = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network, qAsymmS8Options)->ExportNetwork();</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  TestQuantization validatorQAsymmS8(qAsymmS8Options, inShape, outShape);</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(quantizedNetworkQAsymmS8.get(), validatorQAsymmS8);</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span> </div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a> qSymmS8Options(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>);</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantizedNetworkQSymmS8 = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network, qSymmS8Options)->ExportNetwork();</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  TestQuantization validatorQSymmS8(qSymmS8Options, inShape, outShape);</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(quantizedNetworkQSymmS8.get(), validatorQSymmS8);</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span> </div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a> qSymmS16options(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>);</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantizedNetworkQSymmS16 = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network, qSymmS16options)->ExportNetwork();</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  TestQuantization validatorQSymmS16(qSymmS16options, inShape, outShape);</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(quantizedNetworkQSymmS16.get(), validatorQSymmS16);</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span> }</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span> </div><div class="line"><a name="l00563"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#add41518f12c5f76fe1d64e197010f52c"> 563</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(<a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>* network, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape)</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span> {</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network, shape, shape);</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span> }</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span> </div><div class="line"><a name="l00568"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a8baf97065d802063eb9bcdd1a066dc86"> 568</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeAddition)</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span> {</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span> </div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="comment">// Add the layers</span></div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = network->AddInputLayer(0);</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = network->AddInputLayer(1);</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* addition = network->AddAdditionLayer();</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(2);</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span> </div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(addition-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  input1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(addition-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  addition-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span> </div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <span class="comment">// Set TensorInfo</span></div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  input1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  addition-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span> </div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span> }</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span> </div><div class="line"><a name="l00593"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a5fbc1479db5f4ff70a750cf02d58971b"> 593</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="namespacearmnn.xhtml#a5fbc1479db5f4ff70a750cf02d58971b">CreateNetworkWithActivationLayer</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a>& descriptor, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& shape)</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span> {</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span> </div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  <span class="comment">// Add the layers</span></div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = network->AddInputLayer(0);</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activation = network->AddActivationLayer(descriptor);</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(2);</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span> </div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(activation-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  activation-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span> </div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <span class="comment">// Set TensorInfo</span></div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  activation-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span> </div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  <span class="keywordflow">return</span> network;</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span> }</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span> </div><div class="line"><a name="l00614"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a3109511a0984c50aae123ff702f327d2"> 614</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="namespacearmnn.xhtml#a3109511a0984c50aae123ff702f327d2">CreateNetworkWithArgMinMaxLayer</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml">ArgMinMaxDescriptor</a>& descriptor, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& shape)</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span> {</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span> </div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <span class="comment">// Add the layers</span></div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = network->AddInputLayer(0);</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activation = network->AddArgMinMaxLayer(descriptor);</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(2);</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span> </div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(activation-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  activation-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span> </div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  <span class="comment">// Set TensorInfo</span></div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inInfo(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inInfo);</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outInfo({1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>);</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  activation-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outInfo);</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span> </div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  <span class="keywordflow">return</span> network;</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span> }</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span> </div><div class="line"><a name="l00636"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#aa9c6c1a7b5380a99a536f4740f87dd59"> 636</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="namespacearmnn.xhtml#aa9c6c1a7b5380a99a536f4740f87dd59">CreateNetworkWithInputOutputLayers</a>()</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span> {</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span> </div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <span class="comment">// Add input/output layers</span></div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(1);</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span> </div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span> </div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <span class="comment">// Set TensorInfo</span></div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{8U};</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span> </div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  <span class="keywordflow">return</span> network;</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span> }</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span> </div><div class="line"><a name="l00655"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a9cec088786b209989fe9e04e1be9636d"> 655</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(InputOutputLayerDynamicQuant)</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span> {</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="namespacearmnn.xhtml#aa9c6c1a7b5380a99a536f4740f87dd59">CreateNetworkWithInputOutputLayers</a>();</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span> </div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo = <a class="code" href="namespacearmnn.xhtml#adb77d7a922e1a8dee8490f50a5ae4bac">GetInputTensorInfo</a>(network.get());</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span> </div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <span class="comment">// Outliers -56 and 98</span></div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  std::vector<float> inputData({0, 0, 0, -56, 98, 0, 0, 0});</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputTensor(tensorInfo, inputData.data());</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span> </div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors;</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  inputTensors.push_back(std::make_pair(0, inputTensor));</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span> </div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  <a class="code" href="namespacearmnn.xhtml#a41119e261eec9343888d2ceab1e4999a">armnn::INetworkQuantizerPtr</a> quantizer = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">armnn::INetworkQuantizer::Create</a>(network.get());</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span> </div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  quantizer->Refine(inputTensors);</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span> </div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <span class="comment">// Outliers -77 and 65</span></div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  std::vector<float> inputData2({0, -77, 0, -56, 65, 0, 0, 0});</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputTensor2(tensorInfo, inputData2.data());</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors2;</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  inputTensors2.push_back(std::make_pair(0, inputTensor2));</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span> </div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  quantizer->Refine(inputTensors2);</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span> </div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantizedNetwork = quantizer->ExportNetwork();</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  <span class="comment">// Output Layer should be quantized for a min max of -77 and 98</span></div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  <span class="comment">// according to QU8 Quantization Scheme</span></div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  std::unique_ptr<IQuantizationScheme> quantizationScheme = std::make_unique<QAsymmU8QuantizationScheme>();</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qParams = quantizationScheme->ComputeScheme(-77.0, 98.0);</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span> </div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span> <span class="keyword">class </span>TestOutputStrategy : <span class="keyword">public</span> <a class="code" href="classarmnn_1_1_i_strategy.xhtml">IStrategy</a></div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span> {</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  public :</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  TestOutputStrategy(<span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a>& offsetScalePair, <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>& dataType) :</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  m_OffsetScalePair(offsetScalePair), m_DataType(dataType) {}</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span> </div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  <span class="keywordtype">void</span> ExecuteStrategy(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span>)<span class="keyword"> override</span></div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(name, constants, <span class="keywordtype">id</span>, descriptor);</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span> </div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  <span class="keywordflow">switch</span> (layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">GetType</a>())</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  {</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a> :</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  {</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> &<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">GetConnection</a>()-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  BOOST_CHECK_MESSAGE(info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == m_DataType,</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  std::string(<a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">armnn::GetDataTypeName</a>(info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()))</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  .append(<span class="stringliteral">" == "</span>).append(<a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">armnn::GetDataTypeName</a>(m_DataType)));</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  <span class="comment">// int_32t</span></div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  BOOST_CHECK(info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>() == m_OffsetScalePair.second);</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  <span class="comment">// float</span></div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  BOOST_TEST(info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>() == m_OffsetScalePair.first,</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  boost::test_tools::tolerance(0.001));</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  }</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  {}</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  }</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  }</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span> </div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> m_OffsetScalePair;</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> m_DataType;</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span> };</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span> </div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  TestOutputStrategy strategy(qParams, quantizationScheme->GetDataType());</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  quantizedNetwork->ExecuteStrategy(strategy);</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span> }</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span> </div><div class="line"><a name="l00729"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a7db6a78bb6eedbea7f0525f1fe59de28"> 729</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeAbsActivation)</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span> {</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> descriptor;</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa1e34af023adeb7d5f484f8eb4b9826b6">ActivationFunction::Abs</a>;</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 3.5f;</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -10.0f;</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span> </div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="namespacearmnn.xhtml#a5fbc1479db5f4ff70a750cf02d58971b">CreateNetworkWithActivationLayer</a>(descriptor, shape);</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span> </div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span> }</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span> </div><div class="line"><a name="l00742"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a93517fe9f55ec8749fa61e8cf303cad4"> 742</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeArgMax)</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span> {</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  <a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml">ArgMinMaxDescriptor</a> descriptor;</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  descriptor.<a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml#ab1ae6f520bb1a4da191a0ae907477f23">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">ArgMinMaxFunction::Max</a>;</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span> </div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="namespacearmnn.xhtml#a3109511a0984c50aae123ff702f327d2">CreateNetworkWithArgMinMaxLayer</a>(descriptor, shape);</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span> </div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span> }</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span> </div><div class="line"><a name="l00753"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a2df3b432de50a9b9e8b486aa53e11cc5"> 753</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeLinearActivation)</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span> {</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> descriptor;</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa32a843da6ea40ab3b17a3421ccdf671b">ActivationFunction::Linear</a>;</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 3.5f;</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -10.0f;</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span> </div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="namespacearmnn.xhtml#a5fbc1479db5f4ff70a750cf02d58971b">CreateNetworkWithActivationLayer</a>(descriptor, shape);</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span> </div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span> </div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span> }</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span> </div><div class="line"><a name="l00767"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a3dd219b394b8186d1849ee595193268d"> 767</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeReLuActivation)</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span> {</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> descriptor;</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 3.5f;</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -10.0f;</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span> </div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="namespacearmnn.xhtml#a5fbc1479db5f4ff70a750cf02d58971b">CreateNetworkWithActivationLayer</a>(descriptor, shape);</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span> </div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span> }</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span> </div><div class="line"><a name="l00780"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a52e948b4bffc16a3933d812dbc384833"> 780</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeSoftReLuActivation)</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span> {</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> descriptor;</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa6bba7052636d1740303b1b2ef3b53fef">ActivationFunction::SoftReLu</a>;</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 3.5f;</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -10.0f;</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span> </div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="namespacearmnn.xhtml#a5fbc1479db5f4ff70a750cf02d58971b">CreateNetworkWithActivationLayer</a>(descriptor, shape);</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span> </div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span> }</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span> </div><div class="line"><a name="l00793"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#abf109580225cb949565c8223bceadd5d"> 793</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeBoundedReluActivation)</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span> {</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> descriptor;</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">ActivationFunction::BoundedReLu</a>;</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 3.5f;</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -10.0f;</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span> </div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="namespacearmnn.xhtml#a5fbc1479db5f4ff70a750cf02d58971b">CreateNetworkWithActivationLayer</a>(descriptor, shape);</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span> </div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span> }</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span> </div><div class="line"><a name="l00806"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#acbf871a6ec0726bfe2746e761a278108"> 806</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeTanHActivation)</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span> {</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> descriptor;</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">ActivationFunction::TanH</a>;</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 3.5f;</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -10.0f;</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span> </div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="namespacearmnn.xhtml#a5fbc1479db5f4ff70a750cf02d58971b">CreateNetworkWithActivationLayer</a>(descriptor, shape);</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span> </div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span> }</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span> </div><div class="line"><a name="l00819"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a32068047cc7b37f1bed1830508891526"> 819</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeLeakyReLuActivation)</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span> {</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> descriptor;</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaacb7667f5ec2f6e8a5943b781ba6c2735">ActivationFunction::LeakyReLu</a>;</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 3.5f;</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -10.0f;</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span> </div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="namespacearmnn.xhtml#a5fbc1479db5f4ff70a750cf02d58971b">CreateNetworkWithActivationLayer</a>(descriptor, shape);</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span> </div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span> }</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span> </div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span> </div><div class="line"><a name="l00833"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a6c08ed3db08fcfca0592c62cd6080b76"> 833</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeELuActivation)</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span> {</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> descriptor;</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaed67cf7d54c570e4c4891800f085f41d">ActivationFunction::Elu</a>;</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span> </div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="namespacearmnn.xhtml#a5fbc1479db5f4ff70a750cf02d58971b">CreateNetworkWithActivationLayer</a>(descriptor, shape);</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span> </div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span> }</div><div class="line"><a name="l00843"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#ab182b6a1d2348a86472001c92586717a"> 843</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeHardSwishActivation)</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span> {</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> descriptor;</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186">ActivationFunction::HardSwish</a>;</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span> </div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="namespacearmnn.xhtml#a5fbc1479db5f4ff70a750cf02d58971b">CreateNetworkWithActivationLayer</a>(descriptor, shape);</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span> </div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span> }</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span> </div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span> </div><div class="line"><a name="l00855"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#adf59f87645d301e9b56dd70aed350e54"> 855</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeBatchNorm)</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span> {</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span> </div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{3U};</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span> </div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  std::vector<float> meanData{-1.0f, 1.5f, 2.0f};</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  std::vector<float> varData{-1.0f, 1.5f, 2.0f};</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  std::vector<float> betaData{-1.0f, 1.5f, 2.0f};</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  std::vector<float> gammaData{-1.0f, 1.5f, 2.0f};</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span> </div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> mean(info, meanData);</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> var(info, varData);</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> beta(info, betaData);</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> gamma(info, gammaData);</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span> </div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">BatchNormalizationDescriptor</a> desc;</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span> </div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  <span class="comment">// Add the layers</span></div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = network->AddInputLayer(0);</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* batchNorm = network->AddBatchNormalizationLayer(desc, mean, var, beta, gamma);</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(1);</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span> </div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(batchNorm-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  batchNorm-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span> </div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  <span class="comment">// Set TensorInfo</span></div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  batchNorm-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span> </div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span> }</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span> </div><div class="line"><a name="l00890"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#ae91bc23bf56bb5f9c2e0ddb1fc7be75e"> 890</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeDepthToSpace)</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span> {</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape { 1, 2, 2, 4 };</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape{ 1, 4, 4, 1 };</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span> </div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo (inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span> </div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml">DepthToSpaceDescriptor</a> descriptor(2, <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>);</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span> </div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* depthToSpaceLayer = network->AddDepthToSpaceLayer(descriptor);</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span> </div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(depthToSpaceLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  depthToSpaceLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span> </div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  depthToSpaceLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span> </div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), inputShape, outputShape);</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span> }</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span> </div><div class="line"><a name="l00914"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#aa6281ed3090b74167170c8f692688de5"> 914</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(OverrideInputRangeEmptyNetwork)</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span> {</div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span>  <a class="code" href="classarmnn_1_1_range_tracker.xhtml">RangeTracker</a> ranges;</div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  <a class="code" href="classarmnn_1_1_range_tracker.xhtml#a997e96288bdb106c922202e3f33d5d7b">RangeTracker::MinMaxRange</a> minMaxRange(-12.3f, 45.6f); <span class="comment">// Range to use for the override</span></div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span> </div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  <a class="code" href="classarmnn_1_1_network_impl.xhtml">NetworkImpl</a> network; <span class="comment">// Empty network</span></div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  <span class="keyword">auto</span> inputLayers = network.<a class="code" href="classarmnn_1_1_network_impl.xhtml#afe0a4f719f9752a405e71878da7012ba">GetGraph</a>().<a class="code" href="classarmnn_1_1_graph.xhtml#a919fb58873ef3a6549e4490e226f2eae">GetInputLayers</a>(); <span class="comment">// Empty list of input layers</span></div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span> </div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  <a class="code" href="classarmnn_1_1_override_input_range_strategy.xhtml">OverrideInputRangeStrategy</a> overrideInputRangeStrategy(ranges, 0, minMaxRange);</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  <a class="code" href="namespacearmnn.xhtml#a929027fc5caf6a6e20d3b9ac6fcd128b">ApplyStrategyToLayers</a>(inputLayers, overrideInputRangeStrategy);</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span> </div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  BOOST_CHECK(ranges.<a class="code" href="classarmnn_1_1_range_tracker.xhtml#a8e12342fc420701fbffd97025421575a">IsEmpty</a>()); <span class="comment">// Check that the map of ranges remained untouched</span></div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span> }</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span> </div><div class="line"><a name="l00928"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#ad432424d97021ae6c81e38130b1ec5d6"> 928</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(OverrideInputRangeNoInputLayers)</div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span> {</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  <a class="code" href="classarmnn_1_1_range_tracker.xhtml">RangeTracker</a> ranges;</div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  <a class="code" href="namespacearmnn.xhtml#a997e96288bdb106c922202e3f33d5d7b">MinMaxRange</a> minMaxRange(-12.3f, 45.6f); <span class="comment">// Range to use for the override</span></div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span> </div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  <a class="code" href="classarmnn_1_1_network_impl.xhtml">NetworkImpl</a> network;</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  network.<a class="code" href="classarmnn_1_1_network_impl.xhtml#a39f1b38d89c4de186742eafcbb3b1319">AddAdditionLayer</a>(); <span class="comment">// Network with no input layers</span></div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  <span class="keyword">auto</span> inputLayers = network.<a class="code" href="classarmnn_1_1_network_impl.xhtml#afe0a4f719f9752a405e71878da7012ba">GetGraph</a>().<a class="code" href="classarmnn_1_1_graph.xhtml#a919fb58873ef3a6549e4490e226f2eae">GetInputLayers</a>(); <span class="comment">// Empty list of input layers</span></div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span> </div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  <a class="code" href="classarmnn_1_1_override_input_range_strategy.xhtml">OverrideInputRangeStrategy</a> overrideInputRangeStrategy(ranges, 0, minMaxRange);</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  <a class="code" href="namespacearmnn.xhtml#a929027fc5caf6a6e20d3b9ac6fcd128b">ApplyStrategyToLayers</a>(inputLayers, overrideInputRangeStrategy);</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span> </div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  BOOST_CHECK(ranges.<a class="code" href="classarmnn_1_1_range_tracker.xhtml#a8e12342fc420701fbffd97025421575a">IsEmpty</a>()); <span class="comment">// Check that the map of ranges remained untouched</span></div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span> }</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span> </div><div class="line"><a name="l00943"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a6e97e093453fc053a5c82386927a0d6c"> 943</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(OverrideInputRangeInputLayers)</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span> {</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  <a class="code" href="classarmnn_1_1_range_tracker.xhtml">RangeTracker</a> ranges;</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  <a class="code" href="namespacearmnn.xhtml#a997e96288bdb106c922202e3f33d5d7b">MinMaxRange</a> minMaxRange(-12.3f, 45.6f); <span class="comment">// Range to use for the override</span></div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span> </div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  <a class="code" href="classarmnn_1_1_network_impl.xhtml">NetworkImpl</a> network;</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span> </div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  <span class="comment">// Adding the layers</span></div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = network.<a class="code" href="classarmnn_1_1_network_impl.xhtml#aa6c1c42ea44777302e87ce0fad5ac510">AddInputLayer</a>(0);</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = network.<a class="code" href="classarmnn_1_1_network_impl.xhtml#aa6c1c42ea44777302e87ce0fad5ac510">AddInputLayer</a>(1);</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* addition = network.<a class="code" href="classarmnn_1_1_network_impl.xhtml#a39f1b38d89c4de186742eafcbb3b1319">AddAdditionLayer</a>();</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network.<a class="code" href="classarmnn_1_1_network_impl.xhtml#af5790069aa11fd1c5bb2e17cecb06528">AddOutputLayer</a>(2);</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span> </div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  <span class="comment">// Connecting the layer</span></div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(addition-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  input1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(addition-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  addition-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span> </div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  <span class="comment">// Setting the TensorInfos</span></div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  input1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  addition-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span> </div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  <span class="keyword">auto</span> inputLayers = network.<a class="code" href="classarmnn_1_1_network_impl.xhtml#afe0a4f719f9752a405e71878da7012ba">GetGraph</a>().<a class="code" href="classarmnn_1_1_graph.xhtml#a919fb58873ef3a6549e4490e226f2eae">GetInputLayers</a>(); <span class="comment">// List of input layers</span></div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span> </div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  <span class="comment">// Trying to override the input range for the input layer with binding id 3 (does not exist in the network)</span></div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  <a class="code" href="classarmnn_1_1_override_input_range_strategy.xhtml">OverrideInputRangeStrategy</a> overrideInputRangeStrategy3(ranges, 3, minMaxRange);</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  <a class="code" href="namespacearmnn.xhtml#a929027fc5caf6a6e20d3b9ac6fcd128b">ApplyStrategyToLayers</a>(inputLayers, overrideInputRangeStrategy3);</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span> </div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  <span class="comment">// Check that the map of ranges remained untouched</span></div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  BOOST_CHECK(ranges.<a class="code" href="classarmnn_1_1_range_tracker.xhtml#a8e12342fc420701fbffd97025421575a">IsEmpty</a>());</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span> </div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  <span class="comment">// Override the input range for the input layer with binding id 1</span></div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  <a class="code" href="classarmnn_1_1_override_input_range_strategy.xhtml">OverrideInputRangeStrategy</a> overrideInputRangeStrategy1(ranges, 1, minMaxRange);</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  <a class="code" href="namespacearmnn.xhtml#a929027fc5caf6a6e20d3b9ac6fcd128b">ApplyStrategyToLayers</a>(inputLayers, overrideInputRangeStrategy1);</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span> </div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  <span class="comment">// Check that the map of ranges has been populated</span></div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  BOOST_CHECK(!ranges.<a class="code" href="classarmnn_1_1_range_tracker.xhtml#a8e12342fc420701fbffd97025421575a">IsEmpty</a>());</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span> </div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  <span class="comment">// Check that an entry for the input layer with binding id 0 does not exist</span></div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  BOOST_CHECK(!ranges.<a class="code" href="classarmnn_1_1_range_tracker.xhtml#a084c5aacd7e3bb07f2cfd5a8e9b0dd30">HasRanges</a>(input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afb5e65c770f6cee222db8af7581541a6">GetGuid</a>()));</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span> </div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  <span class="comment">// Check that an entry for the input layer with binding id 1 exists</span></div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span>  BOOST_CHECK(ranges.<a class="code" href="classarmnn_1_1_range_tracker.xhtml#a084c5aacd7e3bb07f2cfd5a8e9b0dd30">HasRanges</a>(input1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afb5e65c770f6cee222db8af7581541a6">GetGuid</a>()));</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span> </div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  <span class="comment">// Check the the overridden values are what we intended to set</span></div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  BOOST_CHECK(ranges.<a class="code" href="classarmnn_1_1_range_tracker.xhtml#a507bae23f59e94b4161886ebe663cdf4">GetRange</a>(input1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afb5e65c770f6cee222db8af7581541a6">GetGuid</a>(), 0) == minMaxRange);</div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span> }</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span> </div><div class="line"><a name="l00994"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#aad4b8cb9a4d882a48bc21510f0d1a938"> 994</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="namespacearmnn.xhtml#aad4b8cb9a4d882a48bc21510f0d1a938">CreateNetworkWithFullyConnectedLayer</a>(<span class="keyword">const</span> <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape,</div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape)</div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span> {</div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> desc;</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  desc.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span> </div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span> </div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  std::vector<float> weightsData{-1.0f, 1.5f, 2.0f};</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(info, weightsData);</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span> </div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  <span class="comment">// Add the layers</span></div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = network->AddInputLayer(0);</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* fullyConnected;</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalBias;</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  std::vector<float> biasData{10.0f, 20.0f, 30.0f};</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  <span class="keywordflow">if</span> (desc.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  {</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> bias(info, biasData);</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  optionalBias = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(bias);</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  }</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>  fullyConnected = network->AddFullyConnectedLayer(desc, weights, optionalBias);</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(1);</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span> </div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(fullyConnected->GetInputSlot(0));</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  fullyConnected->GetOutputSlot(0).Connect(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span> </div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>  <span class="comment">// Set TensorInfo</span></div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  fullyConnected->GetOutputSlot(0).SetTensorInfo(outputInfo);</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span> </div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  <span class="keywordflow">return</span> network;</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span> }</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span> </div><div class="line"><a name="l01032"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a245661fc96c9c4a9b898e1d98c8c6962"> 1032</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#a245661fc96c9c4a9b898e1d98c8c6962">ValidateFullyConnectedLayer</a>(<span class="keyword">const</span> <span class="keywordtype">bool</span> biasEnabled)</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span> {</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{3U};</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="namespacearmnn.xhtml#aad4b8cb9a4d882a48bc21510f0d1a938">CreateNetworkWithFullyConnectedLayer</a>(biasEnabled, shape, shape);</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span> </div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span> }</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span> </div><div class="line"><a name="l01040"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#aa145fa23b469fc59c23d9f04f7db4ab7"> 1040</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeFill)</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span> {</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> tensorShape{ 1U };</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> tensorInfo(tensorShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span> </div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span> </div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  <a class="code" href="structarmnn_1_1_fill_descriptor.xhtml">FillDescriptor</a> descriptor;</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>  descriptor.<a class="code" href="structarmnn_1_1_fill_descriptor.xhtml#ab3ebc5cf4a617d43371a4cb7fecdeb32">m_Value</a> = 1;</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span> </div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* fillLayer = network->AddFillLayer(descriptor);</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span> </div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(fillLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  fillLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span> </div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  fillLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span> </div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), tensorShape);</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span> }</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span> </div><div class="line"><a name="l01063"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a881ab05533f917737509402730668e4a"> 1063</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeFullyConnected)</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span> {</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  <a class="code" href="namespacearmnn.xhtml#a245661fc96c9c4a9b898e1d98c8c6962">ValidateFullyConnectedLayer</a>(<span class="keyword">false</span>);</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span> }</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span> </div><div class="line"><a name="l01068"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a69dd8c7608ff0935a247f3aa07f98212"> 1068</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeFullyConnectedBiasEnabled)</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span> {</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>  <a class="code" href="namespacearmnn.xhtml#a245661fc96c9c4a9b898e1d98c8c6962">ValidateFullyConnectedLayer</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span> }</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span> </div><div class="line"><a name="l01073"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a14cfd39cfc30682fa821ade3dd298426"> 1073</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#a14cfd39cfc30682fa821ade3dd298426">TestQuantizeConvolution2d</a>(<span class="keywordtype">bool</span> useBiases)</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span> {</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span> </div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{3U};</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span> </div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>  std::vector<float> weightsData{-1.0f, 1.5f, 2.0f};</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(info, weightsData);</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span> </div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = useBiases;</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span> </div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  <span class="comment">// Add the layers</span></div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = network->AddInputLayer(0);</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* conv2d;</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalBiases;</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>  std::vector<float> biasesData{-1.0f, 1.5f, 2.0f};</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  <span class="keywordflow">if</span> (useBiases)</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  {</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biases(info, biasesData);</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  optionalBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(biases);</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  }</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  conv2d = network->AddConvolution2dLayer(descriptor, weights, optionalBiases);</div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(1);</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span> </div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(conv2d->GetInputSlot(0));</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>  conv2d->GetOutputSlot(0).Connect(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span> </div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>  <span class="comment">// Set TensorInfo</span></div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>  conv2d->GetOutputSlot(0).SetTensorInfo(info);</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span> </div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span> }</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span> </div><div class="line"><a name="l01110"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#aa117e0112fdc02a7a011cfb39dc596ab"> 1110</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeConvolution2d)</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span> {</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  <a class="code" href="namespacearmnn.xhtml#a14cfd39cfc30682fa821ade3dd298426">TestQuantizeConvolution2d</a>(<span class="keyword">false</span>);</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span> }</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span> </div><div class="line"><a name="l01115"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a9827adb2cf787460578999e0484568fa"> 1115</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeConvolution2dWithBiases)</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span> {</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>  <a class="code" href="namespacearmnn.xhtml#a14cfd39cfc30682fa821ade3dd298426">TestQuantizeConvolution2d</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span> }</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span> </div><div class="line"><a name="l01120"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a5abbe8a9ee003c1379a921dbe2745b81"> 1120</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#a5abbe8a9ee003c1379a921dbe2745b81">TestQuantizeDepthwiseConvolution2d</a>(<span class="keywordtype">bool</span> useBiases)</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span> {</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span> </div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{3U};</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span> </div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  std::vector<float> weightsData{-1.0f, 1.5f, 2.0f};</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(info, weightsData);</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span> </div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = useBiases;</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span> </div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  <span class="comment">// Add the layers</span></div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = network->AddInputLayer(0);</div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* depthwiseConv2d;</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalBiases;</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  std::vector<float> biasesData{-1.0f, 1.5f, 2.0f};</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>  <span class="keywordflow">if</span> (useBiases)</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>  {</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biases(info, biasesData);</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  optionalBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(biases);</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>  }</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  depthwiseConv2d = network->AddDepthwiseConvolution2dLayer(descriptor, weights, optionalBiases);</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(1);</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span> </div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(depthwiseConv2d->GetInputSlot(0));</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  depthwiseConv2d->GetOutputSlot(0).Connect(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span> </div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>  <span class="comment">//Set TensorInfo</span></div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  depthwiseConv2d->GetOutputSlot(0).SetTensorInfo(info);</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span> </div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span> }</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span> </div><div class="line"><a name="l01157"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a1db5d836b83fc71602a7993616de5b42"> 1157</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeDepthwiseConvolution2d)</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span> {</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  <a class="code" href="namespacearmnn.xhtml#a5abbe8a9ee003c1379a921dbe2745b81">TestQuantizeDepthwiseConvolution2d</a>(<span class="keyword">false</span>);</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span> }</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span> </div><div class="line"><a name="l01162"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a891abdb9079715cbcf85792e2b450652"> 1162</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeDepthwiseConvolution2dWithBiases)</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span> {</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>  <a class="code" href="namespacearmnn.xhtml#a5abbe8a9ee003c1379a921dbe2745b81">TestQuantizeDepthwiseConvolution2d</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span> }</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span> </div><div class="line"><a name="l01167"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#abd033569519fec65077ea983f6c75a9d"> 1167</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeInstanceNormalization)</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span> {</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{ 1, 4, 4, 1 };</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> tensorInfo(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span> </div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span> </div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* instanceNormLayer = network->AddInstanceNormalizationLayer(<a class="code" href="structarmnn_1_1_instance_normalization_descriptor.xhtml">InstanceNormalizationDescriptor</a>());</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span> </div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(instanceNormLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  instanceNormLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span> </div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  instanceNormLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span> </div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span> }</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span> </div><div class="line"><a name="l01187"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a46d045b35ad6b8c2ffe0c04684f97779"> 1187</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeLogSoftmax)</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span> {</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> tensorShape{ 1U };</div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> tensorInfo(tensorShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span> </div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span> </div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">LogSoftmaxDescriptor</a> descriptor;</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  descriptor.<a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = 1.0f;</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span> </div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* logSoftmaxLayer = network->AddLogSoftmaxLayer(descriptor);</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span> </div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(logSoftmaxLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  logSoftmaxLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span> </div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  logSoftmaxLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span> </div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), tensorShape);</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span> }</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span> </div><div class="line"><a name="l01210"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a9c91b774c3089c55df77cc3a42da72de"> 1210</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="namespacearmnn.xhtml#a9c91b774c3089c55df77cc3a42da72de">CreateNetworkWithSoftmaxLayer</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">SoftmaxDescriptor</a>& descriptor, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& shape)</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span> {</div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span> </div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>  <span class="comment">// Add the layers</span></div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = network->AddInputLayer(0);</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* softmax = network->AddSoftmaxLayer(descriptor);</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(2);</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span> </div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(softmax-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>  softmax-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span> </div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  <span class="comment">// Set TensorInfo</span></div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  softmax-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span> </div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>  <span class="keywordflow">return</span> network;</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span> }</div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span> </div><div class="line"><a name="l01231"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a7e94e9ab356805c498f5fc2fba87e4e6"> 1231</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeSoftmax)</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span> {</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">SoftmaxDescriptor</a> descriptor;</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  descriptor.<a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = 1.0f;</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span> </div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="namespacearmnn.xhtml#a9c91b774c3089c55df77cc3a42da72de">CreateNetworkWithSoftmaxLayer</a>(descriptor, shape);</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span> </div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span> }</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span> </div><div class="line"><a name="l01242"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a4734542212b5811d0511ea6b16f35168"> 1242</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeStandIn)</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span> {</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> tensorShape{ 1U };</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> tensorInfo(tensorShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span> </div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span> </div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>  <a class="code" href="structarmnn_1_1_stand_in_descriptor.xhtml">StandInDescriptor</a> descriptor;</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  descriptor.<a class="code" href="structarmnn_1_1_stand_in_descriptor.xhtml#aed6086070440ceb94129bef06f70173f">m_NumInputs</a> = 1;</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  descriptor.<a class="code" href="structarmnn_1_1_stand_in_descriptor.xhtml#abb8a2d2bb8cc594c26aaa70c820ac5cc">m_NumOutputs</a> = 1;</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span> </div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* standInLayer = network->AddStandInLayer(descriptor);</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span> </div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(standInLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>  standInLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span> </div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span>  standInLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span> </div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  <span class="comment">// test QAsymmU8 quantization</span></div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network.get())->ExportNetwork(),</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>  <a class="code" href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a>);</div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span> </div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  <span class="comment">// test QAsymmS8 quantization</span></div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a> qAsymmS8Options(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>);</div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network.get(), qAsymmS8Options)->ExportNetwork(),</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>  <a class="code" href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a>);</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span> </div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>  <span class="comment">// test QuantisedSymmS16 quantization</span></div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a> qSymmS8Options(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>);</div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network.get(), qSymmS8Options)->ExportNetwork(),</div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>  <a class="code" href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a>);</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span> </div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>  <span class="comment">// test QuantisedSymmS16 quantization</span></div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a> qSymmS16options(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>);</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network.get(), qSymmS16options)->ExportNetwork(),</div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>  <a class="code" href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a>);</div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span> }</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span> </div><div class="line"><a name="l01283"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a120c131df35d78b3a56cb0f07decaf35"> 1283</a></span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <a class="code" href="namespacearmnn.xhtml#a120c131df35d78b3a56cb0f07decaf35">CreateStartOfLeakyReluNetwork</a>(<a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>* network, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span> {</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaacb7667f5ec2f6e8a5943b781ba6c2735">ActivationFunction::LeakyReLu</a>;</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 3.5f;</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -10.0f;</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span> </div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>  <span class="comment">// Add the layers</span></div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = network-><a class="code" href="classarmnn_1_1_i_network.xhtml#aa6c1c42ea44777302e87ce0fad5ac510">AddInputLayer</a>(0);</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activation = network-><a class="code" href="classarmnn_1_1_i_network.xhtml#aa51350bdd4976f3dd5a4e9d00a906b2c">AddActivationLayer</a>(activationDescriptor);</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span> </div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(activation-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span> </div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>  <span class="comment">// Set TensorInfo</span></div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>  activation-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span> </div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>  <span class="keywordflow">return</span> activation;</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span> }</div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span> </div><div class="line"><a name="l01304"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a6fff4b4b1b5d4d37c9cf53d0e31c05dd"> 1304</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#a6fff4b4b1b5d4d37c9cf53d0e31c05dd">CompleteLeakyReluNetwork</a>(<a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>* network,</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activation,</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layerUnderTest,</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span> {</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>  <span class="comment">// Add the output Layer</span></div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network-><a class="code" href="classarmnn_1_1_i_network.xhtml#af5790069aa11fd1c5bb2e17cecb06528">AddOutputLayer</a>(3);</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span> </div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>  activation-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layerUnderTest-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>  layerUnderTest-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span> </div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>  <span class="comment">//Set TensorInfo</span></div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>  layerUnderTest-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span> }</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span> </div><div class="line"><a name="l01320"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#add22da50dd35a100548dde4c57ae89d1"> 1320</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizePermute)</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span> {</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span> </div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span> </div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activation = <a class="code" href="namespacearmnn.xhtml#a120c131df35d78b3a56cb0f07decaf35">CreateStartOfLeakyReluNetwork</a>(network.get(), <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span> </div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>  <span class="comment">// Add the layer under test</span></div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>  <a class="code" href="structarmnn_1_1_permute_descriptor.xhtml">PermuteDescriptor</a> desc;</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* permute = network->AddPermuteLayer(desc);</div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span> </div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>  <a class="code" href="namespacearmnn.xhtml#a6fff4b4b1b5d4d37c9cf53d0e31c05dd">CompleteLeakyReluNetwork</a>(network.get(), activation, permute, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span> </div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span> }</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span> </div><div class="line"><a name="l01338"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a9a6bc66017eb7c132fd6e13ff0dcb540"> 1338</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeSpaceToBatch)</div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span> {</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span> </div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span> </div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activation = <a class="code" href="namespacearmnn.xhtml#a120c131df35d78b3a56cb0f07decaf35">CreateStartOfLeakyReluNetwork</a>(network.get(), <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span> </div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>  <span class="comment">// Add the layer under test</span></div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>  <a class="code" href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml">SpaceToBatchNdDescriptor</a> descriptor;</div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* spaceToBatch = network->AddSpaceToBatchNdLayer(descriptor);</div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span> </div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>  <a class="code" href="namespacearmnn.xhtml#a6fff4b4b1b5d4d37c9cf53d0e31c05dd">CompleteLeakyReluNetwork</a>(network.get(), activation, spaceToBatch, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span> </div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span> }</div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span> </div><div class="line"><a name="l01356"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#aa78ce2bbe65cae8f3d60839467dbfc83"> 1356</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeSpaceToDepth)</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span> {</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span> </div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{ 1u };</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span> </div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activation = <a class="code" href="namespacearmnn.xhtml#a120c131df35d78b3a56cb0f07decaf35">CreateStartOfLeakyReluNetwork</a>(network.get(), <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* spaceToDepth = network->AddSpaceToDepthLayer(<a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml">SpaceToDepthDescriptor</a>());</div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span> </div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>  <a class="code" href="namespacearmnn.xhtml#a6fff4b4b1b5d4d37c9cf53d0e31c05dd">CompleteLeakyReluNetwork</a>(network.get(), activation, spaceToDepth, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span> </div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span> }</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span> </div><div class="line"><a name="l01371"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#aaa86b6903e41d2d2828e00b32f872375"> 1371</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizePooling2d)</div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span> {</div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span>  <span class="keyword">auto</span> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span> </div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span> </div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> desc;</div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaacb7667f5ec2f6e8a5943b781ba6c2735">ActivationFunction::LeakyReLu</a>;</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 3.5f;</div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -10.0f;</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span> </div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span>  <span class="comment">// Add the layers</span></div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = network->AddInputLayer(0);</div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activation = network->AddActivationLayer(activationDescriptor);</div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* pooling2d = network->AddPooling2dLayer(desc);</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(3);</div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span> </div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(activation-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>  activation-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(pooling2d-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>  pooling2d-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span> </div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>  <span class="comment">// Set TensorInfo</span></div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>  activation-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>  pooling2d-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span> </div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span> }</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span> </div><div class="line"><a name="l01403"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a369051e180246c66b20c93de5fecee8c"> 1403</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(<a class="code" href="namespacearmnn.xhtml#a0e2bce68a1f7eff47ead4d9a2804eb91">QuantizeConstant</a>)</div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span> {</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span> </div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>  <span class="comment">// Constant layer data</span></div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  std::vector<float> data = {-2.0f, -1.0f, 0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f};</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U, 1U, 3U, 3U};</div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> tensorInfo(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> constantTensor(tensorInfo, data);</div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span> </div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span>  <span class="comment">// Add the layers</span></div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = network->AddInputLayer(0);</div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* constant = network->AddConstantLayer(constantTensor);</div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* addition = network->AddAdditionLayer();</div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(1);</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span> </div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(addition-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>  constant-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(addition-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>  addition-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span> </div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>  <span class="comment">// Set TensorInfo in the remaining layers</span></div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>  addition-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>  constant-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span> </div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span> }</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span> </div><div class="line"><a name="l01432"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#ae3af95ea62252012cf93a98167afef64"> 1432</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeArgMinMax)</div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span> {</div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span> </div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape{ 1, 1, 1, 5 };</div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape{ 1, 1, 1 };</div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span> </div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span> </div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>  <span class="comment">// Add the input layers</span></div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = network->AddInputLayer(0);</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span> </div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>  <span class="comment">// Add the layer under test</span></div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>  <a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml">ArgMinMaxDescriptor</a> argMinMaxDescriptor;</div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span>  argMinMaxDescriptor.<a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml#ab1ae6f520bb1a4da191a0ae907477f23">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">ArgMinMaxFunction::Max</a>;</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* argMinMaxLayer = network->AddArgMinMaxLayer(argMinMaxDescriptor);</div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span> </div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>  <span class="comment">// Add the output layers</span></div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(1);</div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span> </div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(argMinMaxLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span>  argMinMaxLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span> </div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>  <span class="comment">// Set tensor info</span></div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>  argMinMaxLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span> </div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), inputShape, outputShape);</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span> }</div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span> </div><div class="line"><a name="l01464"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#ab83f837cdd5bfcff537dae72a96d6fc8"> 1464</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeComparison)</div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span> {</div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> tensorShape{ 1u };</div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> tensorInfo(tensorShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span> </div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>  <a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml">ComparisonDescriptor</a> descriptor(<a class="code" href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58caa4cbdbb6070a5abb35fc95ecf1e22c14">ComparisonOperation::LessOrEqual</a>);</div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span> </div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* comparisonLayer = network->AddComparisonLayer(descriptor);</div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span> </div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>  inputLayer0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(comparisonLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>  inputLayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(comparisonLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>  comparisonLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span> </div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</span>  inputLayer0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span>  inputLayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>  comparisonLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span> </div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), tensorShape);</div><div class="line"><a name="l01486"></a><span class="lineno"> 1486</span> }</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span> </div><div class="line"><a name="l01488"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#add47ebcd4a59304a25c71996aea2b38b"> 1488</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeConcat)</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span> {</div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>  <span class="keyword">class </span>TestConcatQuantization : <span class="keyword">public</span> TestQuantization</div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>  {</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span>  TestConcatQuantization(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape)</div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span>  : TestQuantization(inputShape, outputShape) {}</div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span> </div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>  TestConcatQuantization(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a>& options,</div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape,</div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape)</div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span>  : TestQuantization(options, inputShape, outputShape) {}</div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span> </div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>  <span class="keywordtype">void</span> ExecuteStrategy(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l01504"></a><span class="lineno"> 1504</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span>)<span class="keyword"> override</span></div><div class="line"><a name="l01506"></a><span class="lineno"> 1506</span> <span class="keyword"> </span>{</div><div class="line"><a name="l01507"></a><span class="lineno"> 1507</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(name, constants, <span class="keywordtype">id</span>, descriptor);</div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span> </div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>  <span class="keywordflow">switch</span> (layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">GetType</a>())</div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>  {</div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a> :</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a> :</div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae20f0f2826a6549809f050b86274567f">armnn::LayerType::Concat</a> :</div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</span>  {</div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span>  TestQuantizationParams(</div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>  outputInfo, {60.8f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 65},</div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span>  {60.8f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, -63},</div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span>  {45.3f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span>  {45.3f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0});</div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span> </div><div class="line"><a name="l01524"></a><span class="lineno"> 1524</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo0 = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">GetConnection</a>()-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo1 = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">GetConnection</a>()-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo2 = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">GetConnection</a>()-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span> </div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>  TestDifferentQuantizationScale(inputInfo0, inputInfo1);</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>  TestDifferentQuantizationScale(inputInfo0, inputInfo2);</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span>  TestDifferentQuantizationScale(inputInfo1, inputInfo2);</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>  TestDifferentQuantizationScale(inputInfo0, outputInfo);</div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01533"></a><span class="lineno"> 1533</span>  }</div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span>  {}</div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>  }</div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span>  }</div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>  };</div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span> </div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span> </div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = network->AddInputLayer(0);</div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = network->AddInputLayer(1);</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input2 = network->AddInputLayer(2);</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span> </div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> descriptor(3, 1);</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* concatLayer = network->AddConcatLayer(descriptor);</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span> </div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output0 = network->AddOutputLayer(3);</div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</span> </div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>  input1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span>  input2-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>  concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span> </div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>  <span class="comment">// Set TensorInfo</span></div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span> </div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>  input1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>  input2-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>  concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span> </div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a> qSymmS8Options(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>);</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a> qSymmS16options(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>);</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span>  <a class="code" href="namespacearmnn.xhtml#a41119e261eec9343888d2ceab1e4999a">INetworkQuantizerPtr</a> quantizerPtrQAsymmU8 = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network.get());</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>  <a class="code" href="namespacearmnn.xhtml#a41119e261eec9343888d2ceab1e4999a">INetworkQuantizerPtr</a> quantizerPtrQSymmS8 = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network.get(), qSymmS8Options);</div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>  <a class="code" href="namespacearmnn.xhtml#a41119e261eec9343888d2ceab1e4999a">INetworkQuantizerPtr</a> quantizerPtrQSymmS16 = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network.get(), qSymmS16options);</div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span>  <span class="comment">// Override the input ranges</span></div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span>  <span class="keywordtype">float</span> min = -15.5f;</div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>  <span class="keywordtype">float</span> max = 45.3f;</div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span> </div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span>  quantizerPtrQAsymmU8->OverrideInputRange(0, (min + 2.1f), (max - 3.2f));</div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span>  quantizerPtrQAsymmU8->OverrideInputRange(1, (min + 6.7f), max);</div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>  quantizerPtrQAsymmU8->OverrideInputRange(2, min, (max - 7.8f));</div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span> </div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span>  quantizerPtrQSymmS8->OverrideInputRange(0, (min + 2.1f), (max - 3.2f));</div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span>  quantizerPtrQSymmS8->OverrideInputRange(1, (min + 6.7f), max);</div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span>  quantizerPtrQSymmS8->OverrideInputRange(2, min, (max - 7.8f));</div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</span> </div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span>  quantizerPtrQSymmS16->OverrideInputRange(0, (min + 2.1f), (max - 3.2f));</div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span>  quantizerPtrQSymmS16->OverrideInputRange(1, (min + 6.7f), max);</div><div class="line"><a name="l01585"></a><span class="lineno"> 1585</span>  quantizerPtrQSymmS16->OverrideInputRange(2, min, (max - 7.8f));</div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span> </div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantizedNetworkQAsymmU8 = quantizerPtrQAsymmU8->ExportNetwork();</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>  TestConcatQuantization validatorQAsymmU8(shape, shape);</div><div class="line"><a name="l01589"></a><span class="lineno"> 1589</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(quantizedNetworkQAsymmU8.get(), validatorQAsymmU8);</div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span> </div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantizedNetworkQSymmS8 = quantizerPtrQSymmS8->ExportNetwork();</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>  TestConcatQuantization validatorQSymmS8(qSymmS8Options, shape, shape);</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(quantizedNetworkQSymmS8.get(), validatorQSymmS8);</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span> </div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantizedNetworkQSymmS16 = quantizerPtrQSymmS16->ExportNetwork();</div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span>  TestConcatQuantization validatorQSymmS16(qSymmS16options, shape, shape);</div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(quantizedNetworkQSymmS16.get(), validatorQSymmS16);</div><div class="line"><a name="l01598"></a><span class="lineno"> 1598</span> }</div><div class="line"><a name="l01599"></a><span class="lineno"> 1599</span> </div><div class="line"><a name="l01600"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a9258afcd4c6d8443c9130d8c9bf26442"> 1600</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeReshape)</div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span> {</div><div class="line"><a name="l01602"></a><span class="lineno"> 1602</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</span> </div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span> </div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activation = <a class="code" href="namespacearmnn.xhtml#a120c131df35d78b3a56cb0f07decaf35">CreateStartOfLeakyReluNetwork</a>(network.get(), <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span> </div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span>  <span class="comment">// Add the layer under test</span></div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">ReshapeDescriptor</a> descriptor({1, 2, 3, 4});</div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* reshape = network->AddReshapeLayer(descriptor);</div><div class="line"><a name="l01612"></a><span class="lineno"> 1612</span> </div><div class="line"><a name="l01613"></a><span class="lineno"> 1613</span>  <a class="code" href="namespacearmnn.xhtml#a6fff4b4b1b5d4d37c9cf53d0e31c05dd">CompleteLeakyReluNetwork</a>(network.get(), activation, reshape, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span> </div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span> }</div><div class="line"><a name="l01617"></a><span class="lineno"> 1617</span> </div><div class="line"><a name="l01618"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a23a4f3c387a2a3a035e97764e34277c6"> 1618</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeSplitter)</div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span> {</div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01621"></a><span class="lineno"> 1621</span> </div><div class="line"><a name="l01622"></a><span class="lineno"> 1622</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{3U};</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span> </div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activation = <a class="code" href="namespacearmnn.xhtml#a120c131df35d78b3a56cb0f07decaf35">CreateStartOfLeakyReluNetwork</a>(network.get(), <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span> </div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>  <span class="comment">// Add the layer under test</span></div><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">ViewsDescriptor</a> splitterDesc(2,4);</div><div class="line"><a name="l01629"></a><span class="lineno"> 1629</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* splitter = network->AddSplitterLayer(splitterDesc);</div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span>  <a class="code" href="namespacearmnn.xhtml#a6fff4b4b1b5d4d37c9cf53d0e31c05dd">CompleteLeakyReluNetwork</a>(network.get(), activation, splitter, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span> </div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</span> }</div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span> </div><div class="line"><a name="l01635"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a102f37a09de1b0d4d78740a3c12902bf"> 1635</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeResize)</div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span> {</div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span> </div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</span> </div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activation = <a class="code" href="namespacearmnn.xhtml#a120c131df35d78b3a56cb0f07decaf35">CreateStartOfLeakyReluNetwork</a>(network.get(), <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span> </div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span>  <span class="comment">// Add the layer under test</span></div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span>  <a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">ResizeDescriptor</a> descriptor;</div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span>  descriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">m_TargetHeight</a> = 3;</div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>  descriptor.m_TargetWidth = 3;</div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* resizeLayer = network->AddResizeLayer(descriptor);</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span> </div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>  <a class="code" href="namespacearmnn.xhtml#a6fff4b4b1b5d4d37c9cf53d0e31c05dd">CompleteLeakyReluNetwork</a>(network.get(), activation, resizeLayer, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span> </div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span> }</div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span> </div><div class="line"><a name="l01655"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a5f9c6094ae666c8e14907307d0481fac"> 1655</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeStridedSlice)</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span> {</div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span> </div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{3U};</div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span> </div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activation = <a class="code" href="namespacearmnn.xhtml#a120c131df35d78b3a56cb0f07decaf35">CreateStartOfLeakyReluNetwork</a>(network.get(), <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</span> </div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span>  <span class="comment">// Add the layer under test</span></div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>  <a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml">StridedSliceDescriptor</a> stridedSliceDesc;</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* stridedSlice = network->AddStridedSliceLayer(stridedSliceDesc);</div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span> </div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span>  <a class="code" href="namespacearmnn.xhtml#a6fff4b4b1b5d4d37c9cf53d0e31c05dd">CompleteLeakyReluNetwork</a>(network.get(), activation, stridedSlice, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span> </div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span> }</div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span> </div><div class="line"><a name="l01673"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#aec7cf8e3927ee7d24f8b19d206ce3e84"> 1673</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeBatchToSpace)</div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span> {</div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span> </div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U};</div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span> </div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activation = <a class="code" href="namespacearmnn.xhtml#a120c131df35d78b3a56cb0f07decaf35">CreateStartOfLeakyReluNetwork</a>(network.get(), <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span> </div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span>  <span class="comment">// Add the layer under test</span></div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>  <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">BatchToSpaceNdDescriptor</a> descriptor;</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* batchToSpace = network->AddBatchToSpaceNdLayer(descriptor);</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span> </div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>  <a class="code" href="namespacearmnn.xhtml#a6fff4b4b1b5d4d37c9cf53d0e31c05dd">CompleteLeakyReluNetwork</a>(network.get(), activation, batchToSpace, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span> </div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l01689"></a><span class="lineno"> 1689</span> }</div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span> </div><div class="line"><a name="l01691"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a733ef16d4eaaf8cce338320fa042f526"> 1691</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizePrelu)</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span> {</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span>  <span class="keyword">class </span>TestPreluQuantization : <span class="keyword">public</span> TestQuantization</div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span>  {</div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>  TestPreluQuantization(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape,</div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& alphaShape,</div><div class="line"><a name="l01698"></a><span class="lineno"> 1698</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape)</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>  : TestQuantization(inputShape, outputShape)</div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>  , m_AlphaShape(alphaShape)</div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span>  {}</div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span> </div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span>  TestPreluQuantization(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a>& options,</div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape,</div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& alphaShape,</div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape)</div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span>  : TestQuantization(options, inputShape, outputShape)</div><div class="line"><a name="l01708"></a><span class="lineno"> 1708</span>  , m_AlphaShape(alphaShape)</div><div class="line"><a name="l01709"></a><span class="lineno"> 1709</span>  {}</div><div class="line"><a name="l01710"></a><span class="lineno"> 1710</span> </div><div class="line"><a name="l01711"></a><span class="lineno"> 1711</span>  <span class="keywordtype">void</span> ExecuteStrategy(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span>)<span class="keyword"> override</span></div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span> <span class="keyword"> </span>{</div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(name, constants, <span class="keywordtype">id</span>, descriptor);</div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span> </div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>  <span class="keywordflow">switch</span> (layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">GetType</a>())</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>  {</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a> :</div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>  {</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span> </div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>  <span class="keywordflow">switch</span> (<span class="keywordtype">id</span>)</div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>  {</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>  <span class="keywordflow">case</span> 0: <span class="comment">// Input</span></div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>  BOOST_TEST(m_InputShape == info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>  <span class="keywordflow">case</span> 1: <span class="comment">// Alpha</span></div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span>  BOOST_TEST(m_AlphaShape == info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"Invalid layer binding id for PReLU layer"</span>);</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span>  }</div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span> </div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span>  <span class="comment">// Based off current default [-15.0f, 15.0f]</span></div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span>  TestQuantizationParams(info,</div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span>  { 30.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128 }, <span class="comment">// QASymmU8</span></div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span>  { 30.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0}, <span class="comment">// QASymmS8</span></div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>  { 15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0}, <span class="comment">// QSymmS8</span></div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>  { 15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0 }); <span class="comment">// QSymmS16</span></div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>  }</div><div class="line"><a name="l01745"></a><span class="lineno"> 1745</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a> :</div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span>  {</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">GetConnection</a>()-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span>  BOOST_TEST(m_OutputShape == info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>  }</div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a0c5967f09e0669c840ebb1ed0da85e32">armnn::LayerType::Prelu</a> :</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span>  {</div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>  TestQuantizationParams(info,</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>  { 30.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128 }, <span class="comment">// QASymmU8</span></div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span>  { 30.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0}, <span class="comment">// QAsymmS8</span></div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span>  { 15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0}, <span class="comment">// QSymmS8</span></div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span>  { 15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0 }); <span class="comment">// QSymmS16</span></div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span>  }</div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>  {}</div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>  }</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>  }</div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span> </div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> m_AlphaShape;</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>  };</div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span> </div><div class="line"><a name="l01770"></a><span class="lineno"> 1770</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span> </div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape{ 4, 1, 2 };</div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> alphaShape{ 5, 4, 3, 1 };</div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape{ 5, 4, 3, 2 };</div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> alphaInfo(alphaShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span> </div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>  <span class="comment">// Add the input layers</span></div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = network->AddInputLayer(0);</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* alpha = network->AddInputLayer(1);</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span> </div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>  <span class="comment">// Add the layer under test</span></div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* prelu = network->AddPreluLayer(<span class="stringliteral">"prelu"</span>);</div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span> </div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span>  <span class="comment">// Add the output layers</span></div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(0);</div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span> </div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span>  <span class="comment">// Establish connections</span></div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(prelu-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span>  alpha-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(prelu-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>  prelu-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span> </div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>  <span class="comment">// Set tensor info</span></div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span>  alpha-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(alphaInfo);</div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span>  prelu-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span> </div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantizedNetworkQAsymmU8 = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network.get())->ExportNetwork();</div><div class="line"><a name="l01800"></a><span class="lineno"> 1800</span>  TestPreluQuantization validatorQAsymmU8(inputShape, alphaShape, outputShape);</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(quantizedNetworkQAsymmU8.get(), validatorQAsymmU8);</div><div class="line"><a name="l01802"></a><span class="lineno"> 1802</span> </div><div class="line"><a name="l01803"></a><span class="lineno"> 1803</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a> qAsymmS8Options(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>);</div><div class="line"><a name="l01804"></a><span class="lineno"> 1804</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantizedNetworkQAsymmS8 = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network.get(), qAsymmS8Options)->ExportNetwork();</div><div class="line"><a name="l01805"></a><span class="lineno"> 1805</span>  TestPreluQuantization validatorQAsymmS8(qAsymmS8Options, inputShape, alphaShape, outputShape);</div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(quantizedNetworkQAsymmS8.get(), validatorQAsymmS8);</div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span> </div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a> qSymmS8Options(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>);</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantizedNetworkQSymmS8 = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network.get(), qSymmS8Options)->ExportNetwork();</div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span>  TestPreluQuantization validatorQSymmS8(qSymmS8Options, inputShape, alphaShape, outputShape);</div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(quantizedNetworkQSymmS8.get(), validatorQSymmS8);</div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span> </div><div class="line"><a name="l01813"></a><span class="lineno"> 1813</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a> qSymmS16options(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>);</div><div class="line"><a name="l01814"></a><span class="lineno"> 1814</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantizedNetworkQSymmS16 = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network.get(), qSymmS16options)->ExportNetwork();</div><div class="line"><a name="l01815"></a><span class="lineno"> 1815</span>  TestPreluQuantization validatorQSymmS16(qSymmS16options, inputShape, alphaShape, outputShape);</div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(quantizedNetworkQSymmS16.get(), validatorQSymmS16);</div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span> }</div><div class="line"><a name="l01818"></a><span class="lineno"> 1818</span> </div><div class="line"><a name="l01819"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#afa7a0a639e2772ff2ced67d77be810c0"> 1819</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#afa7a0a639e2772ff2ced67d77be810c0">TestQuantizeTransposeConvolution2d</a>(<span class="keywordtype">bool</span> useBiases)</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span> {</div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span> </div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{ 3 };</div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span> </div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span>  std::initializer_list<float> floatData{ -1.0f, 1.5f, 2.0f };</div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span>  std::vector<float> weightsData(floatData);</div><div class="line"><a name="l01828"></a><span class="lineno"> 1828</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(info, weightsData);</div><div class="line"><a name="l01829"></a><span class="lineno"> 1829</span> </div><div class="line"><a name="l01830"></a><span class="lineno"> 1830</span>  <a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">TransposeConvolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l01831"></a><span class="lineno"> 1831</span>  descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = useBiases;</div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span> </div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span>  <span class="comment">// construct network</span></div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = network->AddInputLayer(0);</div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalBiases;</div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>  std::vector<float> biasesData(floatData);</div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span>  <span class="keywordflow">if</span> (useBiases)</div><div class="line"><a name="l01838"></a><span class="lineno"> 1838</span>  {</div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biases(info, biasesData);</div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>  optionalBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(biases);</div><div class="line"><a name="l01841"></a><span class="lineno"> 1841</span>  }</div><div class="line"><a name="l01842"></a><span class="lineno"> 1842</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* transposeConv2d = network->AddTransposeConvolution2dLayer(descriptor, weights, optionalBiases);</div><div class="line"><a name="l01843"></a><span class="lineno"> 1843</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(1);</div><div class="line"><a name="l01844"></a><span class="lineno"> 1844</span> </div><div class="line"><a name="l01845"></a><span class="lineno"> 1845</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(transposeConv2d->GetInputSlot(0));</div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span>  transposeConv2d->GetOutputSlot(0).Connect(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span> </div><div class="line"><a name="l01848"></a><span class="lineno"> 1848</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01849"></a><span class="lineno"> 1849</span>  transposeConv2d->GetOutputSlot(0).SetTensorInfo(info);</div><div class="line"><a name="l01850"></a><span class="lineno"> 1850</span> </div><div class="line"><a name="l01851"></a><span class="lineno"> 1851</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l01852"></a><span class="lineno"> 1852</span> }</div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span> </div><div class="line"><a name="l01854"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a5e66fe270ca921faeecd26735192d08b"> 1854</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeTransposeConvolution2d)</div><div class="line"><a name="l01855"></a><span class="lineno"> 1855</span> {</div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>  <a class="code" href="namespacearmnn.xhtml#afa7a0a639e2772ff2ced67d77be810c0">TestQuantizeTransposeConvolution2d</a>(<span class="keyword">false</span>);</div><div class="line"><a name="l01857"></a><span class="lineno"> 1857</span> }</div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</span> </div><div class="line"><a name="l01859"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#aec82007c45313f59d24b304e35b3db6c"> 1859</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeTransposeConvolution2dWithBiases)</div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span> {</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span>  <a class="code" href="namespacearmnn.xhtml#afa7a0a639e2772ff2ced67d77be810c0">TestQuantizeTransposeConvolution2d</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span> }</div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span> </div><div class="line"><a name="l01864"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a77cba79eef903eb3d758b4edbcc626ef"> 1864</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeStack)</div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span> {</div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span>  <span class="keyword">class </span>TestStackQuantization : <span class="keyword">public</span> TestQuantization</div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>  {</div><div class="line"><a name="l01868"></a><span class="lineno"> 1868</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l01869"></a><span class="lineno"> 1869</span>  TestStackQuantization(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape,</div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape)</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span>  : TestQuantization(inputShape, outputShape) {}</div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span> </div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span>  TestStackQuantization(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a>& options,</div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape,</div><div class="line"><a name="l01875"></a><span class="lineno"> 1875</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape)</div><div class="line"><a name="l01876"></a><span class="lineno"> 1876</span>  : TestQuantization(options, inputShape, outputShape) {}</div><div class="line"><a name="l01877"></a><span class="lineno"> 1877</span> </div><div class="line"><a name="l01878"></a><span class="lineno"> 1878</span>  <span class="keywordtype">void</span> ExecuteStrategy(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l01881"></a><span class="lineno"> 1881</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l01882"></a><span class="lineno"> 1882</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span>)<span class="keyword"> override</span></div><div class="line"><a name="l01883"></a><span class="lineno"> 1883</span> <span class="keyword"> </span>{</div><div class="line"><a name="l01884"></a><span class="lineno"> 1884</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(name, constants, <span class="keywordtype">id</span>, descriptor);</div><div class="line"><a name="l01885"></a><span class="lineno"> 1885</span> </div><div class="line"><a name="l01886"></a><span class="lineno"> 1886</span>  <span class="keywordflow">switch</span> (layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">GetType</a>())</div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span>  {</div><div class="line"><a name="l01888"></a><span class="lineno"> 1888</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a> :</div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>  {</div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span>  }</div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a> :</div><div class="line"><a name="l01893"></a><span class="lineno"> 1893</span>  {</div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>  }</div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2187e1021a911b3807cc1bea2eb1a9ca">armnn::LayerType::Stack</a> :</div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span>  {</div><div class="line"><a name="l01898"></a><span class="lineno"> 1898</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span> </div><div class="line"><a name="l01900"></a><span class="lineno"> 1900</span>  TestQuantizationParams(outputInfo,</div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>  { 30.0f / <a class="code" href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">g_AsymmU8QuantizationBase</a>, 128 },</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span>  { 30.0f / <a class="code" href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">g_AsymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span>  { 15.0f / <a class="code" href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">g_SymmS8QuantizationBase</a>, 0},</div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>  { 15.0f / <a class="code" href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">g_SymmS16QuantizationBase</a>, 0 });</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>  }</div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>  {}</div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>  }</div><div class="line"><a name="l01910"></a><span class="lineno"> 1910</span>  }</div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>  };</div><div class="line"><a name="l01912"></a><span class="lineno"> 1912</span> </div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span> </div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = network->AddInputLayer(0);</div><div class="line"><a name="l01916"></a><span class="lineno"> 1916</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = network->AddInputLayer(1);</div><div class="line"><a name="l01917"></a><span class="lineno"> 1917</span> </div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape{ 3, 4, 5 };</div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape{ 3, 4, 2, 5 };</div><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span> </div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span>  <a class="code" href="structarmnn_1_1_stack_descriptor.xhtml">StackDescriptor</a> descriptor(2, 2, inputShape);</div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* stackLayer = network->AddStackLayer(descriptor);</div><div class="line"><a name="l01923"></a><span class="lineno"> 1923</span> </div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(0);</div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span> </div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(stackLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span>  input1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(stackLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span>  stackLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span> </div><div class="line"><a name="l01930"></a><span class="lineno"> 1930</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantizedNetworkQAsymmU8 = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network.get())->ExportNetwork();</div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>  TestStackQuantization validatorQAsymmU8(inputShape, outputShape);</div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(quantizedNetworkQAsymmU8.get(), validatorQAsymmU8);</div><div class="line"><a name="l01933"></a><span class="lineno"> 1933</span> </div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a> qAsymmS8Options(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>);</div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantizedNetworkQAsymmS8 = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network.get(), qAsymmS8Options)->ExportNetwork();</div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span>  TestStackQuantization validatorQAsymmS8(qAsymmS8Options, inputShape, inputShape);</div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(quantizedNetworkQAsymmS8.get(), validatorQAsymmS8);</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span> </div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a> qSymmS8Options(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>);</div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantizedNetworkQSymmS8 = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network.get(), qSymmS8Options)->ExportNetwork();</div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>  TestStackQuantization validatorQSymmS8(qSymmS8Options, inputShape, inputShape);</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(quantizedNetworkQSymmS8.get(), validatorQSymmS8);</div><div class="line"><a name="l01943"></a><span class="lineno"> 1943</span> </div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a> qSymmS16options(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>);</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantizedNetworkQSymmS16 = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network.get(), qSymmS16options)->ExportNetwork();</div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>  TestStackQuantization validatorQSymmS16(qSymmS16options, inputShape, outputShape);</div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(quantizedNetworkQSymmS16.get(), validatorQSymmS16);</div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span> }</div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span> </div><div class="line"><a name="l01950"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a46f313720b601ca97a9c2a5158814bff"> 1950</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeSlice)</div><div class="line"><a name="l01951"></a><span class="lineno"> 1951</span> {</div><div class="line"><a name="l01952"></a><span class="lineno"> 1952</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{ 3 };</div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span> </div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span> </div><div class="line"><a name="l01957"></a><span class="lineno"> 1957</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* sliceLayer = network->AddSliceLayer(<a class="code" href="structarmnn_1_1_slice_descriptor.xhtml">SliceDescriptor</a>());</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01960"></a><span class="lineno"> 1960</span> </div><div class="line"><a name="l01961"></a><span class="lineno"> 1961</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(sliceLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>  sliceLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span> </div><div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>  sliceLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span> </div><div class="line"><a name="l01967"></a><span class="lineno"> 1967</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>(network.get(), shape);</div><div class="line"><a name="l01968"></a><span class="lineno"> 1968</span> }</div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span> </div><div class="line"><a name="l01970"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a52cbff9d344ba4a1fe01d4da2c1f7ba2"> 1970</a></span> std::vector<uint8_t> <a class="code" href="namespacearmnn.xhtml#a52cbff9d344ba4a1fe01d4da2c1f7ba2">SetupQuantize</a>(<span class="keywordtype">float</span> value)</div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span> {</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({ 1, 2, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>  inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(1.0f);</div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</span>  inputInfo.SetQuantizationOffset(1);</div><div class="line"><a name="l01975"></a><span class="lineno"> 1975</span>  std::vector<float> input({ value, 0.0f, 0.0f, 1.0f });</div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>  <span class="keyword">const</span> std::vector<float> &inputRef = input;</div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span> </div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>  <span class="keyword">auto</span> output = armnnUtils::QuantizedVector<uint8_t>(inputRef,</div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>  inputInfo.GetQuantizationScale(),</div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>  inputInfo.GetQuantizationOffset());</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span> </div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>  <span class="keywordflow">return</span> output;</div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span> }</div><div class="line"><a name="l01984"></a><span class="lineno"> 1984</span> </div><div class="line"><a name="l01985"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a728153b62fa66e6ed1243e09144bfe8c"> 1985</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeInf)</div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span> {</div><div class="line"><a name="l01987"></a><span class="lineno"> 1987</span>  BOOST_CHECK_EQUAL(<a class="code" href="namespacearmnn.xhtml#a52cbff9d344ba4a1fe01d4da2c1f7ba2">SetupQuantize</a>(std::numeric_limits<float>::infinity())[0], 255);</div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span> }</div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span> </div><div class="line"><a name="l01990"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a898305dc4cdb78a5fbed481250f6cd35"> 1990</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QuantizeNegativeInf)</div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span> {</div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>  BOOST_CHECK_EQUAL(<a class="code" href="namespacearmnn.xhtml#a52cbff9d344ba4a1fe01d4da2c1f7ba2">SetupQuantize</a>(-1 * std::numeric_limits<float>::infinity())[0], 0);</div><div class="line"><a name="l01993"></a><span class="lineno"> 1993</span> }</div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span> </div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span> <span class="keyword">class </span>TestPreserveType : <span class="keyword">public</span> TestQuantization</div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span> {</div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span> <span class="keyword">public</span>:</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>  TestPreserveType(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a>& options,</div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>& dataType,</div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape,</div><div class="line"><a name="l02001"></a><span class="lineno"> 2001</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape)</div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>  : TestQuantization(options, inputShape, outputShape)</div><div class="line"><a name="l02003"></a><span class="lineno"> 2003</span>  , m_DataType(dataType)</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>  , m_VisitedQuantizeLayer(<span class="keyword">false</span>)</div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span>  , m_VisitedDequantizeLayer(<span class="keyword">false</span>) {}</div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span> </div><div class="line"><a name="l02007"></a><span class="lineno"> 2007</span>  <span class="keywordtype">void</span> ExecuteStrategy(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span>)<span class="keyword"> override</span></div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span> <span class="keyword"> </span>{</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(name, constants, <span class="keywordtype">id</span>, descriptor);</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span> </div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>  <span class="keywordflow">switch</span> (layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">GetType</a>())</div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>  {</div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a> :</div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>  {</div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span>  BOOST_TEST(<a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()) == <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(m_DataType));</div><div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>  BOOST_TEST(m_InputShape == info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02023"></a><span class="lineno"> 2023</span>  }</div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a> :</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>  {</div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">GetConnection</a>()-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>  BOOST_TEST(<a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()) == <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(m_DataType));</div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>  BOOST_TEST(m_OutputShape == info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>  }</div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aca39930e22f40d10155a57dba32240bb">armnn::LayerType::Quantize</a> :</div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>  {</div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span>  m_VisitedQuantizeLayer = <span class="keyword">true</span>;</div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>  }</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a9bc35e069257a508e14ed82965a8661d">armnn::LayerType::Dequantize</a> :</div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>  {</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span>  m_VisitedDequantizeLayer = <span class="keyword">true</span>;</div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>  }</div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l02042"></a><span class="lineno"> 2042</span>  {}</div><div class="line"><a name="l02043"></a><span class="lineno"> 2043</span>  }</div><div class="line"><a name="l02044"></a><span class="lineno"> 2044</span>  }</div><div class="line"><a name="l02045"></a><span class="lineno"> 2045</span> </div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>  <span class="keywordtype">void</span> CheckQuantizeDequantizeLayerVisited(<span class="keywordtype">bool</span> expected)</div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span>  {</div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span>  <span class="keywordflow">if</span> (expected)</div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>  {</div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span>  BOOST_CHECK(m_VisitedQuantizeLayer);</div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</span>  BOOST_CHECK(m_VisitedDequantizeLayer);</div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span>  }</div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l02054"></a><span class="lineno"> 2054</span>  {</div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span>  BOOST_CHECK(!m_VisitedQuantizeLayer);</div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span>  BOOST_CHECK(!m_VisitedDequantizeLayer);</div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span>  }</div><div class="line"><a name="l02058"></a><span class="lineno"> 2058</span>  }</div><div class="line"><a name="l02059"></a><span class="lineno"> 2059</span> <span class="keyword">private</span>:</div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> m_DataType;</div><div class="line"><a name="l02061"></a><span class="lineno"> 2061</span>  <span class="keywordtype">bool</span> m_VisitedQuantizeLayer;</div><div class="line"><a name="l02062"></a><span class="lineno"> 2062</span>  <span class="keywordtype">bool</span> m_VisitedDequantizeLayer;</div><div class="line"><a name="l02063"></a><span class="lineno"> 2063</span> };</div><div class="line"><a name="l02064"></a><span class="lineno"> 2064</span> </div><div class="line"><a name="l02065"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#abe34cf42d7c8515ecd15d11f4aeb399c"> 2065</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#abe34cf42d7c8515ecd15d11f4aeb399c">PreserveTypeTestImpl</a>(<span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>& dataType)</div><div class="line"><a name="l02066"></a><span class="lineno"> 2066</span> {</div><div class="line"><a name="l02067"></a><span class="lineno"> 2067</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span> </div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span>  <span class="comment">// Add the layers</span></div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = network->AddInputLayer(0);</div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = network->AddInputLayer(1);</div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* addition = network->AddAdditionLayer();</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = network->AddOutputLayer(2);</div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span> </div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(addition-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02076"></a><span class="lineno"> 2076</span>  input1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(addition-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02077"></a><span class="lineno"> 2077</span>  addition-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span> </div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape{1U, 2U, 3U};</div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(shape, dataType);</div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>  input0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span>  input1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span>  addition-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02084"></a><span class="lineno"> 2084</span> </div><div class="line"><a name="l02085"></a><span class="lineno"> 2085</span>  <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a> options = dataType == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a> ?</div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span>  <a class="code" href="structarmnn_1_1_quantizer_options.xhtml">QuantizerOptions</a>(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>, <span class="keyword">true</span>) : QuantizerOptions(dataType, <span class="keyword">true</span>);</div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span> </div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantizedNetworkQAsymmU8 = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">INetworkQuantizer::Create</a>(network.get(), options)->ExportNetwork();</div><div class="line"><a name="l02089"></a><span class="lineno"> 2089</span>  TestPreserveType validatorQAsymmU8(options, dataType, shape, shape);</div><div class="line"><a name="l02090"></a><span class="lineno"> 2090</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(quantizedNetworkQAsymmU8.get(), validatorQAsymmU8);</div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span>  validatorQAsymmU8.CheckQuantizeDequantizeLayerVisited(</div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span>  dataType == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a> || dataType == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>);</div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span> }</div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span> </div><div class="line"><a name="l02095"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a94eb3bdf0e1c8c748c2e29dce048ace4"> 2095</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(PreserveTypeFloat32)</div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</span> {</div><div class="line"><a name="l02097"></a><span class="lineno"> 2097</span>  <a class="code" href="namespacearmnn.xhtml#abe34cf42d7c8515ecd15d11f4aeb399c">PreserveTypeTestImpl</a>(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l02098"></a><span class="lineno"> 2098</span> }</div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span> </div><div class="line"><a name="l02100"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#ab242670b85e047e79bb297cdb192cc93"> 2100</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(PreserveTypeQAsymmU8)</div><div class="line"><a name="l02101"></a><span class="lineno"> 2101</span> {</div><div class="line"><a name="l02102"></a><span class="lineno"> 2102</span>  <a class="code" href="namespacearmnn.xhtml#abe34cf42d7c8515ecd15d11f4aeb399c">PreserveTypeTestImpl</a>(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l02103"></a><span class="lineno"> 2103</span> }</div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span> </div><div class="line"><a name="l02105"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a061891029598224370aae4cd18b78406"> 2105</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(PreserveTypeQsymm8)</div><div class="line"><a name="l02106"></a><span class="lineno"> 2106</span> {</div><div class="line"><a name="l02107"></a><span class="lineno"> 2107</span>  <a class="code" href="namespacearmnn.xhtml#abe34cf42d7c8515ecd15d11f4aeb399c">PreserveTypeTestImpl</a>(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>);</div><div class="line"><a name="l02108"></a><span class="lineno"> 2108</span> }</div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span> </div><div class="line"><a name="l02110"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a4d4386cbb19dbc551e423992ecdd0d61"> 2110</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(PreserveTypeQsymm16)</div><div class="line"><a name="l02111"></a><span class="lineno"> 2111</span> {</div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span>  <a class="code" href="namespacearmnn.xhtml#abe34cf42d7c8515ecd15d11f4aeb399c">PreserveTypeTestImpl</a>(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>);</div><div class="line"><a name="l02113"></a><span class="lineno"> 2113</span> }</div><div class="line"><a name="l02114"></a><span class="lineno"> 2114</span> </div><div class="line"><a name="l02115"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a8c09fbb75d2c2dea48926a540fc5cce9"> 2115</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(TestConnectionPreservationAfterDynamicQuant)</div><div class="line"><a name="l02116"></a><span class="lineno"> 2116</span> {</div><div class="line"><a name="l02117"></a><span class="lineno"> 2117</span>  <span class="keyword">class </span>TestConnectionPreservation : <span class="keyword">public</span> <a class="code" href="classarmnn_1_1_i_strategy.xhtml">IStrategy</a></div><div class="line"><a name="l02118"></a><span class="lineno"> 2118</span>  {</div><div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l02120"></a><span class="lineno"> 2120</span>  TestConnectionPreservation(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& graph)</div><div class="line"><a name="l02121"></a><span class="lineno"> 2121</span>  : m_Graph(graph)</div><div class="line"><a name="l02122"></a><span class="lineno"> 2122</span>  {}</div><div class="line"><a name="l02123"></a><span class="lineno"> 2123</span> </div><div class="line"><a name="l02124"></a><span class="lineno"> 2124</span>  <span class="keywordtype">void</span> ExecuteStrategy(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l02125"></a><span class="lineno"> 2125</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l02126"></a><span class="lineno"> 2126</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l02127"></a><span class="lineno"> 2127</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l02128"></a><span class="lineno"> 2128</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span>)<span class="keyword"> override</span></div><div class="line"><a name="l02129"></a><span class="lineno"> 2129</span> <span class="keyword"> </span>{</div><div class="line"><a name="l02130"></a><span class="lineno"> 2130</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(name, constants, <span class="keywordtype">id</span>, descriptor);</div><div class="line"><a name="l02131"></a><span class="lineno"> 2131</span> </div><div class="line"><a name="l02132"></a><span class="lineno"> 2132</span>  <span class="keywordflow">switch</span> (layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">GetType</a>())</div><div class="line"><a name="l02133"></a><span class="lineno"> 2133</span>  {</div><div class="line"><a name="l02134"></a><span class="lineno"> 2134</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f">armnn::LayerType::Addition</a> :</div><div class="line"><a name="l02135"></a><span class="lineno"> 2135</span>  {</div><div class="line"><a name="l02136"></a><span class="lineno"> 2136</span>  CheckLayerName(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">GetConnection</a>()-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ad0c3555b126975ad6b3e250fe2a59534">GetOwningLayerGuid</a>(), <span class="stringliteral">"reLU1"</span>);</div><div class="line"><a name="l02137"></a><span class="lineno"> 2137</span>  CheckLayerName(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">GetConnection</a>()-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ad0c3555b126975ad6b3e250fe2a59534">GetOwningLayerGuid</a>(), <span class="stringliteral">"reLU2"</span>);</div><div class="line"><a name="l02138"></a><span class="lineno"> 2138</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02139"></a><span class="lineno"> 2139</span>  }</div><div class="line"><a name="l02140"></a><span class="lineno"> 2140</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l02141"></a><span class="lineno"> 2141</span>  {}</div><div class="line"><a name="l02142"></a><span class="lineno"> 2142</span>  }</div><div class="line"><a name="l02143"></a><span class="lineno"> 2143</span>  }</div><div class="line"><a name="l02144"></a><span class="lineno"> 2144</span> </div><div class="line"><a name="l02145"></a><span class="lineno"> 2145</span>  <span class="keywordtype">void</span> CheckLayerName(<a class="code" href="classarmnn_1_1profiling_1_1_profiling_guid.xhtml">LayerGuid</a> guid, std::string expectedName)</div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>  {</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>  <span class="keywordtype">bool</span> guidFound = <span class="keyword">false</span>;</div><div class="line"><a name="l02148"></a><span class="lineno"> 2148</span>  <span class="keywordflow">for</span> (<a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* layer : m_Graph)</div><div class="line"><a name="l02149"></a><span class="lineno"> 2149</span>  {</div><div class="line"><a name="l02150"></a><span class="lineno"> 2150</span>  <span class="keywordflow">if</span> (layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afb5e65c770f6cee222db8af7581541a6">GetGuid</a>() == guid)</div><div class="line"><a name="l02151"></a><span class="lineno"> 2151</span>  {</div><div class="line"><a name="l02152"></a><span class="lineno"> 2152</span>  BOOST_CHECK_EQUAL(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>(), expectedName.c_str());</div><div class="line"><a name="l02153"></a><span class="lineno"> 2153</span>  guidFound = <span class="keyword">true</span>;</div><div class="line"><a name="l02154"></a><span class="lineno"> 2154</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02155"></a><span class="lineno"> 2155</span>  }</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span>  }</div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span>  <span class="keywordflow">if</span> (!guidFound)</div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span>  {</div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span>  BOOST_FAIL(<span class="stringliteral">"No layer matching the GUID was found"</span>);</div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span>  }</div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span>  }</div><div class="line"><a name="l02162"></a><span class="lineno"> 2162</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l02163"></a><span class="lineno"> 2163</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> m_Graph;</div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span>  };</div><div class="line"><a name="l02165"></a><span class="lineno"> 2165</span> </div><div class="line"><a name="l02166"></a><span class="lineno"> 2166</span>  <span class="keyword">class </span><a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a> : <span class="keyword">public</span> <a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a></div><div class="line"><a name="l02167"></a><span class="lineno"> 2167</span>  {</div><div class="line"><a name="l02168"></a><span class="lineno"> 2168</span>  public :</div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span>  <a class="code" href="classarmnn_1_1_network_impl.xhtml">NetworkImpl</a>* GetPNetworkImpl()</div><div class="line"><a name="l02170"></a><span class="lineno"> 2170</span>  {</div><div class="line"><a name="l02171"></a><span class="lineno"> 2171</span>  <span class="keywordflow">return</span> pNetworkImpl.get();</div><div class="line"><a name="l02172"></a><span class="lineno"> 2172</span>  }</div><div class="line"><a name="l02173"></a><span class="lineno"> 2173</span>  };</div><div class="line"><a name="l02174"></a><span class="lineno"> 2174</span> </div><div class="line"><a name="l02175"></a><span class="lineno"> 2175</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a> testNetwork;</div><div class="line"><a name="l02176"></a><span class="lineno"> 2176</span> </div><div class="line"><a name="l02177"></a><span class="lineno"> 2177</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer = testNetwork.AddInputLayer(0,<span class="stringliteral">"inputLayer1"</span>);</div><div class="line"><a name="l02178"></a><span class="lineno"> 2178</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a> ReLUDesc;</div><div class="line"><a name="l02179"></a><span class="lineno"> 2179</span>  ReLUDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l02180"></a><span class="lineno"> 2180</span> </div><div class="line"><a name="l02181"></a><span class="lineno"> 2181</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* reLULayer1 = testNetwork.AddActivationLayer(ReLUDesc, <span class="stringliteral">"reLU1"</span>);</div><div class="line"><a name="l02182"></a><span class="lineno"> 2182</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* reLULayer2 = testNetwork.AddActivationLayer(ReLUDesc, <span class="stringliteral">"reLU2"</span>);</div><div class="line"><a name="l02183"></a><span class="lineno"> 2183</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* addLayer1 = testNetwork.AddAdditionLayer(<span class="stringliteral">"addLayer1"</span>);</div><div class="line"><a name="l02184"></a><span class="lineno"> 2184</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = testNetwork.AddOutputLayer(0,<span class="stringliteral">"outPutLayer1"</span>);</div><div class="line"><a name="l02185"></a><span class="lineno"> 2185</span> </div><div class="line"><a name="l02186"></a><span class="lineno"> 2186</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(reLULayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02187"></a><span class="lineno"> 2187</span>  reLULayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(reLULayer2-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02188"></a><span class="lineno"> 2188</span>  reLULayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(addLayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02189"></a><span class="lineno"> 2189</span>  reLULayer2-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(addLayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02190"></a><span class="lineno"> 2190</span>  addLayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02191"></a><span class="lineno"> 2191</span> </div><div class="line"><a name="l02192"></a><span class="lineno"> 2192</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>({1, 2, 2, 1}), <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l02193"></a><span class="lineno"> 2193</span>  reLULayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>({1, 2, 2, 1}), <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l02194"></a><span class="lineno"> 2194</span>  reLULayer2-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>({1, 2, 2, 1}), <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l02195"></a><span class="lineno"> 2195</span>  addLayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>({1, 2, 2, 1}), <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l02196"></a><span class="lineno"> 2196</span> </div><div class="line"><a name="l02197"></a><span class="lineno"> 2197</span>  TestConnectionPreservation strategy1(testNetwork.GetPNetworkImpl()->GetGraph());</div><div class="line"><a name="l02198"></a><span class="lineno"> 2198</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(&testNetwork, strategy1);</div><div class="line"><a name="l02199"></a><span class="lineno"> 2199</span> </div><div class="line"><a name="l02200"></a><span class="lineno"> 2200</span>  <a class="code" href="namespacearmnn.xhtml#a41119e261eec9343888d2ceab1e4999a">armnn::INetworkQuantizerPtr</a> quantizer = <a class="code" href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">armnn::INetworkQuantizer::Create</a>(&testNetwork);</div><div class="line"><a name="l02201"></a><span class="lineno"> 2201</span> </div><div class="line"><a name="l02202"></a><span class="lineno"> 2202</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo = <a class="code" href="namespacearmnn.xhtml#adb77d7a922e1a8dee8490f50a5ae4bac">GetInputTensorInfo</a>(&testNetwork);</div><div class="line"><a name="l02203"></a><span class="lineno"> 2203</span> </div><div class="line"><a name="l02204"></a><span class="lineno"> 2204</span>  std::vector<float> inputData({0, 2, 0, 4});</div><div class="line"><a name="l02205"></a><span class="lineno"> 2205</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputTensor(tensorInfo, inputData.data());</div><div class="line"><a name="l02206"></a><span class="lineno"> 2206</span> </div><div class="line"><a name="l02207"></a><span class="lineno"> 2207</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors;</div><div class="line"><a name="l02208"></a><span class="lineno"> 2208</span>  inputTensors.push_back(std::make_pair(0, inputTensor));</div><div class="line"><a name="l02209"></a><span class="lineno"> 2209</span>  quantizer->Refine(inputTensors);</div><div class="line"><a name="l02210"></a><span class="lineno"> 2210</span> </div><div class="line"><a name="l02211"></a><span class="lineno"> 2211</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> quantNetwork = quantizer->ExportNetwork();</div><div class="line"><a name="l02212"></a><span class="lineno"> 2212</span> </div><div class="line"><a name="l02213"></a><span class="lineno"> 2213</span>  <a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>* testQuantNetwork = <span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">TestNetwork</a>*<span class="keyword">></span>(quantNetwork.get());</div><div class="line"><a name="l02214"></a><span class="lineno"> 2214</span> </div><div class="line"><a name="l02215"></a><span class="lineno"> 2215</span>  TestConnectionPreservation strategy2(testQuantNetwork->GetPNetworkImpl()->GetGraph());</div><div class="line"><a name="l02216"></a><span class="lineno"> 2216</span>  <a class="code" href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">VisitLayersTopologically</a>(quantNetwork.get(), strategy2);</div><div class="line"><a name="l02217"></a><span class="lineno"> 2217</span> }</div><div class="line"><a name="l02218"></a><span class="lineno"> 2218</span> </div><div class="line"><a name="l02219"></a><span class="lineno"> 2219</span> <a class="code" href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a>()</div><div class="line"><a name="l02220"></a><span class="lineno"> 2220</span> } <span class="comment">// namespace armnn</span></div><div class="ttc" id="_output_shape_of_squeeze_8cpp_xhtml_ae3a6cb217a792718f2bd0e8f45e3ca9e"><div class="ttname"><a href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE(TensorflowLiteParser)</div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a71b23d26c0f5d20416d6c77754f9806a"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a71b23d26c0f5d20416d6c77754f9806a">armnn::LayerType::TransposeConvolution2d</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a19994153bdbd7710f0af3973403bc4cc"><div class="ttname"><a href="namespacearmnn.xhtml#a19994153bdbd7710f0af3973403bc4cc">armnn::g_AsymmU8QuantizationBase</a></div><div class="ttdeci">const float g_AsymmU8QuantizationBase</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l00029">QuantizerTest.cpp:29</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00454">Descriptors.hpp:454</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">armnn::ActivationFunction::ReLu</a></div></div> +<div class="ttc" id="_ignore_unused_8hpp_xhtml"><div class="ttname"><a href="_ignore_unused_8hpp.xhtml">IgnoreUnused.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_aa51350bdd4976f3dd5a4e9d00a906b2c"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#aa51350bdd4976f3dd5a4e9d00a906b2c">armnn::INetwork::AddActivationLayer</a></div><div class="ttdeci">IConnectableLayer * AddActivationLayer(const ActivationDescriptor &activationDescriptor, const char *name=nullptr)</div><div class="ttdoc">Adds an activation layer to the network. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00218">Network.cpp:218</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a9b8e5a95f8c061bbbcdb036915dcb61a"><div class="ttname"><a href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">armnn::OffsetScalePair</a></div><div class="ttdeci">std::pair< float, int > OffsetScalePair</div><div class="ttdef"><b>Definition:</b> <a href="_network_quantization_scheme_8hpp_source.xhtml#l00016">NetworkQuantizationScheme.hpp:16</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ae20f0f2826a6549809f050b86274567f"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae20f0f2826a6549809f050b86274567f">armnn::LayerType::Concat</a></div></div> +<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml">armnn::ViewsDescriptor</a></div><div class="ttdoc">A ViewsDescriptor for the SplitterLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00206">Descriptors.hpp:206</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00062">INetwork.hpp:62</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::DepthwiseConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00506">Descriptors.hpp:506</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4af6c0e3a1c3cfabd32ae8d3ae741fcf0a"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af6c0e3a1c3cfabd32ae8d3ae741fcf0a">armnn::LayerType::Comparison</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">armnn::TransposeConvolution2dDescriptor</a></div><div class="ttdoc">A TransposeConvolution2dDescriptor for the TransposeConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01213">Descriptors.hpp:1213</a></div></div> +<div class="ttc" id="_tensor_8hpp_xhtml"><div class="ttname"><a href="_tensor_8hpp.xhtml">Tensor.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00187">Tensor.hpp:187</a></div></div> +<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional</a></div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00270">Optional.hpp:270</a></div></div> +<div class="ttc" id="structarmnn_1_1_reshape_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_reshape_descriptor.xhtml">armnn::ReshapeDescriptor</a></div><div class="ttdoc">A ReshapeDescriptor for the ReshapeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00832">Descriptors.hpp:832</a></div></div> +<div class="ttc" id="classarmnn_1_1_network_impl_xhtml_a39f1b38d89c4de186742eafcbb3b1319"><div class="ttname"><a href="classarmnn_1_1_network_impl.xhtml#a39f1b38d89c4de186742eafcbb3b1319">armnn::NetworkImpl::AddAdditionLayer</a></div><div class="ttdeci">IConnectableLayer * AddAdditionLayer(const char *name=nullptr)</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l01947">Network.cpp:1947</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_adb77d7a922e1a8dee8490f50a5ae4bac"><div class="ttname"><a href="namespacearmnn.xhtml#adb77d7a922e1a8dee8490f50a5ae4bac">armnn::GetInputTensorInfo</a></div><div class="ttdeci">TensorInfo GetInputTensorInfo(const INetwork *network)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l00080">QuantizerTest.cpp:80</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a6ee06c6045d0c5b6565a247955ef0fc2"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6ee06c6045d0c5b6565a247955ef0fc2">armnn::LayerType::BatchToSpaceNd</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a5fbc1479db5f4ff70a750cf02d58971b"><div class="ttname"><a href="namespacearmnn.xhtml#a5fbc1479db5f4ff70a750cf02d58971b">armnn::CreateNetworkWithActivationLayer</a></div><div class="ttdeci">INetworkPtr CreateNetworkWithActivationLayer(const ActivationDescriptor &descriptor, const TensorShape &shape)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l00593">QuantizerTest.cpp:593</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div> +<div class="ttc" id="_quantize_helper_8hpp_xhtml"><div class="ttname"><a href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_comparison_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_comparison_descriptor.xhtml">armnn::ComparisonDescriptor</a></div><div class="ttdoc">A ComparisonDescriptor for the ComparisonLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00078">Descriptors.hpp:78</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a9493a8c03c5ffd8777ddfd1405e494cb"><div class="ttname"><a href="namespacearmnn.xhtml#a9493a8c03c5ffd8777ddfd1405e494cb">armnn::TestNetwork</a></div><div class="ttdeci">void TestNetwork(INetwork *network, const TensorShape inShape, const TensorShape outShape)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l00540">QuantizerTest.cpp:540</a></div></div> +<div class="ttc" id="classarmnn_1_1_range_tracker_xhtml"><div class="ttname"><a href="classarmnn_1_1_range_tracker.xhtml">armnn::RangeTracker</a></div><div class="ttdef"><b>Definition:</b> <a href="_range_tracker_8hpp_source.xhtml#l00017">RangeTracker.hpp:17</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a></div><div class="ttdoc">A Convolution2dDescriptor for the Convolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00408">Descriptors.hpp:408</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a2187e1021a911b3807cc1bea2eb1a9ca"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2187e1021a911b3807cc1bea2eb1a9ca">armnn::LayerType::Stack</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_aa9c6c1a7b5380a99a536f4740f87dd59"><div class="ttname"><a href="namespacearmnn.xhtml#aa9c6c1a7b5380a99a536f4740f87dd59">armnn::CreateNetworkWithInputOutputLayers</a></div><div class="ttdeci">INetworkPtr CreateNetworkWithInputOutputLayers()</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l00636">QuantizerTest.cpp:636</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aa31904f2b3479b5a00137fd985974b4d"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa31904f2b3479b5a00137fd985974b4d">armnn::LayerType::StridedSlice</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::TransposeConvolution2dDescriptor::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#l01254">Descriptors.hpp:1254</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">armnn::LayerType::Activation</a></div></div> +<div class="ttc" id="classarmnn_1_1profiling_1_1_profiling_guid_xhtml"><div class="ttname"><a href="classarmnn_1_1profiling_1_1_profiling_guid.xhtml">armnn::profiling::ProfilingGuid</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00291">Types.hpp:291</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaacb7667f5ec2f6e8a5943b781ba6c2735"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaacb7667f5ec2f6e8a5943b781ba6c2735">armnn::ActivationFunction::LeakyReLu</a></div></div> +<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml_a8275d51ef9a584feb95726ea0522f6e5"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">armnn::SoftmaxDescriptor::m_Beta</a></div><div class="ttdeci">float m_Beta</div><div class="ttdoc">Exponentiation value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00152">Descriptors.hpp:152</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_acd7f8820d124166a38c95bc8ad38811b"><div class="ttname"><a href="namespacearmnn.xhtml#acd7f8820d124166a38c95bc8ad38811b">armnn::g_SymmS8QuantizationBase</a></div><div class="ttdeci">const float g_SymmS8QuantizationBase</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l00032">QuantizerTest.cpp:32</a></div></div> +<div class="ttc" id="structarmnn_1_1_arg_min_max_descriptor_xhtml_ab1ae6f520bb1a4da191a0ae907477f23"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_descriptor.xhtml#ab1ae6f520bb1a4da191a0ae907477f23">armnn::ArgMinMaxDescriptor::m_Function</a></div><div class="ttdeci">ArgMinMaxFunction m_Function</div><div class="ttdoc">Specify if the function is to find Min or Max. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00070">Descriptors.hpp:70</a></div></div> +<div class="ttc" id="classarmnn_1_1_unimplemented_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00098">Exceptions.hpp:98</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa1e34af023adeb7d5f484f8eb4b9826b6"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa1e34af023adeb7d5f484f8eb4b9826b6">armnn::ActivationFunction::Abs</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a41119e261eec9343888d2ceab1e4999a"><div class="ttname"><a href="namespacearmnn.xhtml#a41119e261eec9343888d2ceab1e4999a">armnn::INetworkQuantizerPtr</a></div><div class="ttdeci">std::unique_ptr< class INetworkQuantizer, void(*)(INetworkQuantizer *quantizer)> INetworkQuantizerPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_quantizer_8hpp_source.xhtml#l00029">INetworkQuantizer.hpp:29</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml">armnn::INetwork</a></div><div class="ttdoc">Main network class which provides the interface for building up a neural network. ...</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00178">INetwork.hpp:178</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a337c392144dca0d18290c6b4711a2279"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a337c392144dca0d18290c6b4711a2279">armnn::LayerType::SpaceToBatchNd</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4acab78faff25393e9defd1911cb58133e"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acab78faff25393e9defd1911cb58133e">armnn::LayerType::FullyConnected</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_aa01bce88f89975a5a031db4cc8861527"><div class="ttname"><a href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">armnn::InputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class ConstTensor > > InputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00340">Tensor.hpp:340</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a52cbff9d344ba4a1fe01d4da2c1f7ba2"><div class="ttname"><a href="namespacearmnn.xhtml#a52cbff9d344ba4a1fe01d4da2c1f7ba2">armnn::SetupQuantize</a></div><div class="ttdeci">std::vector< uint8_t > SetupQuantize(float value)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l01970">QuantizerTest.cpp:1970</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantizer_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_quantizer_options.xhtml">armnn::QuantizerOptions</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_network_quantizer_8hpp_source.xhtml#l00015">INetworkQuantizer.hpp:15</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__software__tools_8dox_source.xhtml#l00006">01_00_software_tools.dox:6</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &&...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a21baa4498161d195f5bb2e3627344ba4"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a21baa4498161d195f5bb2e3627344ba4">armnn::LayerType::InstanceNormalization</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_aad4b8cb9a4d882a48bc21510f0d1a938"><div class="ttname"><a href="namespacearmnn.xhtml#aad4b8cb9a4d882a48bc21510f0d1a938">armnn::CreateNetworkWithFullyConnectedLayer</a></div><div class="ttdeci">INetworkPtr CreateNetworkWithFullyConnectedLayer(const bool biasEnabled, const TensorShape &inputShape, const TensorShape &outputShape)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l00994">QuantizerTest.cpp:994</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_space_to_depth_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_space_to_depth_descriptor.xhtml">armnn::SpaceToDepthDescriptor</a></div><div class="ttdoc">A SpaceToDepthDescriptor for the SpaceToDepthLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00884">Descriptors.hpp:884</a></div></div> +<div class="ttc" id="classarmnn_1_1_optional_reference_switch_xhtml_a77c7d528ac063d870b8c8426ec81c1c3"><div class="ttname"><a href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">armnn::OptionalReferenceSwitch< std::is_reference< T >::value, T >::value</a></div><div class="ttdeci">const T & value() const</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00146">Optional.hpp:146</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">armnn::BatchToSpaceNdDescriptor</a></div><div class="ttdoc">A BatchToSpaceNdDescriptor for the BatchToSpaceNdLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00673">Descriptors.hpp:673</a></div></div> +<div class="ttc" id="classarmnn_1_1_range_tracker_xhtml_a997e96288bdb106c922202e3f33d5d7b"><div class="ttname"><a href="classarmnn_1_1_range_tracker.xhtml#a997e96288bdb106c922202e3f33d5d7b">armnn::RangeTracker::MinMaxRange</a></div><div class="ttdeci">std::pair< float, float > MinMaxRange</div><div class="ttdef"><b>Definition:</b> <a href="_range_tracker_8hpp_source.xhtml#l00020">RangeTracker.hpp:20</a></div></div> +<div class="ttc" id="classarmnn_1_1_network_impl_xhtml"><div class="ttname"><a href="classarmnn_1_1_network_impl.xhtml">armnn::NetworkImpl</a></div><div class="ttdoc">Private implementation of INetwork. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8hpp_source.xhtml#l00031">Network.hpp:31</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ab8cf8f9fb6792e654c2d8d8382f6f01b"><div class="ttname"><a href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a></div><div class="ttdeci">int LayerBindingId</div><div class="ttdoc">Type of identifiers for bindable layers (inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00210">Types.hpp:210</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a0c5967f09e0669c840ebb1ed0da85e32"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a0c5967f09e0669c840ebb1ed0da85e32">armnn::LayerType::Prelu</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &tensorInfo)=0</div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_aa6c1c42ea44777302e87ce0fad5ac510"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#aa6c1c42ea44777302e87ce0fad5ac510">armnn::INetwork::AddInputLayer</a></div><div class="ttdeci">IConnectableLayer * AddInputLayer(LayerBindingId id, const char *name=nullptr)</div><div class="ttdoc">Adds an input layer to the network. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00050">Network.cpp:50</a></div></div> +<div class="ttc" id="structarmnn_1_1_stand_in_descriptor_xhtml_abb8a2d2bb8cc594c26aaa70c820ac5cc"><div class="ttname"><a href="structarmnn_1_1_stand_in_descriptor.xhtml#abb8a2d2bb8cc594c26aaa70c820ac5cc">armnn::StandInDescriptor::m_NumOutputs</a></div><div class="ttdeci">uint32_t m_NumOutputs</div><div class="ttdoc">Number of output tensors. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01072">Descriptors.hpp:1072</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a3109511a0984c50aae123ff702f327d2"><div class="ttname"><a href="namespacearmnn.xhtml#a3109511a0984c50aae123ff702f327d2">armnn::CreateNetworkWithArgMinMaxLayer</a></div><div class="ttdeci">INetworkPtr CreateNetworkWithArgMinMaxLayer(const ArgMinMaxDescriptor &descriptor, const TensorShape &shape)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l00614">QuantizerTest.cpp:614</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a81b5ff8545adad19a1c9d4ca076d552c"><div class="ttname"><a href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">armnn::GetDataTypeName</a></div><div class="ttdeci">constexpr const char * GetDataTypeName(DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00180">TypesUtils.hpp:180</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml">armnn::ResizeDescriptor</a></div><div class="ttdoc">A ResizeDescriptor for the ResizeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00794">Descriptors.hpp:794</a></div></div> +<div class="ttc" id="classarmnn_1_1_range_tracker_xhtml_a507bae23f59e94b4161886ebe663cdf4"><div class="ttname"><a href="classarmnn_1_1_range_tracker.xhtml#a507bae23f59e94b4161886ebe663cdf4">armnn::RangeTracker::GetRange</a></div><div class="ttdeci">MinMaxRange GetRange(LayerGuid guid, unsigned int idx) const</div><div class="ttdoc">Retrieve the Range for a particular output slot on a particular layer. </div><div class="ttdef"><b>Definition:</b> <a href="_range_tracker_8cpp_source.xhtml#l00029">RangeTracker.cpp:29</a></div></div> +<div class="ttc" id="structarmnn_1_1_base_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a></div><div class="ttdoc">Base class for all descriptors. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00022">Descriptors.hpp:22</a></div></div> +<div class="ttc" id="structarmnn_1_1_stack_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_stack_descriptor.xhtml">armnn::StackDescriptor</a></div><div class="ttdoc">A StackDescriptor for the StackLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01024">Descriptors.hpp:1024</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aca39930e22f40d10155a57dba32240bb"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aca39930e22f40d10155a57dba32240bb">armnn::LayerType::Quantize</a></div></div> +<div class="ttc" id="_polymorphic_downcast_8hpp_xhtml"><div class="ttname"><a href="_polymorphic_downcast_8hpp.xhtml">PolymorphicDowncast.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_strategy_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_strategy.xhtml">armnn::IStrategy</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_strategy_8hpp_source.xhtml#l00013">IStrategy.hpp:13</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a4353fa80ece13e3b1664881c27f5a67c"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a4353fa80ece13e3b1664881c27f5a67c">armnn::INetwork::pNetworkImpl</a></div><div class="ttdeci">std::unique_ptr< NetworkImpl > pNetworkImpl</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00693">INetwork.hpp:693</a></div></div> +<div class="ttc" id="classarmnn_1_1_network_impl_xhtml_aa6c1c42ea44777302e87ce0fad5ac510"><div class="ttname"><a href="classarmnn_1_1_network_impl.xhtml#aa6c1c42ea44777302e87ce0fad5ac510">armnn::NetworkImpl::AddInputLayer</a></div><div class="ttdeci">IConnectableLayer * AddInputLayer(LayerBindingId id, const char *name=nullptr)</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l01682">Network.cpp:1682</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="namespacearmnn_xhtml_a120c131df35d78b3a56cb0f07decaf35"><div class="ttname"><a href="namespacearmnn.xhtml#a120c131df35d78b3a56cb0f07decaf35">armnn::CreateStartOfLeakyReluNetwork</a></div><div class="ttdeci">IConnectableLayer * CreateStartOfLeakyReluNetwork(INetwork *network, const TensorInfo &info)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l01283">QuantizerTest.cpp:1283</a></div></div> +<div class="ttc" id="_assert_8hpp_xhtml_a91c4dfde57907d7698c7531785690a7f"><div class="ttname"><a href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a></div><div class="ttdeci">#define ARMNN_ASSERT_MSG(COND, MSG)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00015">Assert.hpp:15</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a929027fc5caf6a6e20d3b9ac6fcd128b"><div class="ttname"><a href="namespacearmnn.xhtml#a929027fc5caf6a6e20d3b9ac6fcd128b">armnn::ApplyStrategyToLayers</a></div><div class="ttdeci">void ApplyStrategyToLayers(const LayerContainer &layerContainer, IStrategy &strategy)</div><div class="ttdef"><b>Definition:</b> <a href="_network_quantizer_utils_8hpp_source.xhtml#l00061">NetworkQuantizerUtils.hpp:61</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233"><div class="ttname"><a href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">armnn::ArgMinMaxFunction::Max</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">armnn::TensorInfo::GetQuantizationOffset</a></div><div class="ttdeci">int32_t GetQuantizationOffset() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00469">Tensor.cpp:469</a></div></div> +<div class="ttc" id="structarmnn_1_1_arg_min_max_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_descriptor.xhtml">armnn::ArgMinMaxDescriptor</a></div><div class="ttdoc">An ArgMinMaxDescriptor for ArgMinMaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00056">Descriptors.hpp:56</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00452">Tensor.cpp:452</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00194">Tensor.hpp:194</a></div></div> +<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a></div><div class="ttdoc">An OriginsDescriptor for the ConcatLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00163">Descriptors.hpp:163</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_optional_base_xhtml_a86b749ce2c4bc627fa8a1fcfaf0e314f"><div class="ttname"><a href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">armnn::OptionalBase::has_value</a></div><div class="ttdeci">bool has_value() const noexcept</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00053">Optional.hpp:53</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a061aafb62b3769f55369845c3990ec7a"><div class="ttname"><a href="namespacearmnn.xhtml#a061aafb62b3769f55369845c3990ec7a">armnn::MinMaxRangeMap</a></div><div class="ttdeci">std::unordered_map< LayerGuid, MinMaxRanges > MinMaxRangeMap</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l00027">QuantizerTest.cpp:27</a></div></div> +<div class="ttc" id="_types_8hpp_xhtml"><div class="ttname"><a href="_types_8hpp.xhtml">Types.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a></div><div class="ttdoc">A FullyConnectedDescriptor for the FullyConnectedLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00389">Descriptors.hpp:389</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_afb5e65c770f6cee222db8af7581541a6"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#afb5e65c770f6cee222db8af7581541a6">armnn::IConnectableLayer::GetGuid</a></div><div class="ttdeci">virtual LayerGuid GetGuid() const =0</div><div class="ttdoc">Returns the unique id of the layer. </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#l00402">Descriptors.hpp:402</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">armnn::LayerType::Permute</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a9d723d04c40bfd81835c0766a698cf63"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a9d723d04c40bfd81835c0766a698cf63">armnn::LayerType::Resize</a></div></div> +<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00314">Tensor.hpp:314</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a">armnn::LayerType::Convolution2d</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_af5790069aa11fd1c5bb2e17cecb06528"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#af5790069aa11fd1c5bb2e17cecb06528">armnn::INetwork::AddOutputLayer</a></div><div class="ttdeci">IConnectableLayer * AddOutputLayer(LayerBindingId id, const char *name=nullptr)</div><div class="ttdoc">Adds an output layer to the network. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00359">Network.cpp:359</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa6bba7052636d1740303b1b2ef3b53fef"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa6bba7052636d1740303b1b2ef3b53fef">armnn::ActivationFunction::SoftReLu</a></div></div> +<div class="ttc" id="classarmnn_1_1_range_tracker_xhtml_a084c5aacd7e3bb07f2cfd5a8e9b0dd30"><div class="ttname"><a href="classarmnn_1_1_range_tracker.xhtml#a084c5aacd7e3bb07f2cfd5a8e9b0dd30">armnn::RangeTracker::HasRanges</a></div><div class="ttdeci">bool HasRanges(LayerGuid guid) const</div><div class="ttdoc">Query that there is an entry for a layer. </div><div class="ttdef"><b>Definition:</b> <a href="_range_tracker_8hpp_source.xhtml#l00032">RangeTracker.hpp:32</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a5abbe8a9ee003c1379a921dbe2745b81"><div class="ttname"><a href="namespacearmnn.xhtml#a5abbe8a9ee003c1379a921dbe2745b81">armnn::TestQuantizeDepthwiseConvolution2d</a></div><div class="ttdeci">void TestQuantizeDepthwiseConvolution2d(bool useBiases)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l01120">QuantizerTest.cpp:1120</a></div></div> +<div class="ttc" id="classarmnn_1_1_network_impl_xhtml_af5790069aa11fd1c5bb2e17cecb06528"><div class="ttname"><a href="classarmnn_1_1_network_impl.xhtml#af5790069aa11fd1c5bb2e17cecb06528">armnn::NetworkImpl::AddOutputLayer</a></div><div class="ttdeci">IConnectableLayer * AddOutputLayer(LayerBindingId id, const char *name=nullptr)</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l01957">Network.cpp:1957</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ac757baefa4b72b54c38f713f86418f8a"><div class="ttname"><a href="namespacearmnn.xhtml#ac757baefa4b72b54c38f713f86418f8a">armnn::MinMaxRanges</a></div><div class="ttdeci">std::vector< MinMaxRange > MinMaxRanges</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l00026">QuantizerTest.cpp:26</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a31d953b9d49a6b4378f45097047976d0"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a31d953b9d49a6b4378f45097047976d0">armnn::LayerType::Softmax</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a685739c4eb65a580e075282cfe6787d6"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">armnn::TensorInfo::SetQuantizationScale</a></div><div class="ttdeci">void SetQuantizationScale(float scale)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00464">Tensor.cpp:464</a></div></div> +<div class="ttc" id="structarmnn_1_1_stand_in_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_stand_in_descriptor.xhtml">armnn::StandInDescriptor</a></div><div class="ttdoc">A StandInDescriptor for the StandIn layer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01054">Descriptors.hpp:1054</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aa7c59ccedc6a3bd90c17f3b990afefad"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa7c59ccedc6a3bd90c17f3b990afefad">armnn::LayerType::Reshape</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ad662867a41bfb30b9f75dda2b5849001"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad662867a41bfb30b9f75dda2b5849001">armnn::LayerType::Pooling2d</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a10d15f3df1ab52b3b915a4be1dbf386b"><div class="ttname"><a href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">armnn::BOOST_AUTO_TEST_CASE</a></div><div class="ttdeci">BOOST_AUTO_TEST_CASE(CheckConvolution2dLayer)</div><div class="ttdef"><b>Definition:</b> <a href="_const_tensor_layer_visitor_8cpp_source.xhtml#l00268">ConstTensorLayerVisitor.cpp:268</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a></div><div class="ttdoc">An ActivationDescriptor for the ActivationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00025">Descriptors.hpp:25</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a9bc35e069257a508e14ed82965a8661d"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a9bc35e069257a508e14ed82965a8661d">armnn::LayerType::Dequantize</a></div></div> +<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_a8aeddebdcf02e1832b22203c08a6b678"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">armnn::BaseTensor::GetInfo</a></div><div class="ttdeci">const TensorInfo & GetInfo() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00282">Tensor.hpp:282</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_a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">armnn::ActivationFunction::BoundedReLu</a></div><div class="ttdoc">min(a, max(b, input)) ReLu1 & ReLu6. </div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f">armnn::LayerType::Addition</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a46c3fa15c46fb0d1dcdc24d0ea5cb5cd"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">armnn::ResizeDescriptor::m_TargetHeight</a></div><div class="ttdeci">uint32_t m_TargetHeight</div><div class="ttdoc">Target height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00818">Descriptors.hpp:818</a></div></div> +<div class="ttc" id="structarmnn_1_1_slice_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_slice_descriptor.xhtml">armnn::SliceDescriptor</a></div><div class="ttdoc">A SliceDescriptor for the SliceLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01001">Descriptors.hpp:1001</a></div></div> +<div class="ttc" id="classarmnn_1_1_layer_visitor_base_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer_visitor_base.xhtml">armnn::LayerVisitorBase</a></div><div class="ttdoc">Visitor base class with empty implementations. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_visitor_base_8hpp_source.xhtml#l00025">LayerVisitorBase.hpp:25</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a1a9a6dea47de10df8e3c76dd45df56fb"><div class="ttname"><a href="namespacearmnn.xhtml#a1a9a6dea47de10df8e3c76dd45df56fb">armnn::g_TestTolerance</a></div><div class="ttdeci">const float g_TestTolerance</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l00034">QuantizerTest.cpp:34</a></div></div> +<div class="ttc" id="classarmnn_1_1_graph_xhtml"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml">armnn::Graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00029">Graph.hpp:29</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_adceb04ae84c524e4d01881e3754a4d59"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">armnn::IConnectableLayer::GetType</a></div><div class="ttdeci">virtual LayerType GetType() const =0</div><div class="ttdoc">Returns the armnn::LayerType of this layer. </div></div> +<div class="ttc" id="_i_network_8hpp_xhtml"><div class="ttname"><a href="_i_network_8hpp.xhtml">INetwork.hpp</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a5e7ff12da912dc79e7e547281823fa4a"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a5e7ff12da912dc79e7e547281823fa4a">armnn::LayerType::SpaceToDepth</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a245661fc96c9c4a9b898e1d98c8c6962"><div class="ttname"><a href="namespacearmnn.xhtml#a245661fc96c9c4a9b898e1d98c8c6962">armnn::ValidateFullyConnectedLayer</a></div><div class="ttdeci">void ValidateFullyConnectedLayer(const bool biasEnabled)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l01032">QuantizerTest.cpp:1032</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a1465480794787d2278d3f0d2e6d887b4"><div class="ttname"><a href="namespacearmnn.xhtml#a1465480794787d2278d3f0d2e6d887b4">armnn::g_SymmS16QuantizationBase</a></div><div class="ttdeci">const float g_SymmS16QuantizationBase</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l00033">QuantizerTest.cpp:33</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4af97adbfc88b7012a0243215b1076e7e7"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af97adbfc88b7012a0243215b1076e7e7">armnn::LayerType::DepthwiseConvolution2d</a></div></div> +<div class="ttc" id="classarmnn_1_1_range_tracker_xhtml_a8e12342fc420701fbffd97025421575a"><div class="ttname"><a href="classarmnn_1_1_range_tracker.xhtml#a8e12342fc420701fbffd97025421575a">armnn::RangeTracker::IsEmpty</a></div><div class="ttdeci">bool IsEmpty() const</div><div class="ttdoc">Query function to check that the RangeTracker is empty. </div><div class="ttdef"><b>Definition:</b> <a href="_range_tracker_8hpp_source.xhtml#l00029">RangeTracker.hpp:29</a></div></div> +<div class="ttc" id="structarmnn_1_1_space_to_batch_nd_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml">armnn::SpaceToBatchNdDescriptor</a></div><div class="ttdoc">A SpaceToBatchNdDescriptor for the SpaceToBatchNdLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00852">Descriptors.hpp:852</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaaed67cf7d54c570e4c4891800f085f41d"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaed67cf7d54c570e4c4891800f085f41d">armnn::ActivationFunction::Elu</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml_a017b2990003a014234f13e999dc7c689"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">armnn::ActivationDescriptor::m_A</a></div><div class="ttdeci">float m_A</div><div class="ttdoc">Alpha upper bound value used by the activation functions. (BoundedReLu, Linear, TanH, Elu). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00050">Descriptors.hpp:50</a></div></div> +<div class="ttc" id="structarmnn_1_1_fill_descriptor_xhtml_ab3ebc5cf4a617d43371a4cb7fecdeb32"><div class="ttname"><a href="structarmnn_1_1_fill_descriptor.xhtml#ab3ebc5cf4a617d43371a4cb7fecdeb32">armnn::FillDescriptor::m_Value</a></div><div class="ttdeci">float m_Value</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00738">Descriptors.hpp:738</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255">armnn::LayerType::Constant</a></div></div> +<div class="ttc" id="_profiler_tests_8cpp_xhtml_af7f71af5c6c124222dd1c42c5df892f4"><div class="ttname"><a href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE_END()</div></div> +<div class="ttc" id="structarmnn_1_1_empty_optional_xhtml"><div class="ttname"><a href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a></div><div class="ttdoc">EmptyOptional is used to initialize the Optional class in case we want to have default value for an O...</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00032">Optional.hpp:32</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a41cb9b797ebc6f6f6314e3ded935f4cf"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a41cb9b797ebc6f6f6314e3ded935f4cf">armnn::LayerType::Splitter</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a997e96288bdb106c922202e3f33d5d7b"><div class="ttname"><a href="namespacearmnn.xhtml#a997e96288bdb106c922202e3f33d5d7b">armnn::MinMaxRange</a></div><div class="ttdeci">std::pair< float, float > MinMaxRange</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l00025">QuantizerTest.cpp:25</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a0e2bce68a1f7eff47ead4d9a2804eb91"><div class="ttname"><a href="namespacearmnn.xhtml#a0e2bce68a1f7eff47ead4d9a2804eb91">armnn::QuantizeConstant</a></div><div class="ttdeci">void QuantizeConstant(const srcType *src, uint8_t *dst, size_t numElements, float &scale, int &offset)</div><div class="ttdef"><b>Definition:</b> <a href="_network_quantizer_utils_8hpp_source.xhtml#l00023">NetworkQuantizerUtils.hpp:23</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="structarmnn_1_1_stand_in_descriptor_xhtml_aed6086070440ceb94129bef06f70173f"><div class="ttname"><a href="structarmnn_1_1_stand_in_descriptor.xhtml#aed6086070440ceb94129bef06f70173f">armnn::StandInDescriptor::m_NumInputs</a></div><div class="ttdeci">uint32_t m_NumInputs</div><div class="ttdoc">Number of input tensors. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01070">Descriptors.hpp:1070</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a14cfd39cfc30682fa821ade3dd298426"><div class="ttname"><a href="namespacearmnn.xhtml#a14cfd39cfc30682fa821ade3dd298426">armnn::TestQuantizeConvolution2d</a></div><div class="ttdeci">void TestQuantizeConvolution2d(bool useBiases)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l01073">QuantizerTest.cpp:1073</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a8dbab5d1803ba220601b477f8412bfe1"><div class="ttname"><a href="namespacearmnn.xhtml#a8dbab5d1803ba220601b477f8412bfe1">armnn::VisitLayersTopologically</a></div><div class="ttdeci">void VisitLayersTopologically(const INetwork *inputNetwork, IStrategy &visitor)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l00073">QuantizerTest.cpp:73</a></div></div> +<div class="ttc" id="classarmnn_1_1_network_impl_xhtml_afe0a4f719f9752a405e71878da7012ba"><div class="ttname"><a href="classarmnn_1_1_network_impl.xhtml#afe0a4f719f9752a405e71878da7012ba">armnn::NetworkImpl::GetGraph</a></div><div class="ttdeci">const Graph & GetGraph() const</div><div class="ttdef"><b>Definition:</b> <a href="_network_8hpp_source.xhtml#l00037">Network.hpp:37</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a6fff4b4b1b5d4d37c9cf53d0e31c05dd"><div class="ttname"><a href="namespacearmnn.xhtml#a6fff4b4b1b5d4d37c9cf53d0e31c05dd">armnn::CompleteLeakyReluNetwork</a></div><div class="ttdeci">void CompleteLeakyReluNetwork(INetwork *network, IConnectableLayer *activation, IConnectableLayer *layerUnderTest, const TensorInfo &info)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l01304">QuantizerTest.cpp:1304</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a2139684546b147c106b329f41547640c"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2139684546b147c106b329f41547640c">armnn::LayerType::ArgMinMax</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot & GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div> +<div class="ttc" id="classarmnn_1_1_i_input_slot_xhtml_a81fbf6103761e55061b62ba989b00f10"><div class="ttname"><a href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">armnn::IInputSlot::GetConnection</a></div><div class="ttdeci">virtual const IOutputSlot * GetConnection() const =0</div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ae4743c3ec15d1d84169b17264634692e"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae4743c3ec15d1d84169b17264634692e">armnn::LayerType::BatchNormalization</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml">armnn::StridedSliceDescriptor</a></div><div class="ttdoc">A StridedSliceDescriptor for the StridedSliceLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01076">Descriptors.hpp:1076</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a9943775a364fc4ab53b85ac88f311886"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">armnn::IOutputSlot::GetTensorInfo</a></div><div class="ttdeci">virtual const TensorInfo & GetTensorInfo() const =0</div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot & GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index. </div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ac21dbda57d88c21ec9857f5d1522c488"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ac21dbda57d88c21ec9857f5d1522c488">armnn::LayerType::LogSoftmax</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_afcc1c3a20bd2860e0ddd21674389246f"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">armnn::IConnectableLayer::GetName</a></div><div class="ttdeci">virtual const char * GetName() const =0</div><div class="ttdoc">Returns the name of the layer. </div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a731729ad1b2c0eb9399b62c770b3482d"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a731729ad1b2c0eb9399b62c770b3482d">armnn::LayerType::DepthToSpace</a></div></div> +<div class="ttc" id="classarmnn_1_1_graph_xhtml_a919fb58873ef3a6549e4490e226f2eae"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a919fb58873ef3a6549e4490e226f2eae">armnn::Graph::GetInputLayers</a></div><div class="ttdeci">InputLayersAccessor GetInputLayers() const</div><div class="ttdoc">Returns a wrapper object with begin(), end() methods to iterate over the input layers in a range-base...</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00185">Graph.hpp:185</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00173">INetwork.hpp:173</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4adb3e3f51c9107e26c9bccf9a188ce2ed"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4adb3e3f51c9107e26c9bccf9a188ce2ed">armnn::LayerType::Fill</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a9c91b774c3089c55df77cc3a42da72de"><div class="ttname"><a href="namespacearmnn.xhtml#a9c91b774c3089c55df77cc3a42da72de">armnn::CreateNetworkWithSoftmaxLayer</a></div><div class="ttdeci">INetworkPtr CreateNetworkWithSoftmaxLayer(const SoftmaxDescriptor &descriptor, const TensorShape &shape)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l01210">QuantizerTest.cpp:1210</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &destination)=0</div></div> +<div class="ttc" id="namespacearmnn_xhtml_a2d299363c9fc33334c571fa29ca4f58caa4cbdbb6070a5abb35fc95ecf1e22c14"><div class="ttname"><a href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58caa4cbdbb6070a5abb35fc95ecf1e22c14">armnn::ComparisonOperation::LessOrEqual</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_ad0c3555b126975ad6b3e250fe2a59534"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#ad0c3555b126975ad6b3e250fe2a59534">armnn::IOutputSlot::GetOwningLayerGuid</a></div><div class="ttdeci">virtual LayerGuid GetOwningLayerGuid() const =0</div></div> +<div class="ttc" id="namespacearmnn_xhtml_abe34cf42d7c8515ecd15d11f4aeb399c"><div class="ttname"><a href="namespacearmnn.xhtml#abe34cf42d7c8515ecd15d11f4aeb399c">armnn::PreserveTypeTestImpl</a></div><div class="ttdeci">void PreserveTypeTestImpl(const DataType &dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l02065">QuantizerTest.cpp:2065</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a></div><div class="ttdoc">A Pooling2dDescriptor for the Pooling2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00329">Descriptors.hpp:329</a></div></div> +<div class="ttc" id="structarmnn_1_1_instance_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_instance_normalization_descriptor.xhtml">armnn::InstanceNormalizationDescriptor</a></div><div class="ttdoc">An InstanceNormalizationDescriptor for InstanceNormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00645">Descriptors.hpp:645</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa32a843da6ea40ab3b17a3421ccdf671b"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa32a843da6ea40ab3b17a3421ccdf671b">armnn::ActivationFunction::Linear</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186">armnn::ActivationFunction::HardSwish</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a464f0ff87b1aabf71febaa71321dd40b"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create(NetworkOptions networkOptions={})</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00510">Network.cpp:510</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ad140d37ad98c12ccd8e1c432f548bcdb"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad140d37ad98c12ccd8e1c432f548bcdb">armnn::LayerType::Slice</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml_a28c4c9cb15f6be3499abbc46b356060b"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">armnn::ActivationDescriptor::m_B</a></div><div class="ttdeci">float m_B</div><div class="ttdoc">Beta lower bound value used by the activation functions. (BoundedReLu, Linear, TanH). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00052">Descriptors.hpp:52</a></div></div> +<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::SoftmaxDescriptor</a></div><div class="ttdoc">A SoftmaxDescriptor for the SoftmaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00139">Descriptors.hpp:139</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml_af10fa7883e3579950f477bee92a64844"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">armnn::ActivationDescriptor::m_Function</a></div><div class="ttdeci">ActivationFunction m_Function</div><div class="ttdoc">The activation function to use (Sigmoid, TanH, Linear, ReLu, BoundedReLu, SoftReLu, LeakyReLu, Abs, Sqrt, Square, Elu). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00048">Descriptors.hpp:48</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_afa7a0a639e2772ff2ced67d77be810c0"><div class="ttname"><a href="namespacearmnn.xhtml#afa7a0a639e2772ff2ced67d77be810c0">armnn::TestQuantizeTransposeConvolution2d</a></div><div class="ttdeci">void TestQuantizeTransposeConvolution2d(bool useBiases)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l01819">QuantizerTest.cpp:1819</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a09bdfaa922d72ce0d9ec014dfa8f8c95"><div class="ttname"><a href="namespacearmnn.xhtml#a09bdfaa922d72ce0d9ec014dfa8f8c95">armnn::g_AsymmS8QuantizationBase</a></div><div class="ttdeci">const float g_AsymmS8QuantizationBase</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l00031">QuantizerTest.cpp:31</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a></div><div class="ttdoc">A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00460">Descriptors.hpp:460</a></div></div> +<div class="ttc" id="classarmnn_1_1_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml">armnn::Layer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00210">Layer.hpp:210</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_quantizer_xhtml_a3a4d01d9351c02a703740290f226441f"><div class="ttname"><a href="classarmnn_1_1_i_network_quantizer.xhtml#a3a4d01d9351c02a703740290f226441f">armnn::INetworkQuantizer::Create</a></div><div class="ttdeci">static INetworkQuantizerPtr Create(INetwork *inputNetwork, const QuantizerOptions &options=QuantizerOptions())</div><div class="ttdoc">Create Quantizer object wrapped in unique_ptr. </div><div class="ttdef"><b>Definition:</b> <a href="_network_quantizer_8cpp_source.xhtml#l00041">NetworkQuantizer.cpp:41</a></div></div> +<div class="ttc" id="structarmnn_1_1_fill_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fill_descriptor.xhtml">armnn::FillDescriptor</a></div><div class="ttdoc">A FillDescriptor for the FillLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00723">Descriptors.hpp:723</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml">armnn::BatchNormalizationDescriptor</a></div><div class="ttdoc">A BatchNormalizationDescriptor for the BatchNormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00626">Descriptors.hpp:626</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div> +<div class="ttc" id="_i_network_quantizer_8hpp_xhtml"><div class="ttname"><a href="_i_network_quantizer_8hpp.xhtml">INetworkQuantizer.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_override_input_range_strategy_xhtml"><div class="ttname"><a href="classarmnn_1_1_override_input_range_strategy.xhtml">armnn::OverrideInputRangeStrategy</a></div><div class="ttdef"><b>Definition:</b> <a href="_override_input_range_visitor_8hpp_source.xhtml#l00016">OverrideInputRangeVisitor.hpp:16</a></div></div> +<div class="ttc" id="structarmnn_1_1_permute_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_permute_descriptor.xhtml">armnn::PermuteDescriptor</a></div><div class="ttdoc">A PermuteDescriptor for the PermuteLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00118">Descriptors.hpp:118</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">armnn::ActivationFunction::TanH</a></div></div> +</div><!-- fragment --></div><!-- contents --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for treeview function! --> + <ul> + <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_e0a84d05c80a2ef4231141dcbbeac5c8.xhtml">armnn</a></li><li class="navelem"><a class="el" href="dir_9d86fd1fbecbedf5bdb69c7e7235fe5f.xhtml">test</a></li><li class="navelem"><a class="el" href="_quantizer_test_8cpp.xhtml">QuantizerTest.cpp</a></li> + <li class="footer">Generated on Thu Feb 25 2021 17:27:29 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> |