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author | Nikhil Raj <nikhil.raj@arm.com> | 2022-06-17 13:24:58 +0100 |
---|---|---|
committer | Nikhil Raj <nikhil.raj@arm.com> | 2022-06-17 13:24:58 +0100 |
commit | d5d43d82c0137e08553e44345c609cdd1a7931c7 (patch) | |
tree | f1509f7fa94db0373a2c127682dd3d0ccc1915bd /22.05.01/_serializer_tests_8cpp_source.xhtml | |
parent | 549b9600a6eaf0727fa084465a75f173edf8f381 (diff) | |
download | armnn-d5d43d82c0137e08553e44345c609cdd1a7931c7.tar.gz |
Update Doxygen for 22.05 patch release
* Pooling3D added to tfLite delegate
* Available in tag 22.05.01
Signed-off-by: Nikhil Raj <nikhil.raj@arm.com>
Change-Id: I8d605bba4e87d30baa2c6d7b338c78a4400dc021
Diffstat (limited to '22.05.01/_serializer_tests_8cpp_source.xhtml')
-rw-r--r-- | 22.05.01/_serializer_tests_8cpp_source.xhtml | 306 |
1 files changed, 306 insertions, 0 deletions
diff --git a/22.05.01/_serializer_tests_8cpp_source.xhtml b/22.05.01/_serializer_tests_8cpp_source.xhtml new file mode 100644 index 0000000000..466f83f461 --- /dev/null +++ b/22.05.01/_serializer_tests_8cpp_source.xhtml @@ -0,0 +1,306 @@ +<!-- 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/armnnSerializer/test/SerializerTests.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"> + $(document).ready(initResizable); +</script> +<link href="search/search.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="search/searchdata.js"></script> +<script type="text/javascript" src="search/search.js"></script> +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + extensions: ["tex2jax.js"], + jax: ["input/TeX","output/HTML-CSS"], +}); +</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> +<link href="doxygen.css" rel="stylesheet" type="text/css" /> +<link href="stylesheet.css" rel="stylesheet" type="text/css"/> +</head> +<body> +<div id="top"><!-- do not remove this div, it is closed by doxygen! --> +<div id="titlearea"> +<table cellspacing="0" cellpadding="0"> + <tbody> + <tr style="height: 56px;"> + <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/> + <td style="padding-left: 0.5em;"> + <div id="projectname"> +  <span id="projectnumber">22.05.01</span> + </div> + </td> + </tr> + </tbody> +</table> +</div> +<!-- end header part --> +<!-- Generated by Doxygen 1.8.13 --> +<script type="text/javascript"> +var searchBox = new SearchBox("searchBox", "search",false,'Search'); +</script> +<script type="text/javascript" src="menudata.js"></script> +<script type="text/javascript" src="menu.js"></script> +<script type="text/javascript"> +$(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('_serializer_tests_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">SerializerTests.cpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_serializer_tests_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 "../Serializer.hpp"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "<a class="code" href="_serializer_test_utils_8hpp.xhtml">SerializerTestUtils.hpp</a>"</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_descriptors_8hpp.xhtml">armnn/Descriptors.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_types_utils_8hpp.xhtml">armnn/TypesUtils.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_lstm_params_8hpp.xhtml">armnn/LstmParams.hpp</a>></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_quantized_lstm_params_8hpp.xhtml">armnn/QuantizedLstmParams.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <<a class="code" href="_i_deserializer_8hpp.xhtml">armnnDeserializer/IDeserializer.hpp</a>></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</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="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <random></span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <vector></span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> </div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include <doctest/doctest.h></span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="keyword">using</span> <a class="code" href="classarmnn_deserializer_1_1_i_deserializer.xhtml">armnnDeserializer::IDeserializer</a>;</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> </div><div class="line"><a name="l00024"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#afad5df20f3fea32614ad88b00f5849fc"> 24</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#afad5df20f3fea32614ad88b00f5849fc">TEST_SUITE</a>(<span class="stringliteral">"SerializerTests"</span>)</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> {</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> TEST_CASE(<span class="stringliteral">"SerializeAddition"</span>)</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> {</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"addition"</span>);</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({1, 2, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> additionLayer = network->AddAdditionLayer(layerName.c_str());</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</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>  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>(additionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</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>(additionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  additionLayer-><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="l00041"></a><span class="lineno"> 41</span> </div><div class="line"><a name="l00042"></a><span class="lineno"> 42</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="l00043"></a><span class="lineno"> 43</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="l00044"></a><span class="lineno"> 44</span>  additionLayer-><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="l00045"></a><span class="lineno"> 45</span> </div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  std::string serializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(serializedNetwork);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  CHECK(deserializedNetwork);</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>  <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a> verifier(layerName, {tensorInfo, tensorInfo}, {tensorInfo});</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> }</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> </div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> <span class="keywordtype">void</span> SerializeArgMinMaxTest(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> dataType)</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="keyword">const</span> std::string layerName(<span class="stringliteral">"argminmax"</span>);</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({1, 2, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({1, 3}, dataType);</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml">armnn::ArgMinMaxDescriptor</a> descriptor;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</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">armnn::ArgMinMaxFunction::Max</a>;</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  descriptor.m_Axis = 1;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> argMinMaxLayer = network->AddArgMinMaxLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  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>(argMinMaxLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</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>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</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>  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="l00073"></a><span class="lineno"> 73</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="l00074"></a><span class="lineno"> 74</span> </div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> </div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::ArgMinMaxDescriptor></a> verifier(layerName,</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  {inputInfo},</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  {outputInfo},</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  descriptor);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</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> </div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> TEST_CASE(<span class="stringliteral">"SerializeArgMinMaxSigned32"</span>)</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>  SerializeArgMinMaxTest(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>);</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"> 90</span> TEST_CASE(<span class="stringliteral">"SerializeArgMinMaxSigned64"</span>)</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>  SerializeArgMinMaxTest(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::DataType::Signed64</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> </div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> TEST_CASE(<span class="stringliteral">"SerializeBatchNormalization"</span>)</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="keyword">const</span> std::string layerName(<span class="stringliteral">"batchNormalization"</span>);</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> </div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> meanInfo({1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> varianceInfo({1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> betaInfo({1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> gammaInfo({1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">armnn::BatchNormalizationDescriptor</a> descriptor;</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  descriptor.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a> = 0.0010000000475f;</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  descriptor.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> </div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  std::vector<float> meanData({5.0});</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  std::vector<float> varianceData({2.0});</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  std::vector<float> betaData({1.0});</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  std::vector<float> gammaData({0.0});</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> </div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  std::vector<armnn::ConstTensor> constants;</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  constants.emplace_back(<a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(meanInfo, meanData));</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  constants.emplace_back(<a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(varianceInfo, varianceData));</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  constants.emplace_back(<a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(betaInfo, betaData));</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  constants.emplace_back(<a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(gammaInfo, gammaData));</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> </div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> batchNormalizationLayer =</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  network->AddBatchNormalizationLayer(descriptor,</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  constants[0],</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  constants[1],</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  constants[2],</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  constants[3],</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  layerName.c_str());</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> </div><div class="line"><a name="l00132"></a><span class="lineno"> 132</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>(batchNormalizationLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  batchNormalizationLayer-><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="l00134"></a><span class="lineno"> 134</span> </div><div class="line"><a name="l00135"></a><span class="lineno"> 135</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="l00136"></a><span class="lineno"> 136</span>  batchNormalizationLayer-><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="l00137"></a><span class="lineno"> 137</span> </div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <a class="code" href="class_layer_verifier_base_with_descriptor_and_constants.xhtml">LayerVerifierBaseWithDescriptorAndConstants<armnn::BatchNormalizationDescriptor></a> verifier(</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  layerName, {inputInfo}, {outputInfo}, descriptor, constants);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor_and_constants.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</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> </div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> TEST_CASE(<span class="stringliteral">"SerializeBatchToSpaceNd"</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="keyword">const</span> std::string layerName(<span class="stringliteral">"spaceToBatchNd"</span>);</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({4, 1, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({1, 1, 4, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> </div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">armnn::BatchToSpaceNdDescriptor</a> desc;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  desc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  desc.m_BlockShape = {2, 2};</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  desc.m_Crops = {{0, 0}, {0, 0}};</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> batchToSpaceNdLayer = network->AddBatchToSpaceNdLayer(desc, layerName.c_str());</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> </div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  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>(batchToSpaceNdLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  batchToSpaceNdLayer-><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="l00164"></a><span class="lineno"> 164</span> </div><div class="line"><a name="l00165"></a><span class="lineno"> 165</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="l00166"></a><span class="lineno"> 166</span>  batchToSpaceNdLayer-><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="l00167"></a><span class="lineno"> 167</span> </div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> </div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::BatchToSpaceNdDescriptor></a> verifier(layerName,</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  {inputInfo},</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  {outputInfo},</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  desc);</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> }</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> </div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> TEST_CASE(<span class="stringliteral">"SerializeCast"</span>)</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> {</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"cast"</span>);</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> </div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> shape{1, 5, 2, 3};</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> </div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* castLayer = network->AddCastLayer(layerName.c_str());</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> </div><div class="line"><a name="l00192"></a><span class="lineno"> 192</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>(castLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  castLayer-><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="l00194"></a><span class="lineno"> 194</span> </div><div class="line"><a name="l00195"></a><span class="lineno"> 195</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="l00196"></a><span class="lineno"> 196</span>  castLayer-><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="l00197"></a><span class="lineno"> 197</span> </div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> </div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a> verifier(layerName, {inputInfo}, {outputInfo});</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</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> </div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> TEST_CASE(<span class="stringliteral">"SerializeChannelShuffle"</span>)</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> {</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"channelShuffle"</span>);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({1, 9}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({1, 9}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> </div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <a class="code" href="structarmnn_1_1_channel_shuffle_descriptor.xhtml">armnn::ChannelShuffleDescriptor</a> descriptor({3, 1});</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> ChannelShuffleLayer =</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  network->AddChannelShuffleLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  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>(ChannelShuffleLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  ChannelShuffleLayer-><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="l00221"></a><span class="lineno"> 221</span> </div><div class="line"><a name="l00222"></a><span class="lineno"> 222</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="l00223"></a><span class="lineno"> 223</span>  ChannelShuffleLayer-><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="l00224"></a><span class="lineno"> 224</span> </div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> </div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::ChannelShuffleDescriptor></a> verifier(</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  layerName, {inputInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> }</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span> </div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> TEST_CASE(<span class="stringliteral">"SerializeComparison"</span>)</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span> {</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"comparison"</span>);</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> </div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> shape{2, 1, 2, 4};</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span> </div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>);</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml">armnn::ComparisonDescriptor</a> descriptor(<a class="code" href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca19bb0af2c3c530538cb41aff7f235b96">armnn::ComparisonOperation::NotEqual</a>);</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> </div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> comparisonLayer = network->AddComparisonLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</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>  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="l00251"></a><span class="lineno"> 251</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="l00252"></a><span class="lineno"> 252</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="l00253"></a><span class="lineno"> 253</span> </div><div class="line"><a name="l00254"></a><span class="lineno"> 254</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>(inputInfo);</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</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>(inputInfo);</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</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>(outputInfo);</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> </div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::ComparisonDescriptor></a> verifier(layerName,</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  { inputInfo, inputInfo },</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  { outputInfo },</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  descriptor);</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> }</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> </div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span> TEST_CASE(<span class="stringliteral">"SerializeConstant"</span>)</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> {</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keyword">class </span>ConstantLayerVerifier : <span class="keyword">public</span> <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  {</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  ConstantLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants)</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  : <a class="code" href="class_layer_verifier_base.xhtml#a39bdf94af97d9484d02649b749da327c">LayerVerifierBase</a>(layerName, inputInfos, outputInfos)</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  , m_Constants(constants) {}</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span> </div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="keywordtype">void</span> <a class="code" href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(<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="l00281"></a><span class="lineno"> 281</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span> = 0)<span class="keyword"> override</span></div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(descriptor, <span class="keywordtype">id</span>);</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> </div><div class="line"><a name="l00288"></a><span class="lineno"> 288</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="l00289"></a><span class="lineno"> 289</span>  {</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f">armnn::LayerType::Addition</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="keywordflow">default</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>  this-><a class="code" href="class_layer_verifier_base.xhtml#a56e5da77beb8c601e09bf78371b95828">VerifyNameAndConnections</a>(layer, name);</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="keywordflow">for</span> (std::size_t i = 0; i < constants.size(); i++)</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  {</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <a class="code" href="_serializer_test_utils_8cpp.xhtml#a104f74b01c30ad4a17d765309a9731bd">CompareConstTensor</a>(constants[i], m_Constants[i]);</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  }</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>  }</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</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="keyword">private</span>:</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor> m_Constants;</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  };</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>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"constant"</span>);</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span> </div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  std::vector<float> constantData = GenerateRandomData<float>(info.GetNumElements());</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> constTensor(info, constantData);</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>());</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* input = network->AddInputLayer(0);</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* constant = network->AddConstantLayer(constTensor, layerName.c_str());</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* add = network->AddAdditionLayer();</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* output = network->AddOutputLayer(0);</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> </div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  add-><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="l00324"></a><span class="lineno"> 324</span> </div><div class="line"><a name="l00325"></a><span class="lineno"> 325</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="l00326"></a><span class="lineno"> 326</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>(info);</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  add-><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="l00328"></a><span class="lineno"> 328</span> </div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span> </div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  ConstantLayerVerifier verifier(layerName, {}, {info}, {constTensor});</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  deserializedNetwork->ExecuteStrategy(verifier);</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span> }</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span> </div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span> <span class="keyword">using</span> Convolution2dDescriptor = <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a>;</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> <span class="keyword">class </span>Convolution2dLayerVerifier : <span class="keyword">public</span> <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor</a><Convolution2dDescriptor></div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> {</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  Convolution2dLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <span class="keyword">const</span> Convolution2dDescriptor& descriptor)</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  : <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<Convolution2dDescriptor></a>(layerName, inputInfos, outputInfos, descriptor) {}</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> </div><div class="line"><a name="l00346"></a><span class="lineno"> 346</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="l00347"></a><span class="lineno"> 347</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span> = 0)<span class="keyword"> override</span></div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(constants, <span class="keywordtype">id</span>);</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</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="l00354"></a><span class="lineno"> 354</span>  {</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a14d24d90ab4ba2956e92e27890ba4c91acb17869fe51048b5a5c4c6106551a255">armnn::LayerType::Constant</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="keywordflow">default</span>:</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>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <span class="keyword">const</span> Convolution2dDescriptor& layerDescriptor =</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span>Convolution2dDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  CHECK(layerDescriptor.m_BiasEnabled == m_Descriptor.m_BiasEnabled);</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>  }</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  }</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span> };</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span> </div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span> TEST_CASE(<span class="stringliteral">"SerializeConvolution2d"</span>)</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span> {</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"convolution2d"</span>);</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 5, 5, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span> </div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span> </div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span> </div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements());</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> </div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = 2;</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = 2;</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span> </div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <a class="code" href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> convLayer =</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  network->AddConvolution2dLayer(descriptor,</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  weights,</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a>(biases),</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  layerName.c_str());</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <a class="code" href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span> </div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer->GetInputSlot(0));</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  convLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));</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>  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="l00411"></a><span class="lineno"> 411</span>  convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span> </div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  CHECK(deserializedNetwork);</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>  Convolution2dLayerVerifier verifier(layerName, {inputInfo, weightsInfo, biasesInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  deserializedNetwork->ExecuteStrategy(verifier);</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> }</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> </div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span> TEST_CASE(<span class="stringliteral">"SerializeConvolution2dWithPerAxisParams"</span>)</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>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span> </div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"convolution2dWithPerAxis"</span>);</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo ({ 1, 3, 1, 2 }, DataType::QAsymmU8, 0.55f, 128);</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo({ 1, 3, 1, 3 }, DataType::QAsymmU8, 0.75f, 128);</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="keyword">const</span> std::vector<float> quantScales{ 0.75f, 0.65f, 0.85f };</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> quantDimension = 0;</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span> </div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> kernelInfo({ 3, 1, 1, 2 }, DataType::QSymmS8, quantScales, quantDimension, <span class="keyword">true</span>);</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span> </div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keyword">const</span> std::vector<float> biasQuantScales{ 0.25f, 0.50f, 0.75f };</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo({ 3 }, DataType::Signed32, biasQuantScales, quantDimension, <span class="keyword">true</span>);</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>  std::vector<int8_t> kernelData = GenerateRandomData<int8_t>(kernelInfo.GetNumElements());</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(kernelInfo, kernelData);</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  std::vector<int32_t> biasData = GenerateRandomData<int32_t>(biasInfo.GetNumElements());</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasInfo, biasData);</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span> </div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  Convolution2dDescriptor descriptor;</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  descriptor.m_StrideX = 1;</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  descriptor.m_StrideY = 1;</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  descriptor.m_PadLeft = 0;</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  descriptor.m_PadRight = 0;</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  descriptor.m_PadTop = 0;</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  descriptor.m_PadBottom = 0;</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  descriptor.m_BiasEnabled = <span class="keyword">true</span>;</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  descriptor.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span> </div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <a class="code" href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> convLayer =</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  network->AddConvolution2dLayer(descriptor,</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  weights,</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a>(biases),</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  layerName.c_str());</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <a class="code" href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</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>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer->GetInputSlot(0));</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  convLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span> </div><div class="line"><a name="l00465"></a><span class="lineno"> 465</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="l00466"></a><span class="lineno"> 466</span>  convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span> </div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span> </div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  Convolution2dLayerVerifier verifier(layerName, {inputInfo, kernelInfo, biasInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span> </div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  deserializedNetwork->ExecuteStrategy(verifier);</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span> }</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span> </div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span> TEST_CASE(<span class="stringliteral">"SerializeConvolution2dWeightsAndBiasesAsConstantLayers"</span>)</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span> {</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"convolution2d"</span>);</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 5, 5, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span> </div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span> </div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span> </div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements());</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span> </div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = 2;</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = 2;</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span> </div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> weightsLayer = network->AddConstantLayer(weights, <span class="stringliteral">"Weights"</span>);</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> biasesLayer = network->AddConstantLayer(biases, <span class="stringliteral">"Biases"</span>);</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> convLayer = network->AddConvolution2dLayer(descriptor,</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  layerName.c_str());</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span> </div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  weightsLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  biasesLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span> </div><div class="line"><a name="l00516"></a><span class="lineno"> 516</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="l00517"></a><span class="lineno"> 517</span>  weightsLayer-><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>(weightsInfo);</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  biasesLayer-><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>(biasesInfo);</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span> </div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  CHECK(deserializedNetwork);</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>  Convolution2dLayerVerifier verifier(layerName, {inputInfo, weightsInfo, biasesInfo}, {outputInfo}, descriptor);</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>  deserializedNetwork->ExecuteStrategy(verifier);</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span> }</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span> </div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span> TEST_CASE(<span class="stringliteral">"SerializeConvolution2dWeightsAndBiasesAsConstantLayers"</span>)</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span> {</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"convolution2d"</span>);</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 5, 5, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span> </div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span> </div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span> </div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements());</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span> </div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = 2;</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = 2;</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span> </div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> weightsLayer = network->AddConstantLayer(weights, <span class="stringliteral">"Weights"</span>);</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> biasesLayer = network->AddConstantLayer(biases, <span class="stringliteral">"Biases"</span>);</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> convLayer = network->AddConvolution2dLayer(descriptor,</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  layerName.c_str());</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span> </div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  weightsLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  biasesLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span> </div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  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="l00570"></a><span class="lineno"> 570</span>  weightsLayer-><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>(weightsInfo);</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  biasesLayer-><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>(biasesInfo);</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span> </div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span> </div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants {weights, biases};</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <a class="code" href="class_layer_verifier_base_with_descriptor_and_constants.xhtml">LayerVerifierBaseWithDescriptorAndConstants<armnn::Convolution2dDescriptor></a> verifier(</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  layerName, {inputInfo, weightsInfo, biasesInfo}, {outputInfo}, descriptor, constants);</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span> </div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor_and_constants.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</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> </div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span> TEST_CASE(<span class="stringliteral">"SerializeConvolution3d"</span>)</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span> {</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"convolution3d"</span>);</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 5, 5, 5, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 2, 2, 2, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 3, 3, 3, 1, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span> </div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span> </div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements());</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span> </div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml">armnn::Convolution3dDescriptor</a> descriptor;</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 0;</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 0;</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 0;</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 0;</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a83ca447892f460dabaa2f87d3dc3db61">m_PadFront</a> = 0;</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a11d5c25face9b54e90f79ee8bdc1d0fb">m_PadBack</a> = 0;</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = 1;</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = 1;</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a16543bce17aa2e4d6e81c88f74227192">m_DilationZ</a> = 1;</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a5164336f6a1b15be0d434a6bbf7289da">m_StrideZ</a> = 2;</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a4dd0194b114cbf51da5b3a72569863ef">armnn::DataLayout::NDHWC</a>;</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span> </div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> weightsLayer = network->AddConstantLayer(weights, <span class="stringliteral">"Weights"</span>);</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> biasesLayer = network->AddConstantLayer(biases, <span class="stringliteral">"Biases"</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">armnn::IConnectableLayer</a>* <span class="keyword">const</span> convLayer = network->AddConvolution3dLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span> </div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  weightsLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  biasesLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><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>  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="l00628"></a><span class="lineno"> 628</span>  weightsLayer-><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>(weightsInfo);</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  biasesLayer-><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>(biasesInfo);</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span> </div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  CHECK(deserializedNetwork);</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>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::Convolution3dDescriptor></a> verifier(</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  layerName, {inputInfo, weightsInfo, biasesInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span> }</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> TEST_CASE(<span class="stringliteral">"SerializeDepthToSpace"</span>)</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span> {</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"depthToSpace"</span>);</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="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 8, 4, 12 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 16, 8, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</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>  <a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml">armnn::DepthToSpaceDescriptor</a> desc;</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  desc.<a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml#a6c6b8957f1e176867e5fb05b1a1a1486">m_BlockSize</a> = 2;</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  desc.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span> </div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> depthToSpaceLayer = network->AddDepthToSpaceLayer(desc, layerName.c_str());</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span> </div><div class="line"><a name="l00656"></a><span class="lineno"> 656</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="l00657"></a><span class="lineno"> 657</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="l00658"></a><span class="lineno"> 658</span> </div><div class="line"><a name="l00659"></a><span class="lineno"> 659</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="l00660"></a><span class="lineno"> 660</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="l00661"></a><span class="lineno"> 661</span> </div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  CHECK(deserializedNetwork);</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="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::DepthToSpaceDescriptor></a> verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</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> </div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span> TEST_CASE(<span class="stringliteral">"SerializeDepthwiseConvolution2d"</span>)</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span> {</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"depwiseConvolution2d"</span>);</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 5, 5, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 3, 3, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span> </div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 1, 3, 3, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</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>  std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span> </div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  std::vector<int32_t> biasesData = GenerateRandomData<int32_t>(biasesInfo.GetNumElements());</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span> </div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = 2;</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = 2;</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span> </div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> depthwiseConvLayer = network->AddDepthwiseConvolution2dLayer(descriptor,</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  layerName.c_str());</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</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>  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>(depthwiseConvLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  depthwiseConvLayer-><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="l00704"></a><span class="lineno"> 704</span> </div><div class="line"><a name="l00705"></a><span class="lineno"> 705</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="l00706"></a><span class="lineno"> 706</span>  depthwiseConvLayer-><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="l00707"></a><span class="lineno"> 707</span> </div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> weightsLayer = network->AddConstantLayer(weights);</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  weightsLayer-><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>(depthwiseConvLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1u));</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  weightsLayer-><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>(weights.GetInfo());</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span> </div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> biasLayer = network->AddConstantLayer(biases);</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  biasLayer-><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>(depthwiseConvLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2u));</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  biasLayer-><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>(biases.GetInfo());</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span> </div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span> </div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants {weights, biases};</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  <a class="code" href="class_layer_verifier_base_with_descriptor_and_constants.xhtml">LayerVerifierBaseWithDescriptorAndConstants<armnn::DepthwiseConvolution2dDescriptor></a> verifier(</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  layerName, {inputInfo, weightsInfo, biasesInfo}, {outputInfo}, descriptor, constants);</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor_and_constants.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</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> TEST_CASE(<span class="stringliteral">"SerializeDepthwiseConvolution2dWithPerAxisParams"</span>)</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span> {</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span> </div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"depwiseConvolution2dWithPerAxis"</span>);</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo ({ 1, 3, 3, 2 }, DataType::QAsymmU8, 0.55f, 128);</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo({ 1, 2, 2, 4 }, DataType::QAsymmU8, 0.75f, 128);</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span> </div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  <span class="keyword">const</span> std::vector<float> quantScales{ 0.75f, 0.80f, 0.90f, 0.95f };</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> quantDimension = 0;</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> kernelInfo({ 2, 2, 2, 2 }, DataType::QSymmS8, quantScales, quantDimension, <span class="keyword">true</span>);</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span> </div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  <span class="keyword">const</span> std::vector<float> biasQuantScales{ 0.25f, 0.35f, 0.45f, 0.55f };</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasQuantDimension = 0;</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo({ 4 }, DataType::Signed32, biasQuantScales, biasQuantDimension, <span class="keyword">true</span>);</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>  std::vector<int8_t> kernelData = GenerateRandomData<int8_t>(kernelInfo.GetNumElements());</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(kernelInfo, kernelData);</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  std::vector<int32_t> biasData = GenerateRandomData<int32_t>(biasInfo.GetNumElements());</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasInfo, biasData);</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span> </div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 0;</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 0;</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 0;</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 0;</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = 1;</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = 1;</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span> </div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> depthwiseConvLayer = network->AddDepthwiseConvolution2dLayer(descriptor,</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  layerName.c_str());</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</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>  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>(depthwiseConvLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  depthwiseConvLayer-><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="l00766"></a><span class="lineno"> 766</span> </div><div class="line"><a name="l00767"></a><span class="lineno"> 767</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="l00768"></a><span class="lineno"> 768</span>  depthwiseConvLayer-><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="l00769"></a><span class="lineno"> 769</span> </div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> weightsLayer = network->AddConstantLayer(weights);</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  weightsLayer-><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>(depthwiseConvLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1u));</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  weightsLayer-><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>(weights.GetInfo());</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> biasLayer = network->AddConstantLayer(biases);</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  biasLayer-><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>(depthwiseConvLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2u));</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  biasLayer-><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>(biases.GetInfo());</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span> </div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span> </div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants {weights, biases};</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  <a class="code" href="class_layer_verifier_base_with_descriptor_and_constants.xhtml">LayerVerifierBaseWithDescriptorAndConstants<armnn::DepthwiseConvolution2dDescriptor></a> verifier(</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  layerName, {inputInfo, kernelInfo, biasInfo}, {outputInfo}, descriptor, constants);</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor_and_constants.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span> }</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> TEST_CASE(<span class="stringliteral">"SerializeDepthwiseConvolution2dWeightsAndBiasesAsConstantLayers"</span>)</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span> {</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"depthwiseConvolution2d"</span>);</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 5, 5, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span> </div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span> </div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span> </div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements());</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span> </div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = 2;</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = 2;</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span> </div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> weightsLayer = network->AddConstantLayer(weights, <span class="stringliteral">"Weights"</span>);</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> biasesLayer = network->AddConstantLayer(biases, <span class="stringliteral">"Biases"</span>);</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> convLayer = network->AddDepthwiseConvolution2dLayer(descriptor,</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  layerName.c_str());</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span> </div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  weightsLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  biasesLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span> </div><div class="line"><a name="l00827"></a><span class="lineno"> 827</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="l00828"></a><span class="lineno"> 828</span>  weightsLayer-><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>(weightsInfo);</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  biasesLayer-><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>(biasesInfo);</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span> </div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  CHECK(deserializedNetwork);</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>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants {weights, biases};</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  <a class="code" href="class_layer_verifier_base_with_descriptor_and_constants.xhtml">LayerVerifierBaseWithDescriptorAndConstants<armnn::DepthwiseConvolution2dDescriptor></a> verifier(</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  layerName, {inputInfo, weightsInfo, biasesInfo}, {outputInfo}, descriptor, constants);</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span> </div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor_and_constants.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</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> </div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span> TEST_CASE(<span class="stringliteral">"SerializeDequantize"</span>)</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span> {</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"dequantize"</span>);</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({ 1, 5, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>, 0.5f, 1);</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 5, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> dequantizeLayer = network->AddDequantizeLayer(layerName.c_str());</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</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>  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>(dequantizeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  dequantizeLayer-><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="l00855"></a><span class="lineno"> 855</span> </div><div class="line"><a name="l00856"></a><span class="lineno"> 856</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="l00857"></a><span class="lineno"> 857</span>  dequantizeLayer-><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="l00858"></a><span class="lineno"> 858</span> </div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  CHECK(deserializedNetwork);</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>  <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a> verifier(layerName, {inputInfo}, {outputInfo});</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span> }</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span> </div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span> TEST_CASE(<span class="stringliteral">"SerializeDeserializeDetectionPostProcess"</span>)</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span> {</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"detectionPostProcess"</span>);</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span> </div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo> inputInfos({</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 6, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>),</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 6, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>)</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> </div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo> outputInfos({</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 3, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>),</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>),</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>),</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>)</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  });</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span> </div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  <a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml">armnn::DetectionPostProcessDescriptor</a> descriptor;</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7ed9bc7c26df67d274d5dd4cd83adf0f">m_UseRegularNms</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae72089bcab60ac175557f4241b16a014">m_MaxDetections</a> = 3;</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a9ae2c9796692ebeafe19a4d3f09c8ea8">m_MaxClassesPerDetection</a> = 1;</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7e2f87544b8bc7e497e1dec8d3ca4055">m_DetectionsPerClass</a> =1;</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a4392dd6b4862cc9cf95ae8f1001ba592">m_NmsScoreThreshold</a> = 0.0;</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a53c8a7f33a40e1e240256bcfcf41b101">m_NmsIouThreshold</a> = 0.5;</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a3a04b0ccee4bb2f21721ee5045e83df4">m_NumClasses</a> = 2;</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7a2156ec7d9c012ce00bbcc6afcb9028">m_ScaleY</a> = 10.0;</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae64523937ea910030ad66fee6fddd51f">m_ScaleX</a> = 10.0;</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#aa61510cbd529870182e918ac6e8b9d72">m_ScaleH</a> = 5.0;</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ab509802c659de19929f18bad14a35c58">m_ScaleW</a> = 5.0;</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">armnn::TensorInfo</a> anchorsInfo({ 6, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  <span class="keyword">const</span> std::vector<float> anchorsData({</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  0.5f, 0.5f, 1.0f, 1.0f,</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  0.5f, 0.5f, 1.0f, 1.0f,</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  0.5f, 0.5f, 1.0f, 1.0f,</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  0.5f, 10.5f, 1.0f, 1.0f,</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  0.5f, 10.5f, 1.0f, 1.0f,</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  0.5f, 100.5f, 1.0f, 1.0f</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  });</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> anchors(anchorsInfo, anchorsData);</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span> </div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> detectionLayer =</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  network->AddDetectionPostProcessLayer(descriptor, anchors, layerName.c_str());</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span> </div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < 2; i++)</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  {</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(static_cast<int>(i));</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</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>(detectionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(i));</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</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>(inputInfos[i]);</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> </div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < 4; i++)</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_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(static_cast<int>(i));</div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  detectionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(i).<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="l00921"></a><span class="lineno"> 921</span>  detectionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(i).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfos[i]);</div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  }</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span> </div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  CHECK(deserializedNetwork);</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>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants {anchors};</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  <a class="code" href="class_layer_verifier_base_with_descriptor_and_constants.xhtml">LayerVerifierBaseWithDescriptorAndConstants<armnn::DetectionPostProcessDescriptor></a> verifier(</div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  layerName, inputInfos, outputInfos, descriptor, constants);</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  deserializedNetwork->ExecuteStrategy(verifier);</div><div class="line"><a name="l00931"></a><span class="lineno"> 931</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> TEST_CASE(<span class="stringliteral">"SerializeDivision"</span>)</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span> {</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"division"</span>);</div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({ 1, 5, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span> </div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> divisionLayer = network->AddDivisionLayer(layerName.c_str());</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span> </div><div class="line"><a name="l00944"></a><span class="lineno"> 944</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>(divisionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</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>(divisionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  divisionLayer-><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="l00947"></a><span class="lineno"> 947</span> </div><div class="line"><a name="l00948"></a><span class="lineno"> 948</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>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</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>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  divisionLayer-><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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span> </div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span> </div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a> verifier(layerName, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, {info});</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span> }</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span> </div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span> TEST_CASE(<span class="stringliteral">"SerializeDeserializeComparisonEqual"</span>)</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="keyword">const</span> std::string layerName(<span class="stringliteral">"EqualLayer"</span>);</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo1 = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({2, 1, 2, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo2 = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({2, 1, 2, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({2, 1, 2, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>);</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span> </div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(0);</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer2 = network->AddInputLayer(1);</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>  <a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml">armnn::ComparisonDescriptor</a> equalDescriptor(<a class="code" href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58caf5f286e73bda105e538310b3190f75c5">armnn::ComparisonOperation::Equal</a>);</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> equalLayer = network->AddComparisonLayer(equalDescriptor, layerName.c_str());</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span> </div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  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>(equalLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</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>(inputTensorInfo1);</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  inputLayer2-><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>(equalLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  inputLayer2-><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>(inputTensorInfo2);</div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  equalLayer-><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="l00978"></a><span class="lineno"> 978</span>  equalLayer-><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>(outputTensorInfo);</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span> </div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span> </div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a> verifier(layerName, {inputTensorInfo1, inputTensorInfo2}, {outputTensorInfo});</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span> }</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="keywordtype">void</span> SerializeElementwiseUnaryTest(<a class="code" href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8">armnn::UnaryOperation</a> unaryOperation)</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span> {</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  <span class="keyword">auto</span> layerName = <a class="code" href="namespacearmnn.xhtml#a6dac966f265381903c8ef4f392becced">GetUnaryOperationAsCString</a>(unaryOperation);</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span> </div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> shape{2, 1, 2, 2};</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span> </div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span>  <a class="code" href="structarmnn_1_1_elementwise_unary_descriptor.xhtml">armnn::ElementwiseUnaryDescriptor</a> descriptor(unaryOperation);</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="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> elementwiseUnaryLayer =</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  network->AddElementwiseUnaryLayer(descriptor, layerName);</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span> </div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</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>(elementwiseUnaryLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  elementwiseUnaryLayer-><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="l01006"></a><span class="lineno"> 1006</span> </div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</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="l01008"></a><span class="lineno"> 1008</span>  elementwiseUnaryLayer-><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="l01009"></a><span class="lineno"> 1009</span> </div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span> </div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span> </div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::ElementwiseUnaryDescriptor></a></div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  verifier(layerName, { inputInfo }, { outputInfo }, descriptor);</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span> </div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span> }</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span> </div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span> TEST_CASE(<span class="stringliteral">"SerializeElementwiseUnary"</span>)</div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span> {</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  <span class="keyword">using</span> op = <a class="code" href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8">armnn::UnaryOperation</a>;</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  std::initializer_list<op> allUnaryOperations = {op::Abs, op::Exp, op::Sqrt, op::Rsqrt, op::Neg,</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  op::LogicalNot, op::Log, op::Sin};</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span> </div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> unaryOperation : allUnaryOperations)</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  {</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  SerializeElementwiseUnaryTest(unaryOperation);</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  }</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"> 1032</span> TEST_CASE(<span class="stringliteral">"SerializeFill"</span>)</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span> {</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"fill"</span>);</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>);</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({1, 3, 3, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span> </div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>  <a class="code" href="structarmnn_1_1_fill_descriptor.xhtml">armnn::FillDescriptor</a> descriptor(1.0f);</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span> </div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> fillLayer = network->AddFillLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</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>  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="l01046"></a><span class="lineno"> 1046</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="l01047"></a><span class="lineno"> 1047</span> </div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</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="l01049"></a><span class="lineno"> 1049</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>(outputInfo);</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span> </div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>  CHECK(deserializedNetwork);</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>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::FillDescriptor></a> verifier(layerName, {inputInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span> </div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span> }</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span> </div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span> TEST_CASE(<span class="stringliteral">"SerializeFloor"</span>)</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span> {</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"floor"</span>);</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({4,4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span> </div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> floorLayer = network->AddFloorLayer(layerName.c_str());</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span> </div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>  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>(floorLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>  floorLayer-><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="l01071"></a><span class="lineno"> 1071</span> </div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>  floorLayer-><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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</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">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span> </div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>  <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a> verifier(layerName, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>});</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span> }</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span> </div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span> <span class="keyword">using</span> <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> = <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a>;</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span> <span class="keyword">class </span>FullyConnectedLayerVerifier : <span class="keyword">public</span> <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor</a><FullyConnectedDescriptor></div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span> {</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span> <span class="keyword">public</span>:</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  FullyConnectedLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a>& descriptor)</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>  : <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<FullyConnectedDescriptor></a>(layerName, inputInfos, outputInfos, descriptor) {}</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span> </div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</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="l01093"></a><span class="lineno"> 1093</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span> = 0)<span class="keyword"> override</span></div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span> <span class="keyword"> </span>{</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(constants, <span class="keywordtype">id</span>);</div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</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="l01100"></a><span class="lineno"> 1100</span>  {</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a14d24d90ab4ba2956e92e27890ba4c91acb17869fe51048b5a5c4c6106551a255">armnn::LayerType::Constant</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>  {</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a>& layerDescriptor =</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  CHECK(layerDescriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a2d3dcfc10f90adedc995b64211dab6e9">m_ConstantWeights</a> == m_Descriptor.m_ConstantWeights);</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  CHECK(layerDescriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> == m_Descriptor.m_BiasEnabled);</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>  CHECK(layerDescriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> == m_Descriptor.m_TransposeWeightMatrix);</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</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"> 1115</span> };</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> TEST_CASE(<span class="stringliteral">"SerializeFullyConnected"</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>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"fullyConnected"</span>);</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 2, 5, 1, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span> </div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 5, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>  std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());</div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>  std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements());</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</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_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a> descriptor;</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a2d3dcfc10f90adedc995b64211dab6e9">m_ConstantWeights</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span> </div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> weightsInputLayer = network->AddInputLayer(1);</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> biasInputLayer = network->AddInputLayer(2);</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> fullyConnectedLayer =</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  network->AddFullyConnectedLayer(descriptor,</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  layerName.c_str());</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span> </div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</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>(fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  weightsInputLayer-><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>(fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  biasInputLayer-><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>(fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  fullyConnectedLayer-><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="l01148"></a><span class="lineno"> 1148</span> </div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</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="l01150"></a><span class="lineno"> 1150</span>  weightsInputLayer-><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>(weightsInfo);</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>  biasInputLayer-><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>(biasesInfo);</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  fullyConnectedLayer-><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="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#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span> </div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  FullyConnectedLayerVerifier verifier(layerName, {inputInfo, weightsInfo, biasesInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  deserializedNetwork->ExecuteStrategy(verifier);</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</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> TEST_CASE(<span class="stringliteral">"SerializeFullyConnectedWeightsAndBiasesAsInputs"</span>)</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span> {</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"fullyConnected_weights_as_inputs"</span>);</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 2, 5, 1, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span> </div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 5, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span> </div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a> weights = <a class="code" href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a>();</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a> bias = <a class="code" href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a>();</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span> </div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a> descriptor;</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a2d3dcfc10f90adedc995b64211dab6e9">m_ConstantWeights</a> = <span class="keyword">false</span>;</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> weightsInputLayer = network->AddInputLayer(1);</div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> biasInputLayer = network->AddInputLayer(2);</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> fullyConnectedLayer =</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  network->AddFullyConnectedLayer(descriptor,</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  layerName.c_str());</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span> </div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</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>(fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  weightsInputLayer-><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>(fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>  biasInputLayer-><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>(fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  fullyConnectedLayer-><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="l01191"></a><span class="lineno"> 1191</span> </div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</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="l01193"></a><span class="lineno"> 1193</span>  weightsInputLayer-><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>(weightsInfo);</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  biasInputLayer-><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>(biasesInfo);</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  fullyConnectedLayer-><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="l01196"></a><span class="lineno"> 1196</span> </div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span> </div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor> constants {};</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  <a class="code" href="class_layer_verifier_base_with_descriptor_and_constants.xhtml">LayerVerifierBaseWithDescriptorAndConstants<armnn::FullyConnectedDescriptor></a> verifier(</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  layerName, {inputInfo, weightsInfo, biasesInfo}, {outputInfo}, descriptor, constants);</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor_and_constants.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span> }</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span> </div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span> TEST_CASE(<span class="stringliteral">"SerializeFullyConnectedWeightsAndBiasesAsConstantLayers"</span>)</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span> {</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"fullyConnected_weights_as_inputs"</span>);</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 2, 5, 1, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 5, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span> </div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>  std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements());</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span> </div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a> descriptor;</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a2d3dcfc10f90adedc995b64211dab6e9">m_ConstantWeights</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span> </div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> weightsLayer = network->AddConstantLayer(weights, <span class="stringliteral">"Weights"</span>);</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> biasesLayer = network->AddConstantLayer(biases, <span class="stringliteral">"Biases"</span>);</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor,layerName.c_str());</div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span> </div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</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>(fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  weightsLayer-><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>(fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  biasesLayer-><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>(fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  fullyConnectedLayer-><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="l01236"></a><span class="lineno"> 1236</span> </div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</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="l01238"></a><span class="lineno"> 1238</span>  weightsLayer-><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>(weightsInfo);</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>  biasesLayer-><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>(biasesInfo);</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  fullyConnectedLayer-><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="l01241"></a><span class="lineno"> 1241</span> </div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span> </div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>  FullyConnectedLayerVerifier verifier(layerName, {inputInfo, weightsInfo, biasesInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>  deserializedNetwork->ExecuteStrategy(verifier);</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span> }</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> TEST_CASE(<span class="stringliteral">"SerializeGather"</span>)</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span> {</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  <span class="keyword">using</span> <a class="code" href="structarmnn_1_1_gather_descriptor.xhtml">GatherDescriptor</a> = <a class="code" href="structarmnn_1_1_gather_descriptor.xhtml">armnn::GatherDescriptor</a>;</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>  <span class="keyword">class </span>GatherLayerVerifier : <span class="keyword">public</span> <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor</a><GatherDescriptor></div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>  {</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>  GatherLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_gather_descriptor.xhtml">GatherDescriptor</a>& descriptor)</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>  : <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<GatherDescriptor></a>(layerName, inputInfos, outputInfos, descriptor) {}</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span> </div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</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="l01262"></a><span class="lineno"> 1262</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span> = 0)<span class="keyword"> override</span></div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span> <span class="keyword"> </span>{</div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(constants, <span class="keywordtype">id</span>);</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</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="l01269"></a><span class="lineno"> 1269</span>  {</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a14d24d90ab4ba2956e92e27890ba4c91acb17869fe51048b5a5c4c6106551a255">armnn::LayerType::Constant</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>  {</div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_gather_descriptor.xhtml">GatherDescriptor</a>& layerDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_gather_descriptor.xhtml">GatherDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>  CHECK(layerDescriptor.<a class="code" href="structarmnn_1_1_gather_descriptor.xhtml#a35d11c7d509d1adbae1ae01c58394a7f">m_Axis</a> == m_Descriptor.m_Axis);</div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  }</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>  }</div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>  }</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"> 1283</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"gather"</span>);</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> paramsInfo({ 8 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>);</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>);</div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> indicesInfo({ 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>  <a class="code" href="structarmnn_1_1_gather_descriptor.xhtml">GatherDescriptor</a> descriptor;</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>  descriptor.<a class="code" href="structarmnn_1_1_gather_descriptor.xhtml#a35d11c7d509d1adbae1ae01c58394a7f">m_Axis</a> = 1;</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>  paramsInfo.SetQuantizationScale(1.0f);</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>  paramsInfo.SetQuantizationOffset(0);</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>  outputInfo.SetQuantizationScale(1.0f);</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>  outputInfo.SetQuantizationOffset(0);</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span> </div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  <span class="keyword">const</span> std::vector<int32_t>& indicesData = {7, 6, 5};</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *<span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *<span class="keyword">const</span> constantLayer =</div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>  network->AddConstantLayer(<a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(indicesInfo, indicesData));</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *<span class="keyword">const</span> gatherLayer = network->AddGatherLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *<span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span> </div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</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>(gatherLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>  constantLayer-><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>(gatherLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>  gatherLayer-><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="l01307"></a><span class="lineno"> 1307</span> </div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</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>(paramsInfo);</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>  constantLayer-><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>(indicesInfo);</div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>  gatherLayer-><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="l01311"></a><span class="lineno"> 1311</span> </div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span> </div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>  GatherLayerVerifier verifier(layerName, {paramsInfo, indicesInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>  deserializedNetwork->ExecuteStrategy(verifier);</div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span> }</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> TEST_CASE(<span class="stringliteral">"SerializeGatherNd"</span>)</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span> {</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>  <span class="keyword">class </span>GatherNdLayerVerifier : <span class="keyword">public</span> <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a></div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  {</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>  GatherNdLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos)</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  : <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a>(layerName, inputInfos, outputInfos) {}</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="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="l01330"></a><span class="lineno"> 1330</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span> = 0)<span class="keyword"> override</span></div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span> <span class="keyword"> </span>{</div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(constants, <span class="keywordtype">id</span>);</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</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="l01337"></a><span class="lineno"> 1337</span>  {</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a>:</div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>:</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a14d24d90ab4ba2956e92e27890ba4c91acb17869fe51048b5a5c4c6106551a255">armnn::LayerType::Constant</a>:</div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>  {</div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>  }</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>  }</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>  };</div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span> </div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"gatherNd"</span>);</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> paramsInfo({ 6, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>);</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 3, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>);</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> indicesInfo({ 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>, 0.0f, 0, <span class="keyword">true</span>);</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>  paramsInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(1.0f);</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>  paramsInfo.SetQuantizationOffset(0);</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>  outputInfo.SetQuantizationScale(1.0f);</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>  outputInfo.SetQuantizationOffset(0);</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> std::vector<int32_t>& indicesData = {5, 1, 0};</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span> </div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *<span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *<span class="keyword">const</span> constantLayer =</div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>  network->AddConstantLayer(<a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(indicesInfo, indicesData));</div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *<span class="keyword">const</span> gatherNdLayer = network->AddGatherNdLayer(layerName.c_str());</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *<span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span> </div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</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>(gatherNdLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>  constantLayer-><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>(gatherNdLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span>  gatherNdLayer-><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="l01372"></a><span class="lineno"> 1372</span> </div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</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>(paramsInfo);</div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span>  constantLayer-><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>(indicesInfo);</div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>  gatherNdLayer-><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="l01376"></a><span class="lineno"> 1376</span> </div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span> </div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>  GatherNdLayerVerifier verifier(layerName, {paramsInfo, indicesInfo}, {outputInfo});</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span>  deserializedNetwork->ExecuteStrategy(verifier);</div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span> }</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> TEST_CASE(<span class="stringliteral">"SerializeComparisonGreater"</span>)</div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span> {</div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"greater"</span>);</div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span> </div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> shape{2, 1, 2, 4};</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="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>);</div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span> </div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>  <a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml">armnn::ComparisonDescriptor</a> greaterDescriptor(<a class="code" href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca8768a6821cd735aea4f5b0df88c1fc6a">armnn::ComparisonOperation::Greater</a>);</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> equalLayer = network->AddComparisonLayer(greaterDescriptor, layerName.c_str());</div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</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>  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>(equalLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</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>(equalLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>  equalLayer-><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="l01403"></a><span class="lineno"> 1403</span> </div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</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>(inputInfo);</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</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>(inputInfo);</div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>  equalLayer-><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="l01407"></a><span class="lineno"> 1407</span> </div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span> </div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>  <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a> verifier(layerName, { inputInfo, inputInfo }, { outputInfo });</div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span> }</div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span> </div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span> </div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span> TEST_CASE(<span class="stringliteral">"SerializeInstanceNormalization"</span>)</div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span> {</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"instanceNormalization"</span>);</div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({ 1, 2, 1, 5 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span> </div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>  <a class="code" href="structarmnn_1_1_instance_normalization_descriptor.xhtml">armnn::InstanceNormalizationDescriptor</a> descriptor;</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>  descriptor.<a class="code" href="structarmnn_1_1_instance_normalization_descriptor.xhtml#a5e078fd505aef7bccaa05c8058e096cc">m_Gamma</a> = 1.1f;</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>  descriptor.m_Beta = 0.1f;</div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>  descriptor.m_Eps = 0.0001f;</div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>  descriptor.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span> </div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> instanceNormLayer =</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>  network->AddInstanceNormalizationLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span> </div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</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="l01434"></a><span class="lineno"> 1434</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="l01435"></a><span class="lineno"> 1435</span> </div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</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>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</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="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>  CHECK(deserializedNetwork);</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>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::InstanceNormalizationDescriptor></a> verifier(</div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>  layerName, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, descriptor);</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span> }</div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span> </div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span> TEST_CASE(<span class="stringliteral">"SerializeL2Normalization"</span>)</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span> {</div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span>  <span class="keyword">const</span> std::string l2NormLayerName(<span class="stringliteral">"l2Normalization"</span>);</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({1, 2, 1, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span> </div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>  <a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml">armnn::L2NormalizationDescriptor</a> desc;</div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span>  desc.<a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>;</div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span>  desc.m_Eps = 0.0001f;</div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span> </div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> l2NormLayer = network->AddL2NormalizationLayer(desc, l2NormLayerName.c_str());</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</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>  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>(l2NormLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span>  l2NormLayer-><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="l01463"></a><span class="lineno"> 1463</span> </div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</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>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span>  l2NormLayer-><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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span> </div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span> </div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::L2NormalizationDescriptor></a> verifier(</div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span>  l2NormLayerName, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, desc);</div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span> }</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span> </div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span> TEST_CASE(<span class="stringliteral">"EnsureL2NormalizationBackwardCompatibility"</span>)</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>  <span class="comment">// The hex data below is a flat buffer containing a simple network with one input</span></div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>  <span class="comment">// a L2Normalization layer and an output layer with dimensions as per the tensor infos below.</span></div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>  <span class="comment">//</span></div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>  <span class="comment">// This test verifies that we can still read back these old style</span></div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</span>  <span class="comment">// models without the normalization epsilon value.</span></div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span>  <span class="keyword">const</span> std::vector<uint8_t> l2NormalizationModel =</div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>  {</div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span>  0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span>  0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01486"></a><span class="lineno"> 1486</span>  0x3C, 0x01, 0x00, 0x00, 0x74, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span>  0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0xE8, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B,</div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>  0x04, 0x00, 0x00, 0x00, 0xD6, 0xFE, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span>  0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>  0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>  0x4C, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x44, 0xFF, 0xFF, 0xFF, 0x00, 0x00,</div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span>  0x00, 0x20, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span>  0x20, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span>  0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>  0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x1F, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x20, 0x00,</div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>  0x00, 0x00, 0x0F, 0x00, 0x00, 0x00, 0x6C, 0x32, 0x4E, 0x6F, 0x72, 0x6D, 0x61, 0x6C, 0x69, 0x7A, 0x61, 0x74,</div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>  0x69, 0x6F, 0x6E, 0x00, 0x01, 0x00, 0x00, 0x00, 0x48, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00,</div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span>  0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span>  0x52, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>  0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span>  0x08, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span>  0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,</div><div class="line"><a name="l01504"></a><span class="lineno"> 1504</span>  0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>  0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01506"></a><span class="lineno"> 1506</span>  0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00,</div><div class="line"><a name="l01507"></a><span class="lineno"> 1507</span>  0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span>  0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>  0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>  0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>  0x05, 0x00, 0x00, 0x00, 0x00</div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span>  };</div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span> </div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork =</div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</span>  <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(std::string(l2NormalizationModel.begin(), l2NormalizationModel.end()));</div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span> </div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"l2Normalization"</span>);</div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 2, 1, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span> </div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span>  <a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml">armnn::L2NormalizationDescriptor</a> desc;</div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>  desc.<a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>;</div><div class="line"><a name="l01524"></a><span class="lineno"> 1524</span>  <span class="comment">// Since this variable does not exist in the l2NormalizationModel dump, the default value will be loaded</span></div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span>  desc.m_Eps = 1e-12f;</div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span> </div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::L2NormalizationDescriptor></a> verifier(</div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>  layerName, {inputInfo}, {inputInfo}, desc);</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span> }</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span> </div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span> TEST_CASE(<span class="stringliteral">"SerializeLogicalBinary"</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="keyword">const</span> std::string layerName(<span class="stringliteral">"logicalBinaryAnd"</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> shape{2, 1, 2, 2};</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>);</div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>);</div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span> </div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span>  <a class="code" href="structarmnn_1_1_logical_binary_descriptor.xhtml">armnn::LogicalBinaryDescriptor</a> descriptor(<a class="code" href="namespacearmnn.xhtml#a2da4db0140d1a6dc69c9c82e9ef5379ea103aa83df42877d5f9baeafdbf620b55">armnn::LogicalBinaryOperation::LogicalAnd</a>);</div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span> </div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> logicalBinaryLayer = network->AddLogicalBinaryLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</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>  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>(logicalBinaryLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</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>(logicalBinaryLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>  logicalBinaryLayer-><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="l01552"></a><span class="lineno"> 1552</span> </div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</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>(inputInfo);</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</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>(inputInfo);</div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>  logicalBinaryLayer-><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="l01556"></a><span class="lineno"> 1556</span> </div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span> </div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::LogicalBinaryDescriptor></a> verifier(</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>  layerName, { inputInfo, inputInfo }, { outputInfo }, descriptor);</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span> }</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span> </div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span> TEST_CASE(<span class="stringliteral">"SerializeLogSoftmax"</span>)</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span> {</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"log_softmax"</span>);</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({1, 10}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span> </div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>  <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::LogSoftmaxDescriptor</a> descriptor;</div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span>  descriptor.<a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = 1.0f;</div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span>  descriptor.m_Axis = -1;</div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span> </div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> logSoftmaxLayer = network->AddLogSoftmaxLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</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>  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="l01580"></a><span class="lineno"> 1580</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="l01581"></a><span class="lineno"> 1581</span> </div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</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>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span> </div><div class="line"><a name="l01585"></a><span class="lineno"> 1585</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span> </div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::LogSoftmaxDescriptor></a> verifier(layerName, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, descriptor);</div><div class="line"><a name="l01589"></a><span class="lineno"> 1589</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</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> </div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span> TEST_CASE(<span class="stringliteral">"SerializeMaximum"</span>)</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span> {</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"maximum"</span>);</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({ 1, 2, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span> </div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01598"></a><span class="lineno"> 1598</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l01599"></a><span class="lineno"> 1599</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> maximumLayer = network->AddMaximumLayer(layerName.c_str());</div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01602"></a><span class="lineno"> 1602</span> </div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</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>(maximumLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</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>(maximumLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span>  maximumLayer-><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="l01606"></a><span class="lineno"> 1606</span> </div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</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>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</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>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span>  maximumLayer-><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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span> </div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01612"></a><span class="lineno"> 1612</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l01613"></a><span class="lineno"> 1613</span> </div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>  <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a> verifier(layerName, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, {info});</div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</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"> 1618</span> TEST_CASE(<span class="stringliteral">"SerializeMean"</span>)</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>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"mean"</span>);</div><div class="line"><a name="l01621"></a><span class="lineno"> 1621</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({1, 1, 3, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</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_info.xhtml">armnn::TensorInfo</a> outputInfo({1, 1, 1, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span> </div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span>  <a class="code" href="structarmnn_1_1_mean_descriptor.xhtml">armnn::MeanDescriptor</a> descriptor;</div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>  descriptor.<a class="code" href="structarmnn_1_1_mean_descriptor.xhtml#a1f0d67b087c491248bd1cde3ff995a95">m_Axis</a> = { 2 };</div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>  descriptor.m_KeepDims = <span class="keyword">true</span>;</div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span> </div><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01629"></a><span class="lineno"> 1629</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> meanLayer = network->AddMeanLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span> </div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</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>(meanLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span>  meanLayer-><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="l01635"></a><span class="lineno"> 1635</span> </div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</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="l01637"></a><span class="lineno"> 1637</span>  meanLayer-><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="l01638"></a><span class="lineno"> 1638</span> </div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span>  CHECK(deserializedNetwork);</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="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::MeanDescriptor></a> verifier(layerName, {inputInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span> }</div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span> </div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span> TEST_CASE(<span class="stringliteral">"SerializeMerge"</span>)</div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span> {</div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"merge"</span>);</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({ 1, 2, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span> </div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> mergeLayer = network->AddMergeLayer(layerName.c_str());</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</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>  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>(mergeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</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>(mergeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>  mergeLayer-><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="l01660"></a><span class="lineno"> 1660</span> </div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</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>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</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>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</span>  mergeLayer-><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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span> </div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span>  CHECK(deserializedNetwork);</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="class_layer_verifier_base.xhtml">LayerVerifierBase</a> verifier(layerName, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, {info});</div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span> }</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> <span class="keyword">class </span>MergerLayerVerifier : <span class="keyword">public</span> <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor</a><armnn::OriginsDescriptor></div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span> {</div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span> <span class="keyword">public</span>:</div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>  MergerLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a>& descriptor)</div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span>  : <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::OriginsDescriptor></a>(layerName, inputInfos, outputInfos, descriptor) {}</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span> </div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</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="l01682"></a><span class="lineno"> 1682</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span> = 0)<span class="keyword"> override</span></div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span> <span class="keyword"> </span>{</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(descriptor, constants, <span class="keywordtype">id</span>);</div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</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="l01689"></a><span class="lineno"> 1689</span>  {</div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a68be4837f6c739877233e527a996dd00">armnn::LayerType::Merge</a>:</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span>  {</div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">"MergerLayer should have translated to ConcatLayer"</span>);</div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>  }</div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae20f0f2826a6549809f050b86274567f">armnn::LayerType::Concat</a>:</div><div class="line"><a name="l01698"></a><span class="lineno"> 1698</span>  {</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">armnn::MergerDescriptor</a>& layerDescriptor =</div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">armnn::MergerDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span>  VerifyDescriptor(layerDescriptor);</div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span>  }</div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span>  {</div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">"Unexpected layer type in Merge test model"</span>);</div><div class="line"><a name="l01708"></a><span class="lineno"> 1708</span>  }</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> };</div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span> </div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span> TEST_CASE(<span class="stringliteral">"EnsureMergerLayerBackwardCompatibility"</span>)</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span> {</div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span>  <span class="comment">// The hex data below is a flat buffer containing a simple network with two inputs</span></div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>  <span class="comment">// a merger layer (now deprecated) and an output layer with dimensions as per the tensor infos below.</span></div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>  <span class="comment">//</span></div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span>  <span class="comment">// This test verifies that we can still read back these old style</span></div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>  <span class="comment">// models replacing the MergerLayers with ConcatLayers with the same parameters.</span></div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>  <span class="keyword">const</span> std::vector<uint8_t> mergerModel =</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>  {</div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>  0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>  0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>  0x38, 0x02, 0x00, 0x00, 0x8C, 0x01, 0x00, 0x00, 0x70, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x02, 0x00,</div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>  0xF4, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x92, 0xFE, 0xFF, 0xFF, 0x04, 0x00,</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>  0x00, 0x00, 0x9A, 0xFE, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x7E, 0xFE, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>  0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>  0xF8, 0xFE, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x48, 0xFE, 0xFF, 0xFF, 0x00, 0x00,</div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span>  0x00, 0x1F, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span>  0x68, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>  0x0C, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>  0x02, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x22, 0xFF, 0xFF, 0xFF, 0x04, 0x00,</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span>  0x00, 0x00, 0x00, 0x00, 0x3E, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x36, 0xFF, 0xFF, 0xFF,</div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span>  0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x1E, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1C, 0x00,</div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span>  0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x6D, 0x65, 0x72, 0x67, 0x65, 0x72, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span>  0x5C, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x34, 0xFF,</div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>  0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x92, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>  0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00,</div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>  0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x08, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>  0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01745"></a><span class="lineno"> 1745</span>  0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,</div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span>  0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>  0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,</div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span>  0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>  0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>  0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00,</div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span>  0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span>  0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>  0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00,</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>  0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,</div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span>  0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span>  0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span>  0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00,</div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>  0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span>  0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>  0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>  0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>  0x02, 0x00, 0x00, 0x00</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> </div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(std::string(mergerModel.begin(), mergerModel.end()));</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>  CHECK(deserializedNetwork);</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 2, 3, 2, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 4, 3, 2, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span> </div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span>  <span class="keyword">const</span> std::vector<armnn::TensorShape> shapes({inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()});</div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span> </div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a> descriptor =</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>  <a class="code" href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a>(shapes.begin(), shapes.end(), 0);</div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span> </div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>  MergerLayerVerifier verifier(<span class="stringliteral">"merger"</span>, { inputInfo, inputInfo }, { outputInfo }, descriptor);</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>  deserializedNetwork->ExecuteStrategy(verifier);</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span> }</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span> </div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span> TEST_CASE(<span class="stringliteral">"SerializeConcat"</span>)</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span> {</div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"concat"</span>);</div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({2, 3, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({4, 3, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span> </div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>  <span class="keyword">const</span> std::vector<armnn::TensorShape> shapes({inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()});</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span> </div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a> descriptor =</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span>  <a class="code" href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a>(shapes.begin(), shapes.end(), 0);</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span> </div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayerOne = network->AddInputLayer(0);</div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayerTwo = network->AddInputLayer(1);</div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> concatLayer = network->AddConcatLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</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>  inputLayerOne-><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="l01800"></a><span class="lineno"> 1800</span>  inputLayerTwo-><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="l01801"></a><span class="lineno"> 1801</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>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</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>  inputLayerOne-><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="l01804"></a><span class="lineno"> 1804</span>  inputLayerTwo-><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="l01805"></a><span class="lineno"> 1805</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>(outputInfo);</div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span> </div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>  std::string concatLayerNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network);</div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(concatLayerNetwork);</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span> </div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>  <span class="comment">// NOTE: using the MergerLayerVerifier to ensure that it is a concat layer and not a</span></div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span>  <span class="comment">// merger layer that gets placed into the graph.</span></div><div class="line"><a name="l01813"></a><span class="lineno"> 1813</span>  MergerLayerVerifier verifier(layerName, {inputInfo, inputInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l01814"></a><span class="lineno"> 1814</span>  deserializedNetwork->ExecuteStrategy(verifier);</div><div class="line"><a name="l01815"></a><span class="lineno"> 1815</span> }</div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span> </div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span> TEST_CASE(<span class="stringliteral">"SerializeMinimum"</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"> 1819</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"minimum"</span>);</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({ 1, 2, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span> </div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> minimumLayer = network->AddMinimumLayer(layerName.c_str());</div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span> </div><div class="line"><a name="l01828"></a><span class="lineno"> 1828</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>(minimumLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01829"></a><span class="lineno"> 1829</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>(minimumLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01830"></a><span class="lineno"> 1830</span>  minimumLayer-><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="l01831"></a><span class="lineno"> 1831</span> </div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</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>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</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>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>  minimumLayer-><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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span> </div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span>  CHECK(deserializedNetwork);</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="class_layer_verifier_base.xhtml">LayerVerifierBase</a> verifier(layerName, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, {info});</div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</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> </div><div class="line"><a name="l01843"></a><span class="lineno"> 1843</span> TEST_CASE(<span class="stringliteral">"SerializeMultiplication"</span>)</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>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"multiplication"</span>);</div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({ 1, 5, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01849"></a><span class="lineno"> 1849</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l01850"></a><span class="lineno"> 1850</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l01851"></a><span class="lineno"> 1851</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> multiplicationLayer = network->AddMultiplicationLayer(layerName.c_str());</div><div class="line"><a name="l01852"></a><span class="lineno"> 1852</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span> </div><div class="line"><a name="l01854"></a><span class="lineno"> 1854</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>(multiplicationLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01855"></a><span class="lineno"> 1855</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>(multiplicationLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>  multiplicationLayer-><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="l01857"></a><span class="lineno"> 1857</span> </div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</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>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</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>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>  multiplicationLayer-><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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span> </div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span> </div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span>  <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a> verifier(layerName, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, {info});</div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</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> </div><div class="line"><a name="l01869"></a><span class="lineno"> 1869</span> TEST_CASE(<span class="stringliteral">"SerializePrelu"</span>)</div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span> {</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"prelu"</span>);</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo ({ 4, 1, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> alphaTensorInfo ({ 5, 4, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01875"></a><span class="lineno"> 1875</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 5, 4, 3, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01876"></a><span class="lineno"> 1876</span> </div><div class="line"><a name="l01877"></a><span class="lineno"> 1877</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01878"></a><span class="lineno"> 1878</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> alphaLayer = network->AddInputLayer(1);</div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> preluLayer = network->AddPreluLayer(layerName.c_str());</div><div class="line"><a name="l01881"></a><span class="lineno"> 1881</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01882"></a><span class="lineno"> 1882</span> </div><div class="line"><a name="l01883"></a><span class="lineno"> 1883</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>(preluLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01884"></a><span class="lineno"> 1884</span>  alphaLayer-><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>(preluLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01885"></a><span class="lineno"> 1885</span>  preluLayer-><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="l01886"></a><span class="lineno"> 1886</span> </div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</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>(inputTensorInfo);</div><div class="line"><a name="l01888"></a><span class="lineno"> 1888</span>  alphaLayer-><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>(alphaTensorInfo);</div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>  preluLayer-><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>(outputTensorInfo);</div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span> </div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>  CHECK(deserializedNetwork);</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>  <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a> verifier(layerName, {inputTensorInfo, alphaTensorInfo}, {outputTensorInfo});</div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span> }</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> TEST_CASE(<span class="stringliteral">"SerializeNormalization"</span>)</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>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"normalization"</span>);</div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({2, 1, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span> </div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span>  <a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a> desc;</div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>  desc.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>;</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>  desc.m_NormSize = 3;</div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>  desc.m_Alpha = 1;</div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>  desc.m_Beta = 1;</div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>  desc.m_K = 1;</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01912"></a><span class="lineno"> 1912</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> normalizationLayer = network->AddNormalizationLayer(desc, layerName.c_str());</div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</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>  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>(normalizationLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01916"></a><span class="lineno"> 1916</span>  normalizationLayer-><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="l01917"></a><span class="lineno"> 1917</span> </div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span>  normalizationLayer-><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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</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="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>  CHECK(deserializedNetwork);</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="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::NormalizationDescriptor></a> verifier(layerName, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, desc);</div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span> }</div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span> </div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span> TEST_CASE(<span class="stringliteral">"SerializePad"</span>)</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>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"pad"</span>);</div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 2, 3, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 5, 7}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</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>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a> desc({{0, 0}, {1, 0}, {1, 1}, {1, 2}});</div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span> </div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> padLayer = network->AddPadLayer(desc, layerName.c_str());</div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span> </div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</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>(padLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>  padLayer-><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="l01943"></a><span class="lineno"> 1943</span> </div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</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>(inputTensorInfo);</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>  padLayer-><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>(outputTensorInfo);</div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span> </div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span> </div><div class="line"><a name="l01950"></a><span class="lineno"> 1950</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::PadDescriptor></a> verifier(layerName,</div><div class="line"><a name="l01951"></a><span class="lineno"> 1951</span>  {inputTensorInfo},</div><div class="line"><a name="l01952"></a><span class="lineno"> 1952</span>  {outputTensorInfo},</div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span>  desc);</div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span> }</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> TEST_CASE(<span class="stringliteral">"SerializePadReflect"</span>)</div><div class="line"><a name="l01958"></a><span class="lineno"> 1958</span> {</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"padReflect"</span>);</div><div class="line"><a name="l01960"></a><span class="lineno"> 1960</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 2, 3, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01961"></a><span class="lineno"> 1961</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 5, 7}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span> </div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a> desc({{0, 0}, {1, 0}, {1, 1}, {1, 2}});</div><div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>  desc.<a class="code" href="structarmnn_1_1_pad_descriptor.xhtml#a66f2c989f51ab6116de4380390250b69">m_PaddingMode</a> = <a class="code" href="namespacearmnn.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a74de3e45e4491e956e8dc18d841d9b00">armnn::PaddingMode::Reflect</a>;</div><div class="line"><a name="l01965"></a><span class="lineno"> 1965</span> </div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01967"></a><span class="lineno"> 1967</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01968"></a><span class="lineno"> 1968</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> padLayer = network->AddPadLayer(desc, layerName.c_str());</div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span> </div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</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>(padLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>  padLayer-><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="l01973"></a><span class="lineno"> 1973</span> </div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</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>(inputTensorInfo);</div><div class="line"><a name="l01975"></a><span class="lineno"> 1975</span>  padLayer-><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>(outputTensorInfo);</div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span> </div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span> </div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::PadDescriptor></a> verifier(layerName,</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>  {inputTensorInfo},</div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>  {outputTensorInfo},</div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>  desc);</div><div class="line"><a name="l01984"></a><span class="lineno"> 1984</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l01985"></a><span class="lineno"> 1985</span> }</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> TEST_CASE(<span class="stringliteral">"EnsurePadBackwardCompatibility"</span>)</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>  <span class="comment">// The PadDescriptor is being extended with a float PadValue (so a value other than 0</span></div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>  <span class="comment">// can be used to pad the tensor.</span></div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>  <span class="comment">//</span></div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>  <span class="comment">// This test contains a binary representation of a simple input->pad->output network</span></div><div class="line"><a name="l01993"></a><span class="lineno"> 1993</span>  <span class="comment">// prior to this change to test that the descriptor has been updated in a backward</span></div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>  <span class="comment">// compatible way with respect to Deserialization of older binary dumps</span></div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>  <span class="keyword">const</span> std::vector<uint8_t> padModel =</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>  0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>  0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span>  0x54, 0x01, 0x00, 0x00, 0x6C, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>  0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0xD0, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B,</div><div class="line"><a name="l02001"></a><span class="lineno"> 2001</span>  0x04, 0x00, 0x00, 0x00, 0x96, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0xFF, 0x04, 0x00,</div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>  0x00, 0x00, 0x72, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02003"></a><span class="lineno"> 2003</span>  0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00,</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>  0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2C, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span>  0x00, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x16, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00,</div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>  0x0E, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x4C, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02007"></a><span class="lineno"> 2007</span>  0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x08, 0x00,</div><div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>  0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>  0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>  0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00,</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>  0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x70, 0x61, 0x64, 0x00, 0x01, 0x00, 0x00, 0x00, 0x48, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>  0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>  0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x05, 0x00,</div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>  0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00,</div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>  0x00, 0x00, 0x06, 0x00, 0x0A, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span>  0x0E, 0x00, 0x14, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>  0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02023"></a><span class="lineno"> 2023</span>  0x08, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>  0x0A, 0x00, 0x10, 0x00, 0x08, 0x00, 0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01,</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>  0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00,</div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>  0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00</div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>  };</div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span> </div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(std::string(padModel.begin(), padModel.end()));</div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span> </div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 2, 3, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 3, 5, 7 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span> </div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a> descriptor({{ 0, 0 }, { 1, 0 }, { 1, 1 }, { 1, 2 }});</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span> </div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::PadDescriptor></a> verifier(<span class="stringliteral">"pad"</span>, { inputInfo }, { outputInfo }, descriptor);</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</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> TEST_CASE(<span class="stringliteral">"SerializePermute"</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>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"permute"</span>);</div><div class="line"><a name="l02044"></a><span class="lineno"> 2044</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({4, 3, 2, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02045"></a><span class="lineno"> 2045</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({1, 2, 3, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span> </div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span>  <a class="code" href="structarmnn_1_1_permute_descriptor.xhtml">armnn::PermuteDescriptor</a> descriptor(<a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a>({3, 2, 1, 0}));</div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span> </div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> permuteLayer = network->AddPermuteLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span> </div><div class="line"><a name="l02054"></a><span class="lineno"> 2054</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>(permuteLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span>  permuteLayer-><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="l02056"></a><span class="lineno"> 2056</span> </div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</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>(inputTensorInfo);</div><div class="line"><a name="l02058"></a><span class="lineno"> 2058</span>  permuteLayer-><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>(outputTensorInfo);</div><div class="line"><a name="l02059"></a><span class="lineno"> 2059</span> </div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02061"></a><span class="lineno"> 2061</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02062"></a><span class="lineno"> 2062</span> </div><div class="line"><a name="l02063"></a><span class="lineno"> 2063</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::PermuteDescriptor></a> verifier(</div><div class="line"><a name="l02064"></a><span class="lineno"> 2064</span>  layerName, {inputTensorInfo}, {outputTensorInfo}, descriptor);</div><div class="line"><a name="l02065"></a><span class="lineno"> 2065</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</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> </div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span> TEST_CASE(<span class="stringliteral">"SerializePooling2d"</span>)</div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span> {</div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"pooling2d"</span>);</div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({1, 2, 2, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({1, 1, 1, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span> </div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a> desc;</div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l02076"></a><span class="lineno"> 2076</span>  desc.m_PadTop = 0;</div><div class="line"><a name="l02077"></a><span class="lineno"> 2077</span>  desc.m_PadBottom = 0;</div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>  desc.m_PadLeft = 0;</div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span>  desc.m_PadRight = 0;</div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span>  desc.m_PoolType = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>  desc.m_OutputShapeRounding = <a class="code" href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a>;</div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span>  desc.m_PaddingMethod = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span>  desc.m_PoolHeight = 2;</div><div class="line"><a name="l02084"></a><span class="lineno"> 2084</span>  desc.m_PoolWidth = 2;</div><div class="line"><a name="l02085"></a><span class="lineno"> 2085</span>  desc.m_StrideX = 2;</div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span>  desc.m_StrideY = 2;</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">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02089"></a><span class="lineno"> 2089</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02090"></a><span class="lineno"> 2090</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> pooling2dLayer = network->AddPooling2dLayer(desc, layerName.c_str());</div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span> </div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</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>(pooling2dLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span>  pooling2dLayer-><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="l02095"></a><span class="lineno"> 2095</span> </div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</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="l02097"></a><span class="lineno"> 2097</span>  pooling2dLayer-><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="l02098"></a><span class="lineno"> 2098</span> </div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02100"></a><span class="lineno"> 2100</span>  CHECK(deserializedNetwork);</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="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::Pooling2dDescriptor></a> verifier(</div><div class="line"><a name="l02103"></a><span class="lineno"> 2103</span>  layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02105"></a><span class="lineno"> 2105</span> }</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> TEST_CASE(<span class="stringliteral">"SerializePooling3d"</span>)</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>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"pooling3d"</span>);</div><div class="line"><a name="l02110"></a><span class="lineno"> 2110</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({1, 1, 2, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02111"></a><span class="lineno"> 2111</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({1, 1, 1, 1, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span> </div><div class="line"><a name="l02113"></a><span class="lineno"> 2113</span>  <a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml">armnn::Pooling3dDescriptor</a> desc;</div><div class="line"><a name="l02114"></a><span class="lineno"> 2114</span>  desc.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a4dd0194b114cbf51da5b3a72569863ef">armnn::DataLayout::NDHWC</a>;</div><div class="line"><a name="l02115"></a><span class="lineno"> 2115</span>  desc.m_PadFront = 0;</div><div class="line"><a name="l02116"></a><span class="lineno"> 2116</span>  desc.m_PadBack = 0;</div><div class="line"><a name="l02117"></a><span class="lineno"> 2117</span>  desc.m_PadTop = 0;</div><div class="line"><a name="l02118"></a><span class="lineno"> 2118</span>  desc.m_PadBottom = 0;</div><div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>  desc.m_PadLeft = 0;</div><div class="line"><a name="l02120"></a><span class="lineno"> 2120</span>  desc.m_PadRight = 0;</div><div class="line"><a name="l02121"></a><span class="lineno"> 2121</span>  desc.m_PoolType = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l02122"></a><span class="lineno"> 2122</span>  desc.m_OutputShapeRounding = <a class="code" href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a>;</div><div class="line"><a name="l02123"></a><span class="lineno"> 2123</span>  desc.m_PaddingMethod = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l02124"></a><span class="lineno"> 2124</span>  desc.m_PoolHeight = 2;</div><div class="line"><a name="l02125"></a><span class="lineno"> 2125</span>  desc.m_PoolWidth = 2;</div><div class="line"><a name="l02126"></a><span class="lineno"> 2126</span>  desc.m_PoolDepth = 2;</div><div class="line"><a name="l02127"></a><span class="lineno"> 2127</span>  desc.m_StrideX = 2;</div><div class="line"><a name="l02128"></a><span class="lineno"> 2128</span>  desc.m_StrideY = 2;</div><div class="line"><a name="l02129"></a><span class="lineno"> 2129</span>  desc.m_StrideZ = 2;</div><div class="line"><a name="l02130"></a><span class="lineno"> 2130</span> </div><div class="line"><a name="l02131"></a><span class="lineno"> 2131</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02132"></a><span class="lineno"> 2132</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02133"></a><span class="lineno"> 2133</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> pooling3dLayer = network->AddPooling3dLayer(desc, layerName.c_str());</div><div class="line"><a name="l02134"></a><span class="lineno"> 2134</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</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>  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>(pooling3dLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02137"></a><span class="lineno"> 2137</span>  pooling3dLayer-><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="l02138"></a><span class="lineno"> 2138</span> </div><div class="line"><a name="l02139"></a><span class="lineno"> 2139</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="l02140"></a><span class="lineno"> 2140</span>  pooling3dLayer-><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="l02141"></a><span class="lineno"> 2141</span> </div><div class="line"><a name="l02142"></a><span class="lineno"> 2142</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02143"></a><span class="lineno"> 2143</span>  CHECK(deserializedNetwork);</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>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::Pooling3dDescriptor></a> verifier(</div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>  layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02148"></a><span class="lineno"> 2148</span> }</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> TEST_CASE(<span class="stringliteral">"SerializeQuantize"</span>)</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>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"quantize"</span>);</div><div class="line"><a name="l02153"></a><span class="lineno"> 2153</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({ 1, 2, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02154"></a><span class="lineno"> 2154</span> </div><div class="line"><a name="l02155"></a><span class="lineno"> 2155</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> quantizeLayer = network->AddQuantizeLayer(layerName.c_str());</div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span> </div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</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>(quantizeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span>  quantizeLayer-><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="l02162"></a><span class="lineno"> 2162</span> </div><div class="line"><a name="l02163"></a><span class="lineno"> 2163</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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span>  quantizeLayer-><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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02167"></a><span class="lineno"> 2167</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02168"></a><span class="lineno"> 2168</span> </div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span>  <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a> verifier(layerName, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>});</div><div class="line"><a name="l02170"></a><span class="lineno"> 2170</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02171"></a><span class="lineno"> 2171</span> }</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> TEST_CASE(<span class="stringliteral">"SerializeRank"</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>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"rank"</span>);</div><div class="line"><a name="l02176"></a><span class="lineno"> 2176</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({1, 9}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02177"></a><span class="lineno"> 2177</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>);</div><div class="line"><a name="l02178"></a><span class="lineno"> 2178</span> </div><div class="line"><a name="l02179"></a><span class="lineno"> 2179</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02180"></a><span class="lineno"> 2180</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02181"></a><span class="lineno"> 2181</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> rankLayer = network->AddRankLayer(layerName.c_str());</div><div class="line"><a name="l02182"></a><span class="lineno"> 2182</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02183"></a><span class="lineno"> 2183</span> </div><div class="line"><a name="l02184"></a><span class="lineno"> 2184</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>(rankLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02185"></a><span class="lineno"> 2185</span>  rankLayer-><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="l02186"></a><span class="lineno"> 2186</span> </div><div class="line"><a name="l02187"></a><span class="lineno"> 2187</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="l02188"></a><span class="lineno"> 2188</span>  rankLayer-><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="l02189"></a><span class="lineno"> 2189</span> </div><div class="line"><a name="l02190"></a><span class="lineno"> 2190</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02191"></a><span class="lineno"> 2191</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02192"></a><span class="lineno"> 2192</span> </div><div class="line"><a name="l02193"></a><span class="lineno"> 2193</span>  <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a> verifier(layerName, {inputInfo}, {outputInfo});</div><div class="line"><a name="l02194"></a><span class="lineno"> 2194</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02195"></a><span class="lineno"> 2195</span> }</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> TEST_CASE(<span class="stringliteral">"SerializeReduceSum"</span>)</div><div class="line"><a name="l02198"></a><span class="lineno"> 2198</span> {</div><div class="line"><a name="l02199"></a><span class="lineno"> 2199</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"Reduce_Sum"</span>);</div><div class="line"><a name="l02200"></a><span class="lineno"> 2200</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({1, 1, 3, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02201"></a><span class="lineno"> 2201</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({1, 1, 1, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02202"></a><span class="lineno"> 2202</span> </div><div class="line"><a name="l02203"></a><span class="lineno"> 2203</span>  <a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml">armnn::ReduceDescriptor</a> descriptor;</div><div class="line"><a name="l02204"></a><span class="lineno"> 2204</span>  descriptor.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#aa1c6fc8c96404252f1072632fc5acb59">m_vAxis</a> = { 2 };</div><div class="line"><a name="l02205"></a><span class="lineno"> 2205</span>  descriptor.m_ReduceOperation = <a class="code" href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0aa0ec87054b5e5b7847d0d8780a01a3d5">armnn::ReduceOperation::Sum</a>;</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#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02208"></a><span class="lineno"> 2208</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02209"></a><span class="lineno"> 2209</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> reduceSumLayer = network->AddReduceLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l02210"></a><span class="lineno"> 2210</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02211"></a><span class="lineno"> 2211</span> </div><div class="line"><a name="l02212"></a><span class="lineno"> 2212</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>(reduceSumLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02213"></a><span class="lineno"> 2213</span>  reduceSumLayer-><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="l02214"></a><span class="lineno"> 2214</span> </div><div class="line"><a name="l02215"></a><span class="lineno"> 2215</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="l02216"></a><span class="lineno"> 2216</span>  reduceSumLayer-><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="l02217"></a><span class="lineno"> 2217</span> </div><div class="line"><a name="l02218"></a><span class="lineno"> 2218</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02219"></a><span class="lineno"> 2219</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02220"></a><span class="lineno"> 2220</span> </div><div class="line"><a name="l02221"></a><span class="lineno"> 2221</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::ReduceDescriptor></a> verifier(layerName, {inputInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l02222"></a><span class="lineno"> 2222</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02223"></a><span class="lineno"> 2223</span> }</div><div class="line"><a name="l02224"></a><span class="lineno"> 2224</span> </div><div class="line"><a name="l02225"></a><span class="lineno"> 2225</span> TEST_CASE(<span class="stringliteral">"SerializeReshape"</span>)</div><div class="line"><a name="l02226"></a><span class="lineno"> 2226</span> {</div><div class="line"><a name="l02227"></a><span class="lineno"> 2227</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"reshape"</span>);</div><div class="line"><a name="l02228"></a><span class="lineno"> 2228</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({1, 9}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02229"></a><span class="lineno"> 2229</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({3, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02230"></a><span class="lineno"> 2230</span> </div><div class="line"><a name="l02231"></a><span class="lineno"> 2231</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">armnn::ReshapeDescriptor</a> descriptor({3, 3});</div><div class="line"><a name="l02232"></a><span class="lineno"> 2232</span> </div><div class="line"><a name="l02233"></a><span class="lineno"> 2233</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02234"></a><span class="lineno"> 2234</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02235"></a><span class="lineno"> 2235</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> reshapeLayer = network->AddReshapeLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l02236"></a><span class="lineno"> 2236</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02237"></a><span class="lineno"> 2237</span> </div><div class="line"><a name="l02238"></a><span class="lineno"> 2238</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>(reshapeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02239"></a><span class="lineno"> 2239</span>  reshapeLayer-><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="l02240"></a><span class="lineno"> 2240</span> </div><div class="line"><a name="l02241"></a><span class="lineno"> 2241</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="l02242"></a><span class="lineno"> 2242</span>  reshapeLayer-><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="l02243"></a><span class="lineno"> 2243</span> </div><div class="line"><a name="l02244"></a><span class="lineno"> 2244</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02245"></a><span class="lineno"> 2245</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02246"></a><span class="lineno"> 2246</span> </div><div class="line"><a name="l02247"></a><span class="lineno"> 2247</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::ReshapeDescriptor></a> verifier(</div><div class="line"><a name="l02248"></a><span class="lineno"> 2248</span>  layerName, {inputInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l02249"></a><span class="lineno"> 2249</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02250"></a><span class="lineno"> 2250</span> }</div><div class="line"><a name="l02251"></a><span class="lineno"> 2251</span> </div><div class="line"><a name="l02252"></a><span class="lineno"> 2252</span> TEST_CASE(<span class="stringliteral">"SerializeResize"</span>)</div><div class="line"><a name="l02253"></a><span class="lineno"> 2253</span> {</div><div class="line"><a name="l02254"></a><span class="lineno"> 2254</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"resize"</span>);</div><div class="line"><a name="l02255"></a><span class="lineno"> 2255</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 5, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02256"></a><span class="lineno"> 2256</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 2, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02257"></a><span class="lineno"> 2257</span> </div><div class="line"><a name="l02258"></a><span class="lineno"> 2258</span>  <a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">armnn::ResizeDescriptor</a> desc;</div><div class="line"><a name="l02259"></a><span class="lineno"> 2259</span>  desc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">m_TargetWidth</a> = 4;</div><div class="line"><a name="l02260"></a><span class="lineno"> 2260</span>  desc.m_TargetHeight = 2;</div><div class="line"><a name="l02261"></a><span class="lineno"> 2261</span>  desc.m_Method = <a class="code" href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaa020331bb30d2fa2ecf7c3a0777823f">armnn::ResizeMethod::NearestNeighbor</a>;</div><div class="line"><a name="l02262"></a><span class="lineno"> 2262</span>  desc.m_AlignCorners = <span class="keyword">true</span>;</div><div class="line"><a name="l02263"></a><span class="lineno"> 2263</span>  desc.m_HalfPixelCenters = <span class="keyword">true</span>;</div><div class="line"><a name="l02264"></a><span class="lineno"> 2264</span> </div><div class="line"><a name="l02265"></a><span class="lineno"> 2265</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02266"></a><span class="lineno"> 2266</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02267"></a><span class="lineno"> 2267</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> resizeLayer = network->AddResizeLayer(desc, layerName.c_str());</div><div class="line"><a name="l02268"></a><span class="lineno"> 2268</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02269"></a><span class="lineno"> 2269</span> </div><div class="line"><a name="l02270"></a><span class="lineno"> 2270</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>(resizeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02271"></a><span class="lineno"> 2271</span>  resizeLayer-><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="l02272"></a><span class="lineno"> 2272</span> </div><div class="line"><a name="l02273"></a><span class="lineno"> 2273</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="l02274"></a><span class="lineno"> 2274</span>  resizeLayer-><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="l02275"></a><span class="lineno"> 2275</span> </div><div class="line"><a name="l02276"></a><span class="lineno"> 2276</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02277"></a><span class="lineno"> 2277</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02278"></a><span class="lineno"> 2278</span> </div><div class="line"><a name="l02279"></a><span class="lineno"> 2279</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::ResizeDescriptor></a> verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l02280"></a><span class="lineno"> 2280</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02281"></a><span class="lineno"> 2281</span> }</div><div class="line"><a name="l02282"></a><span class="lineno"> 2282</span> </div><div class="line"><a name="l02283"></a><span class="lineno"> 2283</span> <span class="keyword">class </span>ResizeBilinearLayerVerifier : <span class="keyword">public</span> <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor</a><armnn::ResizeDescriptor></div><div class="line"><a name="l02284"></a><span class="lineno"> 2284</span> {</div><div class="line"><a name="l02285"></a><span class="lineno"> 2285</span> <span class="keyword">public</span>:</div><div class="line"><a name="l02286"></a><span class="lineno"> 2286</span>  ResizeBilinearLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l02287"></a><span class="lineno"> 2287</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l02288"></a><span class="lineno"> 2288</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l02289"></a><span class="lineno"> 2289</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">armnn::ResizeDescriptor</a>& descriptor)</div><div class="line"><a name="l02290"></a><span class="lineno"> 2290</span>  : <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::ResizeDescriptor></a>(</div><div class="line"><a name="l02291"></a><span class="lineno"> 2291</span>  layerName, inputInfos, outputInfos, descriptor) {}</div><div class="line"><a name="l02292"></a><span class="lineno"> 2292</span> </div><div class="line"><a name="l02293"></a><span class="lineno"> 2293</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="l02294"></a><span class="lineno"> 2294</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l02295"></a><span class="lineno"> 2295</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l02296"></a><span class="lineno"> 2296</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l02297"></a><span class="lineno"> 2297</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span> = 0)<span class="keyword"> override</span></div><div class="line"><a name="l02298"></a><span class="lineno"> 2298</span> <span class="keyword"> </span>{</div><div class="line"><a name="l02299"></a><span class="lineno"> 2299</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(descriptor, constants, <span class="keywordtype">id</span>);</div><div class="line"><a name="l02300"></a><span class="lineno"> 2300</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="l02301"></a><span class="lineno"> 2301</span>  {</div><div class="line"><a name="l02302"></a><span class="lineno"> 2302</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l02303"></a><span class="lineno"> 2303</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l02304"></a><span class="lineno"> 2304</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a9d723d04c40bfd81835c0766a698cf63">armnn::LayerType::Resize</a>:</div><div class="line"><a name="l02305"></a><span class="lineno"> 2305</span>  {</div><div class="line"><a name="l02306"></a><span class="lineno"> 2306</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l02307"></a><span class="lineno"> 2307</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">armnn::ResizeDescriptor</a>& layerDescriptor =</div><div class="line"><a name="l02308"></a><span class="lineno"> 2308</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">armnn::ResizeDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l02309"></a><span class="lineno"> 2309</span>  CHECK(layerDescriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">m_Method</a> == <a class="code" href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f">armnn::ResizeMethod::Bilinear</a>);</div><div class="line"><a name="l02310"></a><span class="lineno"> 2310</span>  CHECK(layerDescriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">m_TargetWidth</a> == m_Descriptor.m_TargetWidth);</div><div class="line"><a name="l02311"></a><span class="lineno"> 2311</span>  CHECK(layerDescriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">m_TargetHeight</a> == m_Descriptor.m_TargetHeight);</div><div class="line"><a name="l02312"></a><span class="lineno"> 2312</span>  CHECK(layerDescriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> == m_Descriptor.m_DataLayout);</div><div class="line"><a name="l02313"></a><span class="lineno"> 2313</span>  CHECK(layerDescriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#ae1a4b3b6c60552509b89747cebb900a2">m_AlignCorners</a> == m_Descriptor.m_AlignCorners);</div><div class="line"><a name="l02314"></a><span class="lineno"> 2314</span>  CHECK(layerDescriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a4022d5107338aaf5eb7abebf78a1360b">m_HalfPixelCenters</a> == m_Descriptor.m_HalfPixelCenters);</div><div class="line"><a name="l02315"></a><span class="lineno"> 2315</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02316"></a><span class="lineno"> 2316</span>  }</div><div class="line"><a name="l02317"></a><span class="lineno"> 2317</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l02318"></a><span class="lineno"> 2318</span>  {</div><div class="line"><a name="l02319"></a><span class="lineno"> 2319</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">"Unexpected layer type in test model. ResizeBiliniar "</span></div><div class="line"><a name="l02320"></a><span class="lineno"> 2320</span>  <span class="stringliteral">"should have translated to Resize"</span>);</div><div class="line"><a name="l02321"></a><span class="lineno"> 2321</span>  }</div><div class="line"><a name="l02322"></a><span class="lineno"> 2322</span>  }</div><div class="line"><a name="l02323"></a><span class="lineno"> 2323</span>  }</div><div class="line"><a name="l02324"></a><span class="lineno"> 2324</span> };</div><div class="line"><a name="l02325"></a><span class="lineno"> 2325</span> </div><div class="line"><a name="l02326"></a><span class="lineno"> 2326</span> TEST_CASE(<span class="stringliteral">"SerializeResizeBilinear"</span>)</div><div class="line"><a name="l02327"></a><span class="lineno"> 2327</span> {</div><div class="line"><a name="l02328"></a><span class="lineno"> 2328</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"resizeBilinear"</span>);</div><div class="line"><a name="l02329"></a><span class="lineno"> 2329</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 5, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02330"></a><span class="lineno"> 2330</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 2, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02331"></a><span class="lineno"> 2331</span> </div><div class="line"><a name="l02332"></a><span class="lineno"> 2332</span>  <a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">armnn::ResizeDescriptor</a> desc;</div><div class="line"><a name="l02333"></a><span class="lineno"> 2333</span>  desc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">m_Method</a> = <a class="code" href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f">armnn::ResizeMethod::Bilinear</a>;</div><div class="line"><a name="l02334"></a><span class="lineno"> 2334</span>  desc.m_TargetWidth = 4u;</div><div class="line"><a name="l02335"></a><span class="lineno"> 2335</span>  desc.m_TargetHeight = 2u;</div><div class="line"><a name="l02336"></a><span class="lineno"> 2336</span>  desc.m_AlignCorners = <span class="keyword">true</span>;</div><div class="line"><a name="l02337"></a><span class="lineno"> 2337</span>  desc.m_HalfPixelCenters = <span class="keyword">true</span>;</div><div class="line"><a name="l02338"></a><span class="lineno"> 2338</span> </div><div class="line"><a name="l02339"></a><span class="lineno"> 2339</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02340"></a><span class="lineno"> 2340</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02341"></a><span class="lineno"> 2341</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> resizeLayer = network->AddResizeLayer(desc, layerName.c_str());</div><div class="line"><a name="l02342"></a><span class="lineno"> 2342</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02343"></a><span class="lineno"> 2343</span> </div><div class="line"><a name="l02344"></a><span class="lineno"> 2344</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>(resizeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02345"></a><span class="lineno"> 2345</span>  resizeLayer-><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="l02346"></a><span class="lineno"> 2346</span> </div><div class="line"><a name="l02347"></a><span class="lineno"> 2347</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="l02348"></a><span class="lineno"> 2348</span>  resizeLayer-><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="l02349"></a><span class="lineno"> 2349</span> </div><div class="line"><a name="l02350"></a><span class="lineno"> 2350</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02351"></a><span class="lineno"> 2351</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02352"></a><span class="lineno"> 2352</span> </div><div class="line"><a name="l02353"></a><span class="lineno"> 2353</span>  ResizeBilinearLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l02354"></a><span class="lineno"> 2354</span>  deserializedNetwork->ExecuteStrategy(verifier);</div><div class="line"><a name="l02355"></a><span class="lineno"> 2355</span> }</div><div class="line"><a name="l02356"></a><span class="lineno"> 2356</span> </div><div class="line"><a name="l02357"></a><span class="lineno"> 2357</span> TEST_CASE(<span class="stringliteral">"EnsureResizeBilinearBackwardCompatibility"</span>)</div><div class="line"><a name="l02358"></a><span class="lineno"> 2358</span> {</div><div class="line"><a name="l02359"></a><span class="lineno"> 2359</span>  <span class="comment">// The hex data below is a flat buffer containing a simple network with an input,</span></div><div class="line"><a name="l02360"></a><span class="lineno"> 2360</span>  <span class="comment">// a ResizeBilinearLayer (now deprecated and removed) and an output</span></div><div class="line"><a name="l02361"></a><span class="lineno"> 2361</span>  <span class="comment">//</span></div><div class="line"><a name="l02362"></a><span class="lineno"> 2362</span>  <span class="comment">// This test verifies that we can still deserialize this old-style model by replacing</span></div><div class="line"><a name="l02363"></a><span class="lineno"> 2363</span>  <span class="comment">// the ResizeBilinearLayer with an equivalent ResizeLayer</span></div><div class="line"><a name="l02364"></a><span class="lineno"> 2364</span>  <span class="keyword">const</span> std::vector<uint8_t> resizeBilinearModel =</div><div class="line"><a name="l02365"></a><span class="lineno"> 2365</span>  {</div><div class="line"><a name="l02366"></a><span class="lineno"> 2366</span>  0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l02367"></a><span class="lineno"> 2367</span>  0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02368"></a><span class="lineno"> 2368</span>  0x50, 0x01, 0x00, 0x00, 0x74, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02369"></a><span class="lineno"> 2369</span>  0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0xD4, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B,</div><div class="line"><a name="l02370"></a><span class="lineno"> 2370</span>  0x04, 0x00, 0x00, 0x00, 0xC2, 0xFE, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00,</div><div class="line"><a name="l02371"></a><span class="lineno"> 2371</span>  0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02372"></a><span class="lineno"> 2372</span>  0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02373"></a><span class="lineno"> 2373</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02374"></a><span class="lineno"> 2374</span>  0x38, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0xFF, 0xFF, 0xFF, 0x00, 0x00,</div><div class="line"><a name="l02375"></a><span class="lineno"> 2375</span>  0x00, 0x1A, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02376"></a><span class="lineno"> 2376</span>  0x34, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x12, 0x00, 0x08, 0x00, 0x0C, 0x00,</div><div class="line"><a name="l02377"></a><span class="lineno"> 2377</span>  0x07, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02378"></a><span class="lineno"> 2378</span>  0x00, 0x00, 0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00,</div><div class="line"><a name="l02379"></a><span class="lineno"> 2379</span>  0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x19, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02380"></a><span class="lineno"> 2380</span>  0x20, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x72, 0x65, 0x73, 0x69, 0x7A, 0x65, 0x42, 0x69, 0x6C, 0x69,</div><div class="line"><a name="l02381"></a><span class="lineno"> 2381</span>  0x6E, 0x65, 0x61, 0x72, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x48, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02382"></a><span class="lineno"> 2382</span>  0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l02383"></a><span class="lineno"> 2383</span>  0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02384"></a><span class="lineno"> 2384</span>  0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l02385"></a><span class="lineno"> 2385</span>  0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02386"></a><span class="lineno"> 2386</span>  0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02387"></a><span class="lineno"> 2387</span>  0x00, 0x09, 0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00,</div><div class="line"><a name="l02388"></a><span class="lineno"> 2388</span>  0x0A, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00,</div><div class="line"><a name="l02389"></a><span class="lineno"> 2389</span>  0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02390"></a><span class="lineno"> 2390</span>  0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02391"></a><span class="lineno"> 2391</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l02392"></a><span class="lineno"> 2392</span>  0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00,</div><div class="line"><a name="l02393"></a><span class="lineno"> 2393</span>  0x08, 0x00, 0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02394"></a><span class="lineno"> 2394</span>  0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x05, 0x00,</div><div class="line"><a name="l02395"></a><span class="lineno"> 2395</span>  0x00, 0x00, 0x05, 0x00, 0x00, 0x00</div><div class="line"><a name="l02396"></a><span class="lineno"> 2396</span>  };</div><div class="line"><a name="l02397"></a><span class="lineno"> 2397</span> </div><div class="line"><a name="l02398"></a><span class="lineno"> 2398</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork =</div><div class="line"><a name="l02399"></a><span class="lineno"> 2399</span>  <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(std::string(resizeBilinearModel.begin(), resizeBilinearModel.end()));</div><div class="line"><a name="l02400"></a><span class="lineno"> 2400</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02401"></a><span class="lineno"> 2401</span> </div><div class="line"><a name="l02402"></a><span class="lineno"> 2402</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 5, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02403"></a><span class="lineno"> 2403</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 2, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02404"></a><span class="lineno"> 2404</span> </div><div class="line"><a name="l02405"></a><span class="lineno"> 2405</span>  <a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">armnn::ResizeDescriptor</a> descriptor;</div><div class="line"><a name="l02406"></a><span class="lineno"> 2406</span>  descriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">m_TargetWidth</a> = 4u;</div><div class="line"><a name="l02407"></a><span class="lineno"> 2407</span>  descriptor.m_TargetHeight = 2u;</div><div class="line"><a name="l02408"></a><span class="lineno"> 2408</span> </div><div class="line"><a name="l02409"></a><span class="lineno"> 2409</span>  ResizeBilinearLayerVerifier verifier(<span class="stringliteral">"resizeBilinear"</span>, { inputInfo }, { outputInfo }, descriptor);</div><div class="line"><a name="l02410"></a><span class="lineno"> 2410</span>  deserializedNetwork->ExecuteStrategy(verifier);</div><div class="line"><a name="l02411"></a><span class="lineno"> 2411</span> }</div><div class="line"><a name="l02412"></a><span class="lineno"> 2412</span> </div><div class="line"><a name="l02413"></a><span class="lineno"> 2413</span> TEST_CASE(<span class="stringliteral">"SerializeShape"</span>)</div><div class="line"><a name="l02414"></a><span class="lineno"> 2414</span> {</div><div class="line"><a name="l02415"></a><span class="lineno"> 2415</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"shape"</span>);</div><div class="line"><a name="l02416"></a><span class="lineno"> 2416</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({1, 3, 3, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>);</div><div class="line"><a name="l02417"></a><span class="lineno"> 2417</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>);</div><div class="line"><a name="l02418"></a><span class="lineno"> 2418</span> </div><div class="line"><a name="l02419"></a><span class="lineno"> 2419</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02420"></a><span class="lineno"> 2420</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02421"></a><span class="lineno"> 2421</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> shapeLayer = network->AddShapeLayer(layerName.c_str());</div><div class="line"><a name="l02422"></a><span class="lineno"> 2422</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02423"></a><span class="lineno"> 2423</span> </div><div class="line"><a name="l02424"></a><span class="lineno"> 2424</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>(shapeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02425"></a><span class="lineno"> 2425</span>  shapeLayer-><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="l02426"></a><span class="lineno"> 2426</span> </div><div class="line"><a name="l02427"></a><span class="lineno"> 2427</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="l02428"></a><span class="lineno"> 2428</span>  shapeLayer-><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="l02429"></a><span class="lineno"> 2429</span> </div><div class="line"><a name="l02430"></a><span class="lineno"> 2430</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02431"></a><span class="lineno"> 2431</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02432"></a><span class="lineno"> 2432</span> </div><div class="line"><a name="l02433"></a><span class="lineno"> 2433</span>  <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a> verifier(layerName, {inputInfo}, {outputInfo});</div><div class="line"><a name="l02434"></a><span class="lineno"> 2434</span> </div><div class="line"><a name="l02435"></a><span class="lineno"> 2435</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02436"></a><span class="lineno"> 2436</span> }</div><div class="line"><a name="l02437"></a><span class="lineno"> 2437</span> </div><div class="line"><a name="l02438"></a><span class="lineno"> 2438</span> TEST_CASE(<span class="stringliteral">"SerializeSlice"</span>)</div><div class="line"><a name="l02439"></a><span class="lineno"> 2439</span> {</div><div class="line"><a name="l02440"></a><span class="lineno"> 2440</span>  <span class="keyword">const</span> std::string layerName{<span class="stringliteral">"slice"</span>};</div><div class="line"><a name="l02441"></a><span class="lineno"> 2441</span> </div><div class="line"><a name="l02442"></a><span class="lineno"> 2442</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({3, 2, 3, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02443"></a><span class="lineno"> 2443</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({2, 2, 2, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02444"></a><span class="lineno"> 2444</span> </div><div class="line"><a name="l02445"></a><span class="lineno"> 2445</span>  <a class="code" href="structarmnn_1_1_slice_descriptor.xhtml">armnn::SliceDescriptor</a> descriptor({ 0, 0, 1, 0}, {2, 2, 2, 1});</div><div class="line"><a name="l02446"></a><span class="lineno"> 2446</span> </div><div class="line"><a name="l02447"></a><span class="lineno"> 2447</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02448"></a><span class="lineno"> 2448</span> </div><div class="line"><a name="l02449"></a><span class="lineno"> 2449</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02450"></a><span class="lineno"> 2450</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> sliceLayer = network->AddSliceLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l02451"></a><span class="lineno"> 2451</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02452"></a><span class="lineno"> 2452</span> </div><div class="line"><a name="l02453"></a><span class="lineno"> 2453</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="l02454"></a><span class="lineno"> 2454</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="l02455"></a><span class="lineno"> 2455</span> </div><div class="line"><a name="l02456"></a><span class="lineno"> 2456</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="l02457"></a><span class="lineno"> 2457</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>(outputInfo);</div><div class="line"><a name="l02458"></a><span class="lineno"> 2458</span> </div><div class="line"><a name="l02459"></a><span class="lineno"> 2459</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02460"></a><span class="lineno"> 2460</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02461"></a><span class="lineno"> 2461</span> </div><div class="line"><a name="l02462"></a><span class="lineno"> 2462</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::SliceDescriptor></a> verifier(layerName, {inputInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l02463"></a><span class="lineno"> 2463</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02464"></a><span class="lineno"> 2464</span> }</div><div class="line"><a name="l02465"></a><span class="lineno"> 2465</span> </div><div class="line"><a name="l02466"></a><span class="lineno"> 2466</span> TEST_CASE(<span class="stringliteral">"SerializeSoftmax"</span>)</div><div class="line"><a name="l02467"></a><span class="lineno"> 2467</span> {</div><div class="line"><a name="l02468"></a><span class="lineno"> 2468</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"softmax"</span>);</div><div class="line"><a name="l02469"></a><span class="lineno"> 2469</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({1, 10}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02470"></a><span class="lineno"> 2470</span> </div><div class="line"><a name="l02471"></a><span class="lineno"> 2471</span>  <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::SoftmaxDescriptor</a> descriptor;</div><div class="line"><a name="l02472"></a><span class="lineno"> 2472</span>  descriptor.<a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = 1.0f;</div><div class="line"><a name="l02473"></a><span class="lineno"> 2473</span> </div><div class="line"><a name="l02474"></a><span class="lineno"> 2474</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02475"></a><span class="lineno"> 2475</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02476"></a><span class="lineno"> 2476</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> softmaxLayer = network->AddSoftmaxLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l02477"></a><span class="lineno"> 2477</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02478"></a><span class="lineno"> 2478</span> </div><div class="line"><a name="l02479"></a><span class="lineno"> 2479</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>(softmaxLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02480"></a><span class="lineno"> 2480</span>  softmaxLayer-><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="l02481"></a><span class="lineno"> 2481</span> </div><div class="line"><a name="l02482"></a><span class="lineno"> 2482</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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l02483"></a><span class="lineno"> 2483</span>  softmaxLayer-><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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l02484"></a><span class="lineno"> 2484</span> </div><div class="line"><a name="l02485"></a><span class="lineno"> 2485</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02486"></a><span class="lineno"> 2486</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02487"></a><span class="lineno"> 2487</span> </div><div class="line"><a name="l02488"></a><span class="lineno"> 2488</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::SoftmaxDescriptor></a> verifier(layerName, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, descriptor);</div><div class="line"><a name="l02489"></a><span class="lineno"> 2489</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02490"></a><span class="lineno"> 2490</span> }</div><div class="line"><a name="l02491"></a><span class="lineno"> 2491</span> </div><div class="line"><a name="l02492"></a><span class="lineno"> 2492</span> TEST_CASE(<span class="stringliteral">"SerializeSpaceToBatchNd"</span>)</div><div class="line"><a name="l02493"></a><span class="lineno"> 2493</span> {</div><div class="line"><a name="l02494"></a><span class="lineno"> 2494</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"spaceToBatchNd"</span>);</div><div class="line"><a name="l02495"></a><span class="lineno"> 2495</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({2, 1, 2, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02496"></a><span class="lineno"> 2496</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({8, 1, 1, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02497"></a><span class="lineno"> 2497</span> </div><div class="line"><a name="l02498"></a><span class="lineno"> 2498</span>  <a class="code" href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml">armnn::SpaceToBatchNdDescriptor</a> desc;</div><div class="line"><a name="l02499"></a><span class="lineno"> 2499</span>  desc.<a class="code" href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>;</div><div class="line"><a name="l02500"></a><span class="lineno"> 2500</span>  desc.m_BlockShape = {2, 2};</div><div class="line"><a name="l02501"></a><span class="lineno"> 2501</span>  desc.m_PadList = {{0, 0}, {2, 0}};</div><div class="line"><a name="l02502"></a><span class="lineno"> 2502</span> </div><div class="line"><a name="l02503"></a><span class="lineno"> 2503</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02504"></a><span class="lineno"> 2504</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02505"></a><span class="lineno"> 2505</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> spaceToBatchNdLayer = network->AddSpaceToBatchNdLayer(desc, layerName.c_str());</div><div class="line"><a name="l02506"></a><span class="lineno"> 2506</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02507"></a><span class="lineno"> 2507</span> </div><div class="line"><a name="l02508"></a><span class="lineno"> 2508</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>(spaceToBatchNdLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02509"></a><span class="lineno"> 2509</span>  spaceToBatchNdLayer-><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="l02510"></a><span class="lineno"> 2510</span> </div><div class="line"><a name="l02511"></a><span class="lineno"> 2511</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="l02512"></a><span class="lineno"> 2512</span>  spaceToBatchNdLayer-><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="l02513"></a><span class="lineno"> 2513</span> </div><div class="line"><a name="l02514"></a><span class="lineno"> 2514</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02515"></a><span class="lineno"> 2515</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02516"></a><span class="lineno"> 2516</span> </div><div class="line"><a name="l02517"></a><span class="lineno"> 2517</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::SpaceToBatchNdDescriptor></a> verifier(</div><div class="line"><a name="l02518"></a><span class="lineno"> 2518</span>  layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l02519"></a><span class="lineno"> 2519</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02520"></a><span class="lineno"> 2520</span> }</div><div class="line"><a name="l02521"></a><span class="lineno"> 2521</span> </div><div class="line"><a name="l02522"></a><span class="lineno"> 2522</span> TEST_CASE(<span class="stringliteral">"SerializeSpaceToDepth"</span>)</div><div class="line"><a name="l02523"></a><span class="lineno"> 2523</span> {</div><div class="line"><a name="l02524"></a><span class="lineno"> 2524</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"spaceToDepth"</span>);</div><div class="line"><a name="l02525"></a><span class="lineno"> 2525</span> </div><div class="line"><a name="l02526"></a><span class="lineno"> 2526</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 16, 8, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02527"></a><span class="lineno"> 2527</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 8, 4, 12 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02528"></a><span class="lineno"> 2528</span> </div><div class="line"><a name="l02529"></a><span class="lineno"> 2529</span>  <a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml">armnn::SpaceToDepthDescriptor</a> desc;</div><div class="line"><a name="l02530"></a><span class="lineno"> 2530</span>  desc.<a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml#a6c6b8957f1e176867e5fb05b1a1a1486">m_BlockSize</a> = 2;</div><div class="line"><a name="l02531"></a><span class="lineno"> 2531</span>  desc.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l02532"></a><span class="lineno"> 2532</span> </div><div class="line"><a name="l02533"></a><span class="lineno"> 2533</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02534"></a><span class="lineno"> 2534</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02535"></a><span class="lineno"> 2535</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> spaceToDepthLayer = network->AddSpaceToDepthLayer(desc, layerName.c_str());</div><div class="line"><a name="l02536"></a><span class="lineno"> 2536</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02537"></a><span class="lineno"> 2537</span> </div><div class="line"><a name="l02538"></a><span class="lineno"> 2538</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>(spaceToDepthLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02539"></a><span class="lineno"> 2539</span>  spaceToDepthLayer-><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="l02540"></a><span class="lineno"> 2540</span> </div><div class="line"><a name="l02541"></a><span class="lineno"> 2541</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="l02542"></a><span class="lineno"> 2542</span>  spaceToDepthLayer-><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="l02543"></a><span class="lineno"> 2543</span> </div><div class="line"><a name="l02544"></a><span class="lineno"> 2544</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02545"></a><span class="lineno"> 2545</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02546"></a><span class="lineno"> 2546</span> </div><div class="line"><a name="l02547"></a><span class="lineno"> 2547</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::SpaceToDepthDescriptor></a> verifier(</div><div class="line"><a name="l02548"></a><span class="lineno"> 2548</span>  layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l02549"></a><span class="lineno"> 2549</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02550"></a><span class="lineno"> 2550</span> }</div><div class="line"><a name="l02551"></a><span class="lineno"> 2551</span> </div><div class="line"><a name="l02552"></a><span class="lineno"> 2552</span> TEST_CASE(<span class="stringliteral">"SerializeSplitter"</span>)</div><div class="line"><a name="l02553"></a><span class="lineno"> 2553</span> {</div><div class="line"><a name="l02554"></a><span class="lineno"> 2554</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numViews = 3;</div><div class="line"><a name="l02555"></a><span class="lineno"> 2555</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDimensions = 4;</div><div class="line"><a name="l02556"></a><span class="lineno"> 2556</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = {1, 18, 4, 4};</div><div class="line"><a name="l02557"></a><span class="lineno"> 2557</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {1, 6, 4, 4};</div><div class="line"><a name="l02558"></a><span class="lineno"> 2558</span> </div><div class="line"><a name="l02559"></a><span class="lineno"> 2559</span>  <span class="comment">// This is modelled on how the caffe parser sets up a splitter layer to partition an input along dimension one.</span></div><div class="line"><a name="l02560"></a><span class="lineno"> 2560</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitterDimSizes[4] = {<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(inputShape[0]),</div><div class="line"><a name="l02561"></a><span class="lineno"> 2561</span>  static_cast<unsigned int>(inputShape[1]),</div><div class="line"><a name="l02562"></a><span class="lineno"> 2562</span>  <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(inputShape[2]),</div><div class="line"><a name="l02563"></a><span class="lineno"> 2563</span>  static_cast<unsigned int>(inputShape[3])};</div><div class="line"><a name="l02564"></a><span class="lineno"> 2564</span>  splitterDimSizes[1] /= numViews;</div><div class="line"><a name="l02565"></a><span class="lineno"> 2565</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">armnn::ViewsDescriptor</a> desc(numViews, numDimensions);</div><div class="line"><a name="l02566"></a><span class="lineno"> 2566</span> </div><div class="line"><a name="l02567"></a><span class="lineno"> 2567</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g < numViews; ++g)</div><div class="line"><a name="l02568"></a><span class="lineno"> 2568</span>  {</div><div class="line"><a name="l02569"></a><span class="lineno"> 2569</span>  desc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(g, 1, splitterDimSizes[1] * g);</div><div class="line"><a name="l02570"></a><span class="lineno"> 2570</span> </div><div class="line"><a name="l02571"></a><span class="lineno"> 2571</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx=0; dimIdx < 4; dimIdx++)</div><div class="line"><a name="l02572"></a><span class="lineno"> 2572</span>  {</div><div class="line"><a name="l02573"></a><span class="lineno"> 2573</span>  desc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(g, dimIdx, splitterDimSizes[dimIdx]);</div><div class="line"><a name="l02574"></a><span class="lineno"> 2574</span>  }</div><div class="line"><a name="l02575"></a><span class="lineno"> 2575</span>  }</div><div class="line"><a name="l02576"></a><span class="lineno"> 2576</span> </div><div class="line"><a name="l02577"></a><span class="lineno"> 2577</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"splitter"</span>);</div><div class="line"><a name="l02578"></a><span class="lineno"> 2578</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo(numDimensions, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02579"></a><span class="lineno"> 2579</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo(numDimensions, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02580"></a><span class="lineno"> 2580</span> </div><div class="line"><a name="l02581"></a><span class="lineno"> 2581</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02582"></a><span class="lineno"> 2582</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02583"></a><span class="lineno"> 2583</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> splitterLayer = network->AddSplitterLayer(desc, layerName.c_str());</div><div class="line"><a name="l02584"></a><span class="lineno"> 2584</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer0 = network->AddOutputLayer(0);</div><div class="line"><a name="l02585"></a><span class="lineno"> 2585</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer1 = network->AddOutputLayer(1);</div><div class="line"><a name="l02586"></a><span class="lineno"> 2586</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer2 = network->AddOutputLayer(2);</div><div class="line"><a name="l02587"></a><span class="lineno"> 2587</span> </div><div class="line"><a name="l02588"></a><span class="lineno"> 2588</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>(splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02589"></a><span class="lineno"> 2589</span>  splitterLayer-><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>(outputLayer0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02590"></a><span class="lineno"> 2590</span>  splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02591"></a><span class="lineno"> 2591</span>  splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer2-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02592"></a><span class="lineno"> 2592</span> </div><div class="line"><a name="l02593"></a><span class="lineno"> 2593</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="l02594"></a><span class="lineno"> 2594</span>  splitterLayer-><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="l02595"></a><span class="lineno"> 2595</span>  splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02596"></a><span class="lineno"> 2596</span>  splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02597"></a><span class="lineno"> 2597</span> </div><div class="line"><a name="l02598"></a><span class="lineno"> 2598</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02599"></a><span class="lineno"> 2599</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02600"></a><span class="lineno"> 2600</span> </div><div class="line"><a name="l02601"></a><span class="lineno"> 2601</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::ViewsDescriptor></a> verifier(</div><div class="line"><a name="l02602"></a><span class="lineno"> 2602</span>  layerName, {inputInfo}, {outputInfo, outputInfo, outputInfo}, desc);</div><div class="line"><a name="l02603"></a><span class="lineno"> 2603</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02604"></a><span class="lineno"> 2604</span> }</div><div class="line"><a name="l02605"></a><span class="lineno"> 2605</span> </div><div class="line"><a name="l02606"></a><span class="lineno"> 2606</span> TEST_CASE(<span class="stringliteral">"SerializeStack"</span>)</div><div class="line"><a name="l02607"></a><span class="lineno"> 2607</span> {</div><div class="line"><a name="l02608"></a><span class="lineno"> 2608</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"stack"</span>);</div><div class="line"><a name="l02609"></a><span class="lineno"> 2609</span> </div><div class="line"><a name="l02610"></a><span class="lineno"> 2610</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo ({4, 3, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02611"></a><span class="lineno"> 2611</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({4, 3, 2, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02612"></a><span class="lineno"> 2612</span> </div><div class="line"><a name="l02613"></a><span class="lineno"> 2613</span>  <a class="code" href="structarmnn_1_1_stack_descriptor.xhtml">armnn::StackDescriptor</a> descriptor(2, 2, {4, 3, 5});</div><div class="line"><a name="l02614"></a><span class="lineno"> 2614</span> </div><div class="line"><a name="l02615"></a><span class="lineno"> 2615</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02616"></a><span class="lineno"> 2616</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(0);</div><div class="line"><a name="l02617"></a><span class="lineno"> 2617</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer2 = network->AddInputLayer(1);</div><div class="line"><a name="l02618"></a><span class="lineno"> 2618</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> stackLayer = network->AddStackLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l02619"></a><span class="lineno"> 2619</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02620"></a><span class="lineno"> 2620</span> </div><div class="line"><a name="l02621"></a><span class="lineno"> 2621</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>(stackLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02622"></a><span class="lineno"> 2622</span>  inputLayer2-><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="l02623"></a><span class="lineno"> 2623</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>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02624"></a><span class="lineno"> 2624</span> </div><div class="line"><a name="l02625"></a><span class="lineno"> 2625</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>(inputTensorInfo);</div><div class="line"><a name="l02626"></a><span class="lineno"> 2626</span>  inputLayer2-><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>(inputTensorInfo);</div><div class="line"><a name="l02627"></a><span class="lineno"> 2627</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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02628"></a><span class="lineno"> 2628</span> </div><div class="line"><a name="l02629"></a><span class="lineno"> 2629</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02630"></a><span class="lineno"> 2630</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02631"></a><span class="lineno"> 2631</span> </div><div class="line"><a name="l02632"></a><span class="lineno"> 2632</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::StackDescriptor></a> verifier(</div><div class="line"><a name="l02633"></a><span class="lineno"> 2633</span>  layerName, {inputTensorInfo, inputTensorInfo}, {outputTensorInfo}, descriptor);</div><div class="line"><a name="l02634"></a><span class="lineno"> 2634</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02635"></a><span class="lineno"> 2635</span> }</div><div class="line"><a name="l02636"></a><span class="lineno"> 2636</span> </div><div class="line"><a name="l02637"></a><span class="lineno"> 2637</span> TEST_CASE(<span class="stringliteral">"SerializeStandIn"</span>)</div><div class="line"><a name="l02638"></a><span class="lineno"> 2638</span> {</div><div class="line"><a name="l02639"></a><span class="lineno"> 2639</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"standIn"</span>);</div><div class="line"><a name="l02640"></a><span class="lineno"> 2640</span> </div><div class="line"><a name="l02641"></a><span class="lineno"> 2641</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({ 1u }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02642"></a><span class="lineno"> 2642</span>  <a class="code" href="structarmnn_1_1_stand_in_descriptor.xhtml">armnn::StandInDescriptor</a> descriptor(2u, 2u);</div><div class="line"><a name="l02643"></a><span class="lineno"> 2643</span> </div><div class="line"><a name="l02644"></a><span class="lineno"> 2644</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02645"></a><span class="lineno"> 2645</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l02646"></a><span class="lineno"> 2646</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l02647"></a><span class="lineno"> 2647</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> standInLayer = network->AddStandInLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l02648"></a><span class="lineno"> 2648</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer0 = network->AddOutputLayer(0);</div><div class="line"><a name="l02649"></a><span class="lineno"> 2649</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer1 = network->AddOutputLayer(1);</div><div class="line"><a name="l02650"></a><span class="lineno"> 2650</span> </div><div class="line"><a name="l02651"></a><span class="lineno"> 2651</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>(standInLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02652"></a><span class="lineno"> 2652</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="l02653"></a><span class="lineno"> 2653</span> </div><div class="line"><a name="l02654"></a><span class="lineno"> 2654</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>(standInLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02655"></a><span class="lineno"> 2655</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="l02656"></a><span class="lineno"> 2656</span> </div><div class="line"><a name="l02657"></a><span class="lineno"> 2657</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>(outputLayer0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02658"></a><span class="lineno"> 2658</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="l02659"></a><span class="lineno"> 2659</span> </div><div class="line"><a name="l02660"></a><span class="lineno"> 2660</span>  standInLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02661"></a><span class="lineno"> 2661</span>  standInLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l02662"></a><span class="lineno"> 2662</span> </div><div class="line"><a name="l02663"></a><span class="lineno"> 2663</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02664"></a><span class="lineno"> 2664</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02665"></a><span class="lineno"> 2665</span> </div><div class="line"><a name="l02666"></a><span class="lineno"> 2666</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::StandInDescriptor></a> verifier(</div><div class="line"><a name="l02667"></a><span class="lineno"> 2667</span>  layerName, { tensorInfo, tensorInfo }, { tensorInfo, tensorInfo }, descriptor);</div><div class="line"><a name="l02668"></a><span class="lineno"> 2668</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02669"></a><span class="lineno"> 2669</span> }</div><div class="line"><a name="l02670"></a><span class="lineno"> 2670</span> </div><div class="line"><a name="l02671"></a><span class="lineno"> 2671</span> TEST_CASE(<span class="stringliteral">"SerializeStridedSlice"</span>)</div><div class="line"><a name="l02672"></a><span class="lineno"> 2672</span> {</div><div class="line"><a name="l02673"></a><span class="lineno"> 2673</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"stridedSlice"</span>);</div><div class="line"><a name="l02674"></a><span class="lineno"> 2674</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({3, 2, 3, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02675"></a><span class="lineno"> 2675</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({3, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02676"></a><span class="lineno"> 2676</span> </div><div class="line"><a name="l02677"></a><span class="lineno"> 2677</span>  <a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml">armnn::StridedSliceDescriptor</a> desc({0, 0, 1, 0}, {1, 1, 1, 1}, {1, 1, 1, 1});</div><div class="line"><a name="l02678"></a><span class="lineno"> 2678</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#ac37e49c0d6e6e54f9d2015d0f11f8ee7">m_EndMask</a> = (1 << 4) - 1;</div><div class="line"><a name="l02679"></a><span class="lineno"> 2679</span>  desc.m_ShrinkAxisMask = (1 << 1) | (1 << 2);</div><div class="line"><a name="l02680"></a><span class="lineno"> 2680</span>  desc.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>;</div><div class="line"><a name="l02681"></a><span class="lineno"> 2681</span> </div><div class="line"><a name="l02682"></a><span class="lineno"> 2682</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02683"></a><span class="lineno"> 2683</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02684"></a><span class="lineno"> 2684</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> stridedSliceLayer = network->AddStridedSliceLayer(desc, layerName.c_str());</div><div class="line"><a name="l02685"></a><span class="lineno"> 2685</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02686"></a><span class="lineno"> 2686</span> </div><div class="line"><a name="l02687"></a><span class="lineno"> 2687</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>(stridedSliceLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02688"></a><span class="lineno"> 2688</span>  stridedSliceLayer-><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="l02689"></a><span class="lineno"> 2689</span> </div><div class="line"><a name="l02690"></a><span class="lineno"> 2690</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="l02691"></a><span class="lineno"> 2691</span>  stridedSliceLayer-><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="l02692"></a><span class="lineno"> 2692</span> </div><div class="line"><a name="l02693"></a><span class="lineno"> 2693</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02694"></a><span class="lineno"> 2694</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02695"></a><span class="lineno"> 2695</span> </div><div class="line"><a name="l02696"></a><span class="lineno"> 2696</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::StridedSliceDescriptor></a> verifier(</div><div class="line"><a name="l02697"></a><span class="lineno"> 2697</span>  layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l02698"></a><span class="lineno"> 2698</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02699"></a><span class="lineno"> 2699</span> }</div><div class="line"><a name="l02700"></a><span class="lineno"> 2700</span> </div><div class="line"><a name="l02701"></a><span class="lineno"> 2701</span> TEST_CASE(<span class="stringliteral">"SerializeSubtraction"</span>)</div><div class="line"><a name="l02702"></a><span class="lineno"> 2702</span> {</div><div class="line"><a name="l02703"></a><span class="lineno"> 2703</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"subtraction"</span>);</div><div class="line"><a name="l02704"></a><span class="lineno"> 2704</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({ 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02705"></a><span class="lineno"> 2705</span> </div><div class="line"><a name="l02706"></a><span class="lineno"> 2706</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02707"></a><span class="lineno"> 2707</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l02708"></a><span class="lineno"> 2708</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l02709"></a><span class="lineno"> 2709</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> subtractionLayer = network->AddSubtractionLayer(layerName.c_str());</div><div class="line"><a name="l02710"></a><span class="lineno"> 2710</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02711"></a><span class="lineno"> 2711</span> </div><div class="line"><a name="l02712"></a><span class="lineno"> 2712</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>(subtractionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02713"></a><span class="lineno"> 2713</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>(subtractionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02714"></a><span class="lineno"> 2714</span>  subtractionLayer-><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="l02715"></a><span class="lineno"> 2715</span> </div><div class="line"><a name="l02716"></a><span class="lineno"> 2716</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>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l02717"></a><span class="lineno"> 2717</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>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l02718"></a><span class="lineno"> 2718</span>  subtractionLayer-><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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l02719"></a><span class="lineno"> 2719</span> </div><div class="line"><a name="l02720"></a><span class="lineno"> 2720</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02721"></a><span class="lineno"> 2721</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02722"></a><span class="lineno"> 2722</span> </div><div class="line"><a name="l02723"></a><span class="lineno"> 2723</span>  <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a> verifier(layerName, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, {info});</div><div class="line"><a name="l02724"></a><span class="lineno"> 2724</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02725"></a><span class="lineno"> 2725</span> }</div><div class="line"><a name="l02726"></a><span class="lineno"> 2726</span> </div><div class="line"><a name="l02727"></a><span class="lineno"> 2727</span> TEST_CASE(<span class="stringliteral">"SerializeSwitch"</span>)</div><div class="line"><a name="l02728"></a><span class="lineno"> 2728</span> {</div><div class="line"><a name="l02729"></a><span class="lineno"> 2729</span>  <span class="keyword">class </span>SwitchLayerVerifier : <span class="keyword">public</span> <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a></div><div class="line"><a name="l02730"></a><span class="lineno"> 2730</span>  {</div><div class="line"><a name="l02731"></a><span class="lineno"> 2731</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l02732"></a><span class="lineno"> 2732</span>  SwitchLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l02733"></a><span class="lineno"> 2733</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l02734"></a><span class="lineno"> 2734</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos)</div><div class="line"><a name="l02735"></a><span class="lineno"> 2735</span>  : <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a>(layerName, inputInfos, outputInfos) {}</div><div class="line"><a name="l02736"></a><span class="lineno"> 2736</span> </div><div class="line"><a name="l02737"></a><span class="lineno"> 2737</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="l02738"></a><span class="lineno"> 2738</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l02739"></a><span class="lineno"> 2739</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l02740"></a><span class="lineno"> 2740</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l02741"></a><span class="lineno"> 2741</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span> = 0)<span class="keyword"> override</span></div><div class="line"><a name="l02742"></a><span class="lineno"> 2742</span> <span class="keyword"> </span>{</div><div class="line"><a name="l02743"></a><span class="lineno"> 2743</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(descriptor, constants, <span class="keywordtype">id</span>);</div><div class="line"><a name="l02744"></a><span class="lineno"> 2744</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="l02745"></a><span class="lineno"> 2745</span>  {</div><div class="line"><a name="l02746"></a><span class="lineno"> 2746</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l02747"></a><span class="lineno"> 2747</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l02748"></a><span class="lineno"> 2748</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a14d24d90ab4ba2956e92e27890ba4c91acb17869fe51048b5a5c4c6106551a255">armnn::LayerType::Constant</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l02749"></a><span class="lineno"> 2749</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4abbc155fb2b111bf61c4f5ff892915e6b">armnn::LayerType::Switch</a>:</div><div class="line"><a name="l02750"></a><span class="lineno"> 2750</span>  {</div><div class="line"><a name="l02751"></a><span class="lineno"> 2751</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l02752"></a><span class="lineno"> 2752</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02753"></a><span class="lineno"> 2753</span>  }</div><div class="line"><a name="l02754"></a><span class="lineno"> 2754</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l02755"></a><span class="lineno"> 2755</span>  {</div><div class="line"><a name="l02756"></a><span class="lineno"> 2756</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">"Unexpected layer type in Switch test model"</span>);</div><div class="line"><a name="l02757"></a><span class="lineno"> 2757</span>  }</div><div class="line"><a name="l02758"></a><span class="lineno"> 2758</span>  }</div><div class="line"><a name="l02759"></a><span class="lineno"> 2759</span>  }</div><div class="line"><a name="l02760"></a><span class="lineno"> 2760</span>  };</div><div class="line"><a name="l02761"></a><span class="lineno"> 2761</span> </div><div class="line"><a name="l02762"></a><span class="lineno"> 2762</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"switch"</span>);</div><div class="line"><a name="l02763"></a><span class="lineno"> 2763</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({ 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l02764"></a><span class="lineno"> 2764</span> </div><div class="line"><a name="l02765"></a><span class="lineno"> 2765</span>  std::vector<float> constantData = GenerateRandomData<float>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>.GetNumElements());</div><div class="line"><a name="l02766"></a><span class="lineno"> 2766</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> constTensor(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, constantData);</div><div class="line"><a name="l02767"></a><span class="lineno"> 2767</span> </div><div class="line"><a name="l02768"></a><span class="lineno"> 2768</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02769"></a><span class="lineno"> 2769</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02770"></a><span class="lineno"> 2770</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> constantLayer = network->AddConstantLayer(constTensor, <span class="stringliteral">"constant"</span>);</div><div class="line"><a name="l02771"></a><span class="lineno"> 2771</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> switchLayer = network->AddSwitchLayer(layerName.c_str());</div><div class="line"><a name="l02772"></a><span class="lineno"> 2772</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> trueOutputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02773"></a><span class="lineno"> 2773</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> falseOutputLayer = network->AddOutputLayer(1);</div><div class="line"><a name="l02774"></a><span class="lineno"> 2774</span> </div><div class="line"><a name="l02775"></a><span class="lineno"> 2775</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>(switchLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02776"></a><span class="lineno"> 2776</span>  constantLayer-><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>(switchLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02777"></a><span class="lineno"> 2777</span>  switchLayer-><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>(trueOutputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02778"></a><span class="lineno"> 2778</span>  switchLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(falseOutputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02779"></a><span class="lineno"> 2779</span> </div><div class="line"><a name="l02780"></a><span class="lineno"> 2780</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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l02781"></a><span class="lineno"> 2781</span>  constantLayer-><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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l02782"></a><span class="lineno"> 2782</span>  switchLayer-><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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l02783"></a><span class="lineno"> 2783</span>  switchLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l02784"></a><span class="lineno"> 2784</span> </div><div class="line"><a name="l02785"></a><span class="lineno"> 2785</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02786"></a><span class="lineno"> 2786</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02787"></a><span class="lineno"> 2787</span> </div><div class="line"><a name="l02788"></a><span class="lineno"> 2788</span>  SwitchLayerVerifier verifier(layerName, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, {info, info});</div><div class="line"><a name="l02789"></a><span class="lineno"> 2789</span>  deserializedNetwork->ExecuteStrategy(verifier);</div><div class="line"><a name="l02790"></a><span class="lineno"> 2790</span> }</div><div class="line"><a name="l02791"></a><span class="lineno"> 2791</span> </div><div class="line"><a name="l02792"></a><span class="lineno"> 2792</span> TEST_CASE(<span class="stringliteral">"SerializeTranspose"</span>)</div><div class="line"><a name="l02793"></a><span class="lineno"> 2793</span> {</div><div class="line"><a name="l02794"></a><span class="lineno"> 2794</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"transpose"</span>);</div><div class="line"><a name="l02795"></a><span class="lineno"> 2795</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({4, 3, 2, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02796"></a><span class="lineno"> 2796</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({1, 2, 3, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02797"></a><span class="lineno"> 2797</span> </div><div class="line"><a name="l02798"></a><span class="lineno"> 2798</span>  <a class="code" href="structarmnn_1_1_transpose_descriptor.xhtml">armnn::TransposeDescriptor</a> descriptor(<a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a>({3, 2, 1, 0}));</div><div class="line"><a name="l02799"></a><span class="lineno"> 2799</span> </div><div class="line"><a name="l02800"></a><span class="lineno"> 2800</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02801"></a><span class="lineno"> 2801</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02802"></a><span class="lineno"> 2802</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> transposeLayer = network->AddTransposeLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l02803"></a><span class="lineno"> 2803</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02804"></a><span class="lineno"> 2804</span> </div><div class="line"><a name="l02805"></a><span class="lineno"> 2805</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>(transposeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02806"></a><span class="lineno"> 2806</span>  transposeLayer-><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="l02807"></a><span class="lineno"> 2807</span> </div><div class="line"><a name="l02808"></a><span class="lineno"> 2808</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>(inputTensorInfo);</div><div class="line"><a name="l02809"></a><span class="lineno"> 2809</span>  transposeLayer-><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>(outputTensorInfo);</div><div class="line"><a name="l02810"></a><span class="lineno"> 2810</span> </div><div class="line"><a name="l02811"></a><span class="lineno"> 2811</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02812"></a><span class="lineno"> 2812</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02813"></a><span class="lineno"> 2813</span> </div><div class="line"><a name="l02814"></a><span class="lineno"> 2814</span>  <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<armnn::TransposeDescriptor></a> verifier(</div><div class="line"><a name="l02815"></a><span class="lineno"> 2815</span>  layerName, {inputTensorInfo}, {outputTensorInfo}, descriptor);</div><div class="line"><a name="l02816"></a><span class="lineno"> 2816</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02817"></a><span class="lineno"> 2817</span> }</div><div class="line"><a name="l02818"></a><span class="lineno"> 2818</span> </div><div class="line"><a name="l02819"></a><span class="lineno"> 2819</span> TEST_CASE(<span class="stringliteral">"SerializeTransposeConvolution2d"</span>)</div><div class="line"><a name="l02820"></a><span class="lineno"> 2820</span> {</div><div class="line"><a name="l02821"></a><span class="lineno"> 2821</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"transposeConvolution2d"</span>);</div><div class="line"><a name="l02822"></a><span class="lineno"> 2822</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 7, 7, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02823"></a><span class="lineno"> 2823</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 9, 9, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02824"></a><span class="lineno"> 2824</span> </div><div class="line"><a name="l02825"></a><span class="lineno"> 2825</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l02826"></a><span class="lineno"> 2826</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l02827"></a><span class="lineno"> 2827</span> </div><div class="line"><a name="l02828"></a><span class="lineno"> 2828</span>  std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());</div><div class="line"><a name="l02829"></a><span class="lineno"> 2829</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l02830"></a><span class="lineno"> 2830</span> </div><div class="line"><a name="l02831"></a><span class="lineno"> 2831</span>  std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements());</div><div class="line"><a name="l02832"></a><span class="lineno"> 2832</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</div><div class="line"><a name="l02833"></a><span class="lineno"> 2833</span> </div><div class="line"><a name="l02834"></a><span class="lineno"> 2834</span>  <a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">armnn::TransposeConvolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l02835"></a><span class="lineno"> 2835</span>  descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l02836"></a><span class="lineno"> 2836</span>  descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l02837"></a><span class="lineno"> 2837</span>  descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l02838"></a><span class="lineno"> 2838</span>  descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l02839"></a><span class="lineno"> 2839</span>  descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l02840"></a><span class="lineno"> 2840</span>  descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l02841"></a><span class="lineno"> 2841</span>  descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l02842"></a><span class="lineno"> 2842</span>  descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l02843"></a><span class="lineno"> 2843</span> </div><div class="line"><a name="l02844"></a><span class="lineno"> 2844</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02845"></a><span class="lineno"> 2845</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02846"></a><span class="lineno"> 2846</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> convLayer =</div><div class="line"><a name="l02847"></a><span class="lineno"> 2847</span>  network->AddTransposeConvolution2dLayer(descriptor,</div><div class="line"><a name="l02848"></a><span class="lineno"> 2848</span>  weights,</div><div class="line"><a name="l02849"></a><span class="lineno"> 2849</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a>(biases),</div><div class="line"><a name="l02850"></a><span class="lineno"> 2850</span>  layerName.c_str());</div><div class="line"><a name="l02851"></a><span class="lineno"> 2851</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02852"></a><span class="lineno"> 2852</span> </div><div class="line"><a name="l02853"></a><span class="lineno"> 2853</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02854"></a><span class="lineno"> 2854</span>  convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02855"></a><span class="lineno"> 2855</span> </div><div class="line"><a name="l02856"></a><span class="lineno"> 2856</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="l02857"></a><span class="lineno"> 2857</span>  convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02858"></a><span class="lineno"> 2858</span> </div><div class="line"><a name="l02859"></a><span class="lineno"> 2859</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02860"></a><span class="lineno"> 2860</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02861"></a><span class="lineno"> 2861</span> </div><div class="line"><a name="l02862"></a><span class="lineno"> 2862</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor> constants {weights, biases};</div><div class="line"><a name="l02863"></a><span class="lineno"> 2863</span>  <a class="code" href="class_layer_verifier_base_with_descriptor_and_constants.xhtml">LayerVerifierBaseWithDescriptorAndConstants<armnn::TransposeConvolution2dDescriptor></a> verifier(</div><div class="line"><a name="l02864"></a><span class="lineno"> 2864</span>  layerName, {inputInfo}, {outputInfo}, descriptor, constants);</div><div class="line"><a name="l02865"></a><span class="lineno"> 2865</span>  deserializedNetwork-><a class="code" href="class_layer_verifier_base_with_descriptor_and_constants.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(verifier);</div><div class="line"><a name="l02866"></a><span class="lineno"> 2866</span> }</div><div class="line"><a name="l02867"></a><span class="lineno"> 2867</span> </div><div class="line"><a name="l02868"></a><span class="lineno"> 2868</span> TEST_CASE(<span class="stringliteral">"SerializeDeserializeNonLinearNetwork"</span>)</div><div class="line"><a name="l02869"></a><span class="lineno"> 2869</span> {</div><div class="line"><a name="l02870"></a><span class="lineno"> 2870</span>  <span class="keyword">class </span>ConstantLayerVerifier : <span class="keyword">public</span> <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a></div><div class="line"><a name="l02871"></a><span class="lineno"> 2871</span>  {</div><div class="line"><a name="l02872"></a><span class="lineno"> 2872</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l02873"></a><span class="lineno"> 2873</span>  ConstantLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l02874"></a><span class="lineno"> 2874</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l02875"></a><span class="lineno"> 2875</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l02876"></a><span class="lineno"> 2876</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& layerInput)</div><div class="line"><a name="l02877"></a><span class="lineno"> 2877</span>  : <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a>(layerName, inputInfos, outputInfos)</div><div class="line"><a name="l02878"></a><span class="lineno"> 2878</span>  , m_LayerInput(layerInput) {}</div><div class="line"><a name="l02879"></a><span class="lineno"> 2879</span> </div><div class="line"><a name="l02880"></a><span class="lineno"> 2880</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="l02881"></a><span class="lineno"> 2881</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l02882"></a><span class="lineno"> 2882</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l02883"></a><span class="lineno"> 2883</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l02884"></a><span class="lineno"> 2884</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span> = 0)<span class="keyword"> override</span></div><div class="line"><a name="l02885"></a><span class="lineno"> 2885</span> <span class="keyword"> </span>{</div><div class="line"><a name="l02886"></a><span class="lineno"> 2886</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(descriptor, constants, <span class="keywordtype">id</span>);</div><div class="line"><a name="l02887"></a><span class="lineno"> 2887</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="l02888"></a><span class="lineno"> 2888</span>  {</div><div class="line"><a name="l02889"></a><span class="lineno"> 2889</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l02890"></a><span class="lineno"> 2890</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l02891"></a><span class="lineno"> 2891</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f">armnn::LayerType::Addition</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l02892"></a><span class="lineno"> 2892</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a14d24d90ab4ba2956e92e27890ba4c91acb17869fe51048b5a5c4c6106551a255">armnn::LayerType::Constant</a>:</div><div class="line"><a name="l02893"></a><span class="lineno"> 2893</span>  {</div><div class="line"><a name="l02894"></a><span class="lineno"> 2894</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l02895"></a><span class="lineno"> 2895</span>  <a class="code" href="_serializer_test_utils_8cpp.xhtml#a104f74b01c30ad4a17d765309a9731bd">CompareConstTensor</a>(constants.at(0), m_LayerInput);</div><div class="line"><a name="l02896"></a><span class="lineno"> 2896</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02897"></a><span class="lineno"> 2897</span>  }</div><div class="line"><a name="l02898"></a><span class="lineno"> 2898</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l02899"></a><span class="lineno"> 2899</span>  {</div><div class="line"><a name="l02900"></a><span class="lineno"> 2900</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">"Unexpected layer type in test model"</span>);</div><div class="line"><a name="l02901"></a><span class="lineno"> 2901</span>  }</div><div class="line"><a name="l02902"></a><span class="lineno"> 2902</span>  }</div><div class="line"><a name="l02903"></a><span class="lineno"> 2903</span>  }</div><div class="line"><a name="l02904"></a><span class="lineno"> 2904</span> </div><div class="line"><a name="l02905"></a><span class="lineno"> 2905</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l02906"></a><span class="lineno"> 2906</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_LayerInput;</div><div class="line"><a name="l02907"></a><span class="lineno"> 2907</span>  };</div><div class="line"><a name="l02908"></a><span class="lineno"> 2908</span> </div><div class="line"><a name="l02909"></a><span class="lineno"> 2909</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"constant"</span>);</div><div class="line"><a name="l02910"></a><span class="lineno"> 2910</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({ 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l02911"></a><span class="lineno"> 2911</span> </div><div class="line"><a name="l02912"></a><span class="lineno"> 2912</span>  std::vector<float> constantData = GenerateRandomData<float>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>.GetNumElements());</div><div class="line"><a name="l02913"></a><span class="lineno"> 2913</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> constTensor(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, constantData);</div><div class="line"><a name="l02914"></a><span class="lineno"> 2914</span> </div><div class="line"><a name="l02915"></a><span class="lineno"> 2915</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>());</div><div class="line"><a name="l02916"></a><span class="lineno"> 2916</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* input = network->AddInputLayer(0);</div><div class="line"><a name="l02917"></a><span class="lineno"> 2917</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* add = network->AddAdditionLayer();</div><div class="line"><a name="l02918"></a><span class="lineno"> 2918</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* constant = network->AddConstantLayer(constTensor, layerName.c_str());</div><div class="line"><a name="l02919"></a><span class="lineno"> 2919</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* output = network->AddOutputLayer(0);</div><div class="line"><a name="l02920"></a><span class="lineno"> 2920</span> </div><div class="line"><a name="l02921"></a><span class="lineno"> 2921</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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02922"></a><span class="lineno"> 2922</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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02923"></a><span class="lineno"> 2923</span>  add-><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="l02924"></a><span class="lineno"> 2924</span> </div><div class="line"><a name="l02925"></a><span class="lineno"> 2925</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>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l02926"></a><span class="lineno"> 2926</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>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l02927"></a><span class="lineno"> 2927</span>  add-><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="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l02928"></a><span class="lineno"> 2928</span> </div><div class="line"><a name="l02929"></a><span class="lineno"> 2929</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02930"></a><span class="lineno"> 2930</span>  CHECK(deserializedNetwork);</div><div class="line"><a name="l02931"></a><span class="lineno"> 2931</span> </div><div class="line"><a name="l02932"></a><span class="lineno"> 2932</span>  ConstantLayerVerifier verifier(layerName, {}, {<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>}, constTensor);</div><div class="line"><a name="l02933"></a><span class="lineno"> 2933</span>  deserializedNetwork->ExecuteStrategy(verifier);</div><div class="line"><a name="l02934"></a><span class="lineno"> 2934</span> }</div><div class="line"><a name="l02935"></a><span class="lineno"> 2935</span> </div><div class="line"><a name="l02936"></a><span class="lineno"> 2936</span> }</div><div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Convolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00537">Descriptors.hpp:537</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#l00547">Descriptors.hpp:547</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="namespacearmnn_xhtml_a2d299363c9fc33334c571fa29ca4f58ca8768a6821cd735aea4f5b0df88c1fc6a"><div class="ttname"><a href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca8768a6821cd735aea4f5b0df88c1fc6a">armnn::ComparisonOperation::Greater</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Convolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00549">Descriptors.hpp:549</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#l00217">Descriptors.hpp:217</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#l00066">INetwork.hpp:66</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_ab509802c659de19929f18bad14a35c58"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ab509802c659de19929f18bad14a35c58">armnn::DetectionPostProcessDescriptor::m_ScaleW</a></div><div class="ttdeci">float m_ScaleW</div><div class="ttdoc">Center size encoding scale weight. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00728">Descriptors.hpp:728</a></div></div> +<div class="ttc" id="class_layer_verifier_base_with_descriptor_xhtml"><div class="ttname"><a href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8hpp_source.xhtml#l00051">SerializerTestUtils.hpp:51</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</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#l00673">Descriptors.hpp:673</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#l01421">Descriptors.hpp:1421</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#l00191">Tensor.hpp:191</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::DepthwiseConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00663">Descriptors.hpp:663</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="namespacearmnn_xhtml_a2d299363c9fc33334c571fa29ca4f58caf5f286e73bda105e538310b3190f75c5"><div class="ttname"><a href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58caf5f286e73bda105e538310b3190f75c5">armnn::ComparisonOperation::Equal</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3"><div class="ttname"><a href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</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#l01004">Descriptors.hpp:1004</a></div></div> +<div class="ttc" id="_serializer_test_utils_8cpp_xhtml_a59d03e40f8f051241e46091cca50d31f"><div class="ttname"><a href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a></div><div class="ttdeci">armnn::INetworkPtr DeserializeNetwork(const std::string &serializerString)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8cpp_source.xhtml#l00153">SerializerTestUtils.cpp:153</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_a11d5c25face9b54e90f79ee8bdc1d0fb"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#a11d5c25face9b54e90f79ee8bdc1d0fb">armnn::Convolution3dDescriptor::m_PadBack</a></div><div class="ttdeci">uint32_t m_PadBack</div><div class="ttdoc">Padding back value in the depth dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00604">Descriptors.hpp:604</a></div></div> +<div class="ttc" id="_deprecated_8hpp_xhtml_ab66a241a0ed3ee89c866e777b035d0ed"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_BEGIN</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00033">Deprecated.hpp:33</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::DepthwiseConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00675">Descriptors.hpp:675</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div> +<div class="ttc" id="structarmnn_1_1_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#l00089">Descriptors.hpp:89</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_ae64523937ea910030ad66fee6fddd51f"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae64523937ea910030ad66fee6fddd51f">armnn::DetectionPostProcessDescriptor::m_ScaleX</a></div><div class="ttdeci">float m_ScaleX</div><div class="ttdoc">Center size encoding scale x. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00724">Descriptors.hpp:724</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_a281fcaec86e17c97f7b8402633f6b55a"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">armnn::FullyConnectedDescriptor::m_TransposeWeightMatrix</a></div><div class="ttdeci">bool m_TransposeWeightMatrix</div><div class="ttdoc">Enable/disable transpose weight matrix. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00493">Descriptors.hpp:493</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a2da4db0140d1a6dc69c9c82e9ef5379ea103aa83df42877d5f9baeafdbf620b55"><div class="ttname"><a href="namespacearmnn.xhtml#a2da4db0140d1a6dc69c9c82e9ef5379ea103aa83df42877d5f9baeafdbf620b55">armnn::LogicalBinaryOperation::LogicalAnd</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution3dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00606">Descriptors.hpp:606</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#l00499">Descriptors.hpp:499</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="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::DepthwiseConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00657">Descriptors.hpp:657</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#l01462">Descriptors.hpp:1462</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="_quantized_lstm_params_8hpp_xhtml"><div class="ttname"><a href="_quantized_lstm_params_8hpp.xhtml">QuantizedLstmParams.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a869254cb56968986a78a79e1d6d4a86b"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">armnn::ResizeDescriptor::m_Method</a></div><div class="ttdeci">ResizeMethod m_Method</div><div class="ttdoc">The Interpolation method to use (Bilinear, NearestNeighbor). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00993">Descriptors.hpp:993</a></div></div> +<div class="ttc" id="structarmnn_1_1_instance_normalization_descriptor_xhtml_a5e078fd505aef7bccaa05c8058e096cc"><div class="ttname"><a href="structarmnn_1_1_instance_normalization_descriptor.xhtml#a5e078fd505aef7bccaa05c8058e096cc">armnn::InstanceNormalizationDescriptor::m_Gamma</a></div><div class="ttdeci">float m_Gamma</div><div class="ttdoc">Gamma, the scale scalar value applied for the normalized tensor. Defaults to 1.0. ...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00830">Descriptors.hpp:830</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#l00163">Descriptors.hpp:163</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6"><div class="ttname"><a href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a></div><div class="ttdoc">The padding fields don&#39;t count and are ignored. </div></div> +<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml_a11c821c7524251004a72ed13c510853c"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">armnn::BatchNormalizationDescriptor::m_Eps</a></div><div class="ttdeci">float m_Eps</div><div class="ttdoc">Value to add to the variance. Used to avoid dividing by zero. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00806">Descriptors.hpp:806</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#l00081">Descriptors.hpp:81</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a7e2f87544b8bc7e497e1dec8d3ca4055"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7e2f87544b8bc7e497e1dec8d3ca4055">armnn::DetectionPostProcessDescriptor::m_DetectionsPerClass</a></div><div class="ttdeci">uint32_t m_DetectionsPerClass</div><div class="ttdoc">Detections per classes, used in Regular NMS. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00714">Descriptors.hpp:714</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::BatchToSpaceNdDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00867">Descriptors.hpp:867</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Convolution3dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00600">Descriptors.hpp:600</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution3dDescriptor::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#l00618">Descriptors.hpp:618</a></div></div> +<div class="ttc" id="structarmnn_1_1_logical_binary_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_logical_binary_descriptor.xhtml">armnn::LogicalBinaryDescriptor</a></div><div class="ttdoc">A LogicalBinaryDescriptor for the LogicalBinaryLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01492">Descriptors.hpp:1492</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::DataType::Signed64</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Convolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00533">Descriptors.hpp:533</a></div></div> +<div class="ttc" id="class_layer_verifier_base_with_descriptor_xhtml_a49f7f1098adb86fd2197d9aee3924de2"><div class="ttname"><a href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">LayerVerifierBaseWithDescriptor::ExecuteStrategy</a></div><div class="ttdeci">void ExecuteStrategy(const armnn::IConnectableLayer *layer, const armnn::BaseDescriptor &descriptor, const std::vector< armnn::ConstTensor > &constants, const char *name, const armnn::LayerBindingId id=0) override</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8hpp_source.xhtml#l00061">SerializerTestUtils.hpp:61</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::NormalizationDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00770">Descriptors.hpp:770</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__quick__start_8dox_source.xhtml#l00006">01_00_quick_start.dox:6</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Pooling3dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCDHW, NDHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00464">Descriptors.hpp:464</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="_lstm_params_8hpp_xhtml"><div class="ttname"><a href="_lstm_params_8hpp.xhtml">LstmParams.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::TransposeConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01456">Descriptors.hpp:1456</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_convolution2d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::Convolution2dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation along y axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00545">Descriptors.hpp:545</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_ac37e49c0d6e6e54f9d2015d0f11f8ee7"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#ac37e49c0d6e6e54f9d2015d0f11f8ee7">armnn::StridedSliceDescriptor::m_EndMask</a></div><div class="ttdeci">int32_t m_EndMask</div><div class="ttdoc">End mask value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01334">Descriptors.hpp:1334</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#l01056">Descriptors.hpp:1056</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a68be4837f6c739877233e527a996dd00"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a68be4837f6c739877233e527a996dd00">armnn::LayerType::Merge</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::Convolution3dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation along x axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00612">Descriptors.hpp:612</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::DepthwiseConvolution2dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation factor value for height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00671">Descriptors.hpp:671</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#l00840">Descriptors.hpp:840</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#l00290">Types.hpp:290</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="structarmnn_1_1_convolution3d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution3dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00608">Descriptors.hpp:608</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 ResizeBilinearDescriptor for the ResizeBilinearLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00966">Descriptors.hpp:966</a></div></div> +<div class="ttc" id="structarmnn_1_1_l2_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::L2NormalizationDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00789">Descriptors.hpp:789</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a9ae2c9796692ebeafe19a4d3f09c8ea8"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a9ae2c9796692ebeafe19a4d3f09c8ea8">armnn::DetectionPostProcessDescriptor::m_MaxClassesPerDetection</a></div><div class="ttdeci">uint32_t m_MaxClassesPerDetection</div><div class="ttdoc">Maximum numbers of classes per detection, used in Fast NMS. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00712">Descriptors.hpp:712</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_mean_descriptor_xhtml_a1f0d67b087c491248bd1cde3ff995a95"><div class="ttname"><a href="structarmnn_1_1_mean_descriptor.xhtml#a1f0d67b087c491248bd1cde3ff995a95">armnn::MeanDescriptor::m_Axis</a></div><div class="ttdeci">std::vector< unsigned int > m_Axis</div><div class="ttdoc">Values for the dimensions to reduce. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01171">Descriptors.hpp:1171</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#l01232">Descriptors.hpp:1232</a></div></div> +<div class="ttc" id="structarmnn_1_1_space_to_batch_nd_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::SpaceToBatchNdDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01052">Descriptors.hpp:1052</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a6dac966f265381903c8ef4f392becced"><div class="ttname"><a href="namespacearmnn.xhtml#a6dac966f265381903c8ef4f392becced">armnn::GetUnaryOperationAsCString</a></div><div class="ttdeci">constexpr char const * GetUnaryOperationAsCString(UnaryOperation operation)</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00071">TypesUtils.hpp:71</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Convolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00535">Descriptors.hpp:535</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_ae72089bcab60ac175557f4241b16a014"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae72089bcab60ac175557f4241b16a014">armnn::DetectionPostProcessDescriptor::m_MaxDetections</a></div><div class="ttdeci">uint32_t m_MaxDetections</div><div class="ttdoc">Maximum numbers of detections. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00710">Descriptors.hpp:710</a></div></div> +<div class="ttc" id="structarmnn_1_1_pad_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a></div><div class="ttdoc">A PadDescriptor for the PadLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01177">Descriptors.hpp:1177</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00539">Descriptors.hpp:539</a></div></div> +<div class="ttc" id="_types_utils_8hpp_xhtml"><div class="ttname"><a href="_types_utils_8hpp.xhtml">TypesUtils.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::DepthwiseConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00665">Descriptors.hpp:665</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#l00048">Types.hpp:48</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a53c8a7f33a40e1e240256bcfcf41b101"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a53c8a7f33a40e1e240256bcfcf41b101">armnn::DetectionPostProcessDescriptor::m_NmsIouThreshold</a></div><div class="ttdeci">float m_NmsIouThreshold</div><div class="ttdoc">Intersection over union threshold. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00718">Descriptors.hpp:718</a></div></div> +<div class="ttc" id="_deprecated_8hpp_xhtml_ad762b11b48e5c1d1c1743f529485728a"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_END</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00034">Deprecated.hpp:34</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::DepthwiseConvolution2dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation factor value for width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00669">Descriptors.hpp:669</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::DepthwiseConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00661">Descriptors.hpp:661</a></div></div> +<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml_aae0893695f5803a3517985c7cb1ccb2e"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">armnn::ViewsDescriptor::SetViewSize</a></div><div class="ttdeci">Status SetViewSize(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the size of the views. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00315">Descriptors.cpp:315</a></div></div> +<div class="ttc" id="class_layer_verifier_base_with_descriptor_and_constants_xhtml_a49f7f1098adb86fd2197d9aee3924de2"><div class="ttname"><a href="class_layer_verifier_base_with_descriptor_and_constants.xhtml#a49f7f1098adb86fd2197d9aee3924de2">LayerVerifierBaseWithDescriptorAndConstants::ExecuteStrategy</a></div><div class="ttdeci">void ExecuteStrategy(const armnn::IConnectableLayer *layer, const armnn::BaseDescriptor &descriptor, const std::vector< armnn::ConstTensor > &constants, const char *name, const armnn::LayerBindingId id=0) override</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8hpp_source.xhtml#l00119">SerializerTestUtils.hpp:119</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="structarmnn_1_1_l2_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_descriptor.xhtml">armnn::L2NormalizationDescriptor</a></div><div class="ttdoc">A L2NormalizationDescriptor for the L2NormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00774">Descriptors.hpp:774</a></div></div> +<div class="ttc" id="class_layer_verifier_base_xhtml_a56e5da77beb8c601e09bf78371b95828"><div class="ttname"><a href="class_layer_verifier_base.xhtml#a56e5da77beb8c601e09bf78371b95828">LayerVerifierBase::VerifyNameAndConnections</a></div><div class="ttdeci">void VerifyNameAndConnections(const armnn::IConnectableLayer *layer, const char *name)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8cpp_source.xhtml#l00040">SerializerTestUtils.cpp:40</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#l00067">Descriptors.hpp:67</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#l00174">Descriptors.hpp:174</a></div></div> +<div class="ttc" id="structarmnn_1_1_reduce_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_reduce_descriptor.xhtml">armnn::ReduceDescriptor</a></div><div class="ttdoc">A ReduceDescriptor for the REDUCE operators. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01512">Descriptors.hpp:1512</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="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#l00468">Descriptors.hpp:468</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::FullyConnectedDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00491">Descriptors.hpp:491</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#l00327">Tensor.hpp:327</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_adcf5037208faac36c0788239a073f75c"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">armnn::ResizeDescriptor::m_TargetWidth</a></div><div class="ttdeci">uint32_t m_TargetWidth</div><div class="ttdoc">Target width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00988">Descriptors.hpp:988</a></div></div> +<div class="ttc" id="structarmnn_1_1_gather_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_gather_descriptor.xhtml">armnn::GatherDescriptor</a></div><div class="ttdoc">A GatherDescriptor for the GatherLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00909">Descriptors.hpp:909</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a3a04b0ccee4bb2f21721ee5045e83df4"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a3a04b0ccee4bb2f21721ee5045e83df4">armnn::DetectionPostProcessDescriptor::m_NumClasses</a></div><div class="ttdeci">uint32_t m_NumClasses</div><div class="ttdoc">Number of classes. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00720">Descriptors.hpp:720</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a4022d5107338aaf5eb7abebf78a1360b"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a4022d5107338aaf5eb7abebf78a1360b">armnn::ResizeDescriptor::m_HalfPixelCenters</a></div><div class="ttdeci">bool m_HalfPixelCenters</div><div class="ttdoc">Half Pixel Centers. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00999">Descriptors.hpp:999</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a2d299363c9fc33334c571fa29ca4f58ca19bb0af2c3c530538cb41aff7f235b96"><div class="ttname"><a href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca19bb0af2c3c530538cb41aff7f235b96">armnn::ComparisonOperation::NotEqual</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::TransposeConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01454">Descriptors.hpp:1454</a></div></div> +<div class="ttc" id="class_layer_verifier_base_with_descriptor_and_constants_xhtml"><div class="ttname"><a href="class_layer_verifier_base_with_descriptor_and_constants.xhtml">LayerVerifierBaseWithDescriptorAndConstants</a></div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8hpp_source.xhtml#l00108">SerializerTestUtils.hpp:108</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#l00473">Tensor.cpp:473</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#l01262">Descriptors.hpp:1262</a></div></div> +<div class="ttc" id="class_layer_verifier_base_xhtml_a39bdf94af97d9484d02649b749da327c"><div class="ttname"><a href="class_layer_verifier_base.xhtml#a39bdf94af97d9484d02649b749da327c">LayerVerifierBase::LayerVerifierBase</a></div><div class="ttdeci">LayerVerifierBase(const std::string &layerName, const std::vector< armnn::TensorInfo > &inputInfos, const std::vector< armnn::TensorInfo > &outputInfos)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8cpp_source.xhtml#l00013">SerializerTestUtils.cpp:13</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a7ed9bc7c26df67d274d5dd4cd83adf0f"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7ed9bc7c26df67d274d5dd4cd83adf0f">armnn::DetectionPostProcessDescriptor::m_UseRegularNms</a></div><div class="ttdeci">bool m_UseRegularNms</div><div class="ttdoc">Use Regular NMS. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00722">Descriptors.hpp:722</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_a83ca447892f460dabaa2f87d3dc3db61"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#a83ca447892f460dabaa2f87d3dc3db61">armnn::Convolution3dDescriptor::m_PadFront</a></div><div class="ttdeci">uint32_t m_PadFront</div><div class="ttdoc">Padding front value in the depth dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00602">Descriptors.hpp:602</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::TransposeConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01464">Descriptors.hpp:1464</a></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="namespacearmnn_xhtml_a14d24d90ab4ba2956e92e27890ba4c91a74de3e45e4491e956e8dc18d841d9b00"><div class="ttname"><a href="namespacearmnn.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a74de3e45e4491e956e8dc18d841d9b00">armnn::PaddingMode::Reflect</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Convolution3dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00594">Descriptors.hpp:594</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#l00990">Descriptors.hpp:990</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#l01209">Descriptors.hpp:1209</a></div></div> +<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00295">Types.hpp:295</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00541">Descriptors.hpp:541</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml">armnn::Convolution3dDescriptor</a></div><div class="ttdoc">A Convolution3dDescriptor for the Convolution3dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00553">Descriptors.hpp:553</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Convolution3dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00596">Descriptors.hpp:596</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="structarmnn_1_1_space_to_depth_descriptor_xhtml_a6c6b8957f1e176867e5fb05b1a1a1486"><div class="ttname"><a href="structarmnn_1_1_space_to_depth_descriptor.xhtml#a6c6b8957f1e176867e5fb05b1a1a1486">armnn::SpaceToDepthDescriptor::m_BlockSize</a></div><div class="ttdeci">unsigned int m_BlockSize</div><div class="ttdoc">Scalar specifying the input block size. It must be >= 1. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01073">Descriptors.hpp:1073</a></div></div> +<div class="ttc" id="structarmnn_1_1_pad_descriptor_xhtml_a66f2c989f51ab6116de4380390250b69"><div class="ttname"><a href="structarmnn_1_1_pad_descriptor.xhtml#a66f2c989f51ab6116de4380390250b69">armnn::PadDescriptor::m_PaddingMode</a></div><div class="ttdeci">PaddingMode m_PaddingMode</div><div class="ttdoc">Specifies the Padding mode (Constant, Reflect or Symmetric) </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01205">Descriptors.hpp:1205</a></div></div> +<div class="ttc" id="_i_network_8hpp_xhtml"><div class="ttname"><a href="_i_network_8hpp.xhtml">INetwork.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Pooling2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00388">Descriptors.hpp:388</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml">armnn::Pooling3dDescriptor</a></div><div class="ttdoc">A Pooling3dDescriptor for the Pooling3dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00392">Descriptors.hpp:392</a></div></div> +<div class="ttc" id="structarmnn_1_1_reduce_descriptor_xhtml_aa1c6fc8c96404252f1072632fc5acb59"><div class="ttname"><a href="structarmnn_1_1_reduce_descriptor.xhtml#aa1c6fc8c96404252f1072632fc5acb59">armnn::ReduceDescriptor::m_vAxis</a></div><div class="ttdeci">std::vector< uint32_t > m_vAxis</div><div class="ttdoc">The indices of the dimensions to reduce. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01530">Descriptors.hpp:1530</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_aa61510cbd529870182e918ac6e8b9d72"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#aa61510cbd529870182e918ac6e8b9d72">armnn::DetectionPostProcessDescriptor::m_ScaleH</a></div><div class="ttdeci">float m_ScaleH</div><div class="ttdoc">Center size encoding scale height. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00730">Descriptors.hpp:730</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#l01024">Descriptors.hpp:1024</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Convolution3dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NDHWC, NCDHW). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00620">Descriptors.hpp:620</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::Convolution2dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation along x axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00543">Descriptors.hpp:543</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::TransposeConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01450">Descriptors.hpp:1450</a></div></div> +<div class="ttc" id="_serializer_test_utils_8hpp_xhtml"><div class="ttname"><a href="_serializer_test_utils_8hpp.xhtml">SerializerTestUtils.hpp</a></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="structarmnn_1_1_resize_descriptor_xhtml_ae1a4b3b6c60552509b89747cebb900a2"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#ae1a4b3b6c60552509b89747cebb900a2">armnn::ResizeDescriptor::m_AlignCorners</a></div><div class="ttdeci">bool m_AlignCorners</div><div class="ttdoc">Aligned corners. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00997">Descriptors.hpp:997</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::TransposeConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01458">Descriptors.hpp:1458</a></div></div> +<div class="ttc" id="structarmnn_1_1_gather_descriptor_xhtml_a35d11c7d509d1adbae1ae01c58394a7f"><div class="ttname"><a href="structarmnn_1_1_gather_descriptor.xhtml#a35d11c7d509d1adbae1ae01c58394a7f">armnn::GatherDescriptor::m_Axis</a></div><div class="ttdeci">int32_t m_Axis</div><div class="ttdoc">The axis in params to gather indices from. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00925">Descriptors.hpp:925</a></div></div> +<div class="ttc" id="structarmnn_1_1_elementwise_unary_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_elementwise_unary_descriptor.xhtml">armnn::ElementwiseUnaryDescriptor</a></div><div class="ttdoc">A ElementwiseUnaryDescriptor for the ElementwiseUnaryLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00109">Descriptors.hpp:109</a></div></div> +<div class="ttc" id="_i_deserializer_8hpp_xhtml"><div class="ttname"><a href="_i_deserializer_8hpp.xhtml">IDeserializer.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::DepthwiseConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00667">Descriptors.hpp:667</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4abbc155fb2b111bf61c4f5ff892915e6b"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4abbc155fb2b111bf61c4f5ff892915e6b">armnn::LayerType::Switch</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::TransposeConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01460">Descriptors.hpp:1460</a></div></div> +<div class="ttc" id="_serializer_tests_8cpp_xhtml_afad5df20f3fea32614ad88b00f5849fc"><div class="ttname"><a href="_serializer_tests_8cpp.xhtml#afad5df20f3fea32614ad88b00f5849fc">TEST_SUITE</a></div><div class="ttdeci">TEST_SUITE("SerializerTests")</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_tests_8cpp_source.xhtml#l00024">SerializerTests.cpp:24</a></div></div> +<div class="ttc" id="classarmnn_1_1_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_exception.xhtml">armnn::Exception</a></div><div class="ttdoc">Base class for all ArmNN exceptions so that users can filter to just those. </div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00046">Exceptions.hpp:46</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="_descriptors_8hpp_xhtml"><div class="ttname"><a href="_descriptors_8hpp.xhtml">Descriptors.hpp</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f"><div class="ttname"><a href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f">armnn::ResizeMethod::Bilinear</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Convolution3dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00598">Descriptors.hpp:598</a></div></div> +<div class="ttc" id="class_layer_verifier_base_xhtml"><div class="ttname"><a href="class_layer_verifier_base.xhtml">LayerVerifierBase</a></div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8hpp_source.xhtml#l00024">SerializerTestUtils.hpp:24</a></div></div> +<div class="ttc" id="_serializer_test_utils_8cpp_xhtml_a104f74b01c30ad4a17d765309a9731bd"><div class="ttname"><a href="_serializer_test_utils_8cpp.xhtml#a104f74b01c30ad4a17d765309a9731bd">CompareConstTensor</a></div><div class="ttdeci">void CompareConstTensor(const armnn::ConstTensor &tensor1, const armnn::ConstTensor &tensor2)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8cpp_source.xhtml#l00122">SerializerTestUtils.cpp:122</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="structarmnn_1_1_mean_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_mean_descriptor.xhtml">armnn::MeanDescriptor</a></div><div class="ttdoc">A MeanDescriptor for the MeanLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01153">Descriptors.hpp:1153</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a1cfaa710db2a54673b21d2ea2da757c8"><div class="ttname"><a href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8">armnn::UnaryOperation</a></div><div class="ttdeci">UnaryOperation</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00124">Types.hpp:124</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::TransposeConvolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01452">Descriptors.hpp:1452</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_descriptor.xhtml">armnn::TransposeDescriptor</a></div><div class="ttdoc">A TransposeDescriptor for the TransposeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01471">Descriptors.hpp:1471</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#l01284">Descriptors.hpp:1284</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_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_a9a2af2f8c4af4f9efa8e79417d505ac4aaa020331bb30d2fa2ecf7c3a0777823f"><div class="ttname"><a href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaa020331bb30d2fa2ecf7c3a0777823f">armnn::ResizeMethod::NearestNeighbor</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_abc05539fc6e7907f32ef0fb242e3b3b0aa0ec87054b5e5b7847d0d8780a01a3d5"><div class="ttname"><a href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0aa0ec87054b5e5b7847d0d8780a01a3d5">armnn::ReduceOperation::Sum</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a7a2156ec7d9c012ce00bbcc6afcb9028"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7a2156ec7d9c012ce00bbcc6afcb9028">armnn::DetectionPostProcessDescriptor::m_ScaleY</a></div><div class="ttdeci">float m_ScaleY</div><div class="ttdoc">Center size encoding scale y. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00726">Descriptors.hpp:726</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a733ae6b70d0bfa43433c3e7606992328"><div class="ttname"><a href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a></div><div class="ttdeci">OriginsDescriptor CreateDescriptorForConcatenation(TensorShapeIt first, TensorShapeIt last, unsigned int concatenationDimension)</div><div class="ttdoc">Convenience template to create an OriginsDescriptor to use when creating a ConcatLayer for performing...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00261">Descriptors.hpp:261</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a4392dd6b4862cc9cf95ae8f1001ba592"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a4392dd6b4862cc9cf95ae8f1001ba592">armnn::DetectionPostProcessDescriptor::m_NmsScoreThreshold</a></div><div class="ttdeci">float m_NmsScoreThreshold</div><div class="ttdoc">NMS score threshold. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00716">Descriptors.hpp:716</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#l00241">INetwork.hpp:241</a></div></div> +<div class="ttc" id="classarmnn_deserializer_1_1_i_deserializer_xhtml"><div class="ttname"><a href="classarmnn_deserializer_1_1_i_deserializer.xhtml">armnnDeserializer::IDeserializer</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_deserializer_8hpp_source.xhtml#l00027">IDeserializer.hpp:27</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_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</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#l00332">Descriptors.hpp:332</a></div></div> +<div class="ttc" id="_serializer_test_utils_8cpp_xhtml_a228162aa622e2e39abb4f498c761ab5e"><div class="ttname"><a href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a></div><div class="ttdeci">std::string SerializeNetwork(const armnn::INetwork &network)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8cpp_source.xhtml#l00159">SerializerTestUtils.cpp:159</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a></div><div class="ttdoc">A NormalizationDescriptor for the NormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00734">Descriptors.hpp:734</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::ResizeDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00995">Descriptors.hpp:995</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml">armnn::DetectionPostProcessDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00678">Descriptors.hpp:678</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#l00812">Descriptors.hpp:812</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a14d24d90ab4ba2956e92e27890ba4c91acb17869fe51048b5a5c4c6106551a255"><div class="ttname"><a href="namespacearmnn.xhtml#a14d24d90ab4ba2956e92e27890ba4c91acb17869fe51048b5a5c4c6106551a255">armnn::PaddingMode::Constant</a></div></div> +<div class="ttc" id="structarmnn_1_1_channel_shuffle_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_channel_shuffle_descriptor.xhtml">armnn::ChannelShuffleDescriptor</a></div><div class="ttdoc">A ChannelShuffleDescriptor for the ChannelShuffle operator. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01536">Descriptors.hpp:1536</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a4dd0194b114cbf51da5b3a72569863ef"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a4dd0194b114cbf51da5b3a72569863ef">armnn::DataLayout::NDHWC</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#l00476">Network.cpp:476</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_a16543bce17aa2e4d6e81c88f74227192"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#a16543bce17aa2e4d6e81c88f74227192">armnn::Convolution3dDescriptor::m_DilationZ</a></div><div class="ttdeci">uint32_t m_DilationZ</div><div class="ttdoc">Dilation along z axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00616">Descriptors.hpp:616</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#l00150">Descriptors.hpp:150</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_a5164336f6a1b15be0d434a6bbf7289da"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#a5164336f6a1b15be0d434a6bbf7289da">armnn::Convolution3dDescriptor::m_StrideZ</a></div><div class="ttdeci">uint32_t m_StrideZ</div><div class="ttdoc">Stride value when proceeding through input for the depth dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00610">Descriptors.hpp:610</a></div></div> +<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml_a2b125117aa61f9baf3a9cb8658aa61a2"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">armnn::ViewsDescriptor::SetViewOriginCoord</a></div><div class="ttdeci">Status SetViewOriginCoord(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the view origin coordinates. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00310">Descriptors.cpp:310</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#l00624">Descriptors.hpp:624</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::Convolution3dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation along y axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00614">Descriptors.hpp:614</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#l00890">Descriptors.hpp:890</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#l00793">Descriptors.hpp:793</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Convolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00531">Descriptors.hpp:531</a></div></div> +<div class="ttc" id="class_layer_verifier_base_xhtml_a49f7f1098adb86fd2197d9aee3924de2"><div class="ttname"><a href="class_layer_verifier_base.xhtml#a49f7f1098adb86fd2197d9aee3924de2">LayerVerifierBase::ExecuteStrategy</a></div><div class="ttdeci">void ExecuteStrategy(const armnn::IConnectableLayer *layer, const armnn::BaseDescriptor &descriptor, const std::vector< armnn::ConstTensor > &constants, const char *name, const armnn::LayerBindingId id=0) override</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8cpp_source.xhtml#l00021">SerializerTestUtils.cpp:21</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="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#l00129">Descriptors.hpp:129</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::DepthwiseConvolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00659">Descriptors.hpp:659</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_a2d3dcfc10f90adedc995b64211dab6e9"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#a2d3dcfc10f90adedc995b64211dab6e9">armnn::FullyConnectedDescriptor::m_ConstantWeights</a></div><div class="ttdeci">bool m_ConstantWeights</div><div class="ttdoc">Enable/disable constant weights and biases. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00495">Descriptors.hpp:495</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_bff0d59bec81fb3d13742442d8f4421d.xhtml">armnnSerializer</a></li><li class="navelem"><a class="el" href="dir_fa9774f03679f86fc845ac51a8a81eba.xhtml">test</a></li><li class="navelem"><a class="el" href="_serializer_tests_8cpp.xhtml">SerializerTests.cpp</a></li> + <li class="footer">Generated on Fri Jun 17 2022 13:20:20 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> |