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| author | James Ward <james.ward@arm.com> | 2020-11-26 11:08:12 +0000 |
|---|---|---|
| committer | Colm Donelan <colm.donelan@arm.com> | 2020-11-26 14:39:49 +0000 |
| commit | fb14ebbd68e04876809145296af96f6f41857418 (patch) | |
| tree | 7eacc90dd6c79f3a6eae6e3c7b04d3a04161c794 /20.11/_fuse_activation_tests_8cpp_source.xhtml | |
| parent | 4840dfb7543d66652dc11c5ff39c8f5c1e2f9370 (diff) | |
| download | armnn-fb14ebbd68e04876809145296af96f6f41857418.tar.gz | |
IVGCVSW-5348 Update Doxygen Docu
* Update Doxygen Documentation for 20.11 release
Signed-off-by: James Ward <james.ward@arm.com>
Change-Id: Ib47edac7923a642a277b1169d1085e5622021dc0
Diffstat (limited to '20.11/_fuse_activation_tests_8cpp_source.xhtml')
| -rw-r--r-- | 20.11/_fuse_activation_tests_8cpp_source.xhtml | 204 |
1 files changed, 204 insertions, 0 deletions
diff --git a/20.11/_fuse_activation_tests_8cpp_source.xhtml b/20.11/_fuse_activation_tests_8cpp_source.xhtml new file mode 100644 index 0000000000..01611a3bbb --- /dev/null +++ b/20.11/_fuse_activation_tests_8cpp_source.xhtml @@ -0,0 +1,204 @@ +<!-- Copyright (c) 2020 ARM Limited. --> +<!-- --> +<!-- SPDX-License-Identifier: MIT --> +<!-- --> +<!-- HTML header for doxygen 1.8.13--> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> +<html xmlns="http://www.w3.org/1999/xhtml"> +<head> +<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> +<meta http-equiv="X-UA-Compatible" content="IE=9"/> +<meta name="generator" content="Doxygen 1.8.13"/> +<meta name="robots" content="NOINDEX, NOFOLLOW" /> +<meta name="viewport" content="width=device-width, initial-scale=1"/> +<title>ArmNN: src/armnn/test/optimizations/FuseActivationTests.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"> + $(document).ready(initResizable); +</script> +<link href="search/search.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="search/searchdata.js"></script> +<script type="text/javascript" src="search/search.js"></script> +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + extensions: ["tex2jax.js"], + jax: ["input/TeX","output/HTML-CSS"], +}); +</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> +<link href="doxygen.css" rel="stylesheet" type="text/css" /> +<link href="stylesheet.css" rel="stylesheet" type="text/css"/> +</head> +<body> +<div id="top"><!-- do not remove this div, it is closed by doxygen! --> +<div id="titlearea"> +<table cellspacing="0" cellpadding="0"> + <tbody> + <tr style="height: 56px;"> + <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/> + <td style="padding-left: 0.5em;"> + <div id="projectname"> +  <span id="projectnumber">20.11</span> + </div> + </td> + </tr> + </tbody> +</table> +</div> +<!-- end header part --> +<!-- Generated by Doxygen 1.8.13 --> +<script type="text/javascript"> +var searchBox = new SearchBox("searchBox", "search",false,'Search'); +</script> +<script type="text/javascript" src="menudata.js"></script> +<script type="text/javascript" src="menu.js"></script> +<script type="text/javascript"> +$(function() { + initMenu('',true,false,'search.php','Search'); + $(document).ready(function() { init_search(); }); +}); +</script> +<div id="main-nav"></div> +</div><!-- top --> +<div id="side-nav" class="ui-resizable side-nav-resizable"> + <div id="nav-tree"> + <div id="nav-tree-contents"> + <div id="nav-sync" class="sync"></div> + </div> + </div> + <div id="splitbar" style="-moz-user-select:none;" + class="ui-resizable-handle"> + </div> +</div> +<script type="text/javascript"> +$(document).ready(function(){initNavTree('_fuse_activation_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">FuseActivationTests.cpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_fuse_activation_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 © 2020 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 "<a class="code" href="_layers_fwd_8hpp.xhtml">LayersFwd.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_network_8hpp.xhtml">Network.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_resolve_type_8hpp.xhtml">ResolveType.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="_test_utils_8hpp.xhtml">test/TestUtils.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <boost/test/unit_test.hpp></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <<a class="code" href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a>></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <string></span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> </div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</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> <a class="code" href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a>(<a class="code" href="classarmnn_1_1_optimizer.xhtml">Optimizer</a>)</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">namespace</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> {</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="keyword">const</span> <span class="keywordtype">float</span> g_qScale = 1.0f;</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="keyword">const</span> int32_t g_qOffset = 0;</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> <span class="keyword">template</span><<span class="keyword">typename</span> T></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> std::vector<T> GetVector(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> size, <span class="keywordtype">float</span> initial, <span class="keywordtype">float</span> increment)</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> {</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  std::vector<float> typeVector(size, initial);</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  std::vector<T> vector(size);</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordflow">if</span> (size > 1)</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  {</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < size; ++i)</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  {</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  vector[i] = T(initial + (increment * static_cast<float>(i)));</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  }</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  }</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="keywordflow">return</span> vector;</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> </div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="keyword">struct </span>Convolution2dTest</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>  <span class="keyword">using</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4">LayerType</a> = <a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">armnn::Convolution2dLayer</a>;</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">static</span> std::string GetReceiverLayerName() { <span class="keywordflow">return</span> <span class="stringliteral">"Convolution2d"</span>; };</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> isElementWise = <span class="keyword">false</span>;</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetInputShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {1, 4, 4, 3}); } <span class="comment">// NHWCin</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetOutputShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {1, 3, 3, 4}); } <span class="comment">// NHWCout</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetWeightsShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {4, 2, 2, 3}); } <span class="comment">// CoutHWCin</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>  constexpr <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 48; <span class="comment">// batchIn * heightIn * widthIn * channelIn</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  constexpr <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 36; <span class="comment">// batchOut * heightOut * widthOut * channelOut</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* AddReceiverLayer(<a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>* network,</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)</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_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  std::vector<float> weightsData = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42};</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  std::vector<T> weightsVector = armnnUtils::QuantizedVector<T>(weightsData, g_qScale, g_qOffset);</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightsInfo(GetWeightsShape(), ArmnnType, g_qScale, g_qOffset);</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(weightsInfo, weightsVector);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalBias;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> </div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordflow">return</span> network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a073e2f61f527d7d3801c26bdbd37dd7e">AddConvolution2dLayer</a>(descriptor, weights, optionalBias, name);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  }</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> };</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <span class="keyword">struct </span><a class="code" href="_conv2d_test_impl_8cpp.xhtml#a9ed9dc40170e362160eb6e6e7edda209">DepthwiseConvolution2dTest</a></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> {</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keyword">using</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4">LayerType</a> = <a class="code" href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml">armnn::DepthwiseConvolution2dLayer</a>;</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keyword">static</span> std::string GetReceiverLayerName() { <span class="keywordflow">return</span> <span class="stringliteral">"DepthwiseConvolution2d"</span>; };</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> isElementWise = <span class="keyword">false</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>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetInputShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {1, 4, 4, 3}); } <span class="comment">// NHWCin</span></div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetOutputShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {1, 3, 3, 12}); } <span class="comment">// NHWCout</span></div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetWeightsShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {4, 3, 2, 2}); } <span class="comment">// MCinHW</span></div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  constexpr <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 48; <span class="comment">//batchIn * heightIn * widthIn * channelIn;</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  constexpr <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 108; <span class="comment">//batchOut * heightOut * widthOut * channelOut;</span></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>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* AddReceiverLayer(<a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>* network,</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)</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>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> </div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  std::vector<float> weightsData = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42};</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  std::vector<T> weightsVector = armnnUtils::QuantizedVector<T>(weightsData, g_qScale, g_qOffset);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightsInfo(GetWeightsShape(), ArmnnType, g_qScale, g_qOffset);</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(weightsInfo, weightsVector);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalBias;</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> </div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keywordflow">return</span> network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a78367a5054c92d435f4f5c7e10ec65b8">AddDepthwiseConvolution2dLayer</a>(descriptor, weights, optionalBias, name);</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  }</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> };</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> <span class="keyword">struct </span><a class="code" href="_fully_connected_test_impl_8cpp.xhtml#a834305b5bfdbee9e753bb7ad299944cf">FullyConnectedTest</a></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> {</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keyword">using</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4">LayerType</a> = <a class="code" href="classarmnn_1_1_fully_connected_layer.xhtml">armnn::FullyConnectedLayer</a>;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keyword">static</span> std::string GetReceiverLayerName() { <span class="keywordflow">return</span> <span class="stringliteral">"FullyConnected"</span>; };</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> isElementWise = <span class="keyword">false</span>;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> </div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetInputShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {2, 5, 1, 1}); } <span class="comment">// NCinHW</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetOutputShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {2, 3}); } <span class="comment">// NCout</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetWeightsShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {5, 3}); } <span class="comment">// CinCout</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  constexpr <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 10; <span class="comment">// batchIn * heightIn * widthIn * channelIn</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  constexpr <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 6; <span class="comment">// batchOut * heightOut * widthOut * channelOut</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* AddReceiverLayer(<a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>* network,</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  {</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> descriptor;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> </div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  std::vector<float> weightsData = { 1, 2, 3, 4, 5,</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  6, 7, 8, 9, 10,</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  11, 12, 13, 14, 15};</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  std::vector<T> weightsVector = armnnUtils::QuantizedVector<T>(weightsData, g_qScale, g_qOffset);</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightsInfo(GetWeightsShape(), ArmnnType, g_qScale, g_qOffset);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(weightsInfo, weightsVector);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalBias;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordflow">return</span> network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a4839e4ec3f07974c57ca2c856b40cd57">AddFullyConnectedLayer</a>(descriptor, weights, optionalBias, name);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  }</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> };</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> <span class="keyword">struct </span>BatchNormTest</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> {</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keyword">using</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4">LayerType</a> = <a class="code" href="classarmnn_1_1_batch_normalization_layer.xhtml">armnn::BatchNormalizationLayer</a>;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keyword">static</span> std::string GetReceiverLayerName() { <span class="keywordflow">return</span> <span class="stringliteral">"BatchNorm"</span>; };</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> isElementWise = <span class="keyword">false</span>;</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> </div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetInputShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {1, 4, 4, 3}); } <span class="comment">// NHWCin</span></div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetOutputShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {1, 4, 4, 3}); } <span class="comment">// NHWCout</span></div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> </div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  constexpr <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 48; <span class="comment">// batchIn * heightIn * widthIn * channelIn</span></div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  constexpr <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 48; <span class="comment">// batchOut * heightOut * widthOut * channelOut</span></div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> </div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* AddReceiverLayer(<a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>* network,</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  {</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">BatchNormalizationDescriptor</a> descriptor;</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  descriptor.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> </div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  std::vector<T> betaVector = GetVector<T>(GetOutputShape()[3], 0.0f, 0.2f);</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  std::vector<T> gammaVector = GetVector<T>(GetOutputShape()[3], 0.5f, 0.1f);</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  std::vector<T> meanVector = GetVector<T>(GetOutputShape()[3], 0.1f, 0.1f);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  std::vector<T> varianceVector = GetVector<T>(GetOutputShape()[3], 1.0f, 0.1f);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> </div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannelSize[] = { GetOutputShape()[3] };</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> beta(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, outputChannelSize, ArmnnType), betaVector);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> gamma(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, outputChannelSize, ArmnnType), gammaVector);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> mean(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, outputChannelSize, ArmnnType), meanVector);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> variance(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, outputChannelSize, ArmnnType), varianceVector);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> </div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordflow">return</span> network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a8d1067e754512c434da1238b67ad26ea">AddBatchNormalizationLayer</a>(descriptor, mean, variance, beta, gamma, name);</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> };</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">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> <span class="keyword">struct </span><a class="code" href="_multiplication_test_impl_8cpp.xhtml#ab7950f4e2ffcdf27eb2b81408c47c720">MultiplicationTest</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>  <span class="keyword">using</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4">LayerType</a> = <a class="code" href="classarmnn_1_1_multiplication_layer.xhtml">armnn::MultiplicationLayer</a>;</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keyword">static</span> std::string GetReceiverLayerName() { <span class="keywordflow">return</span> <span class="stringliteral">"Multiplication"</span>; };</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> isElementWise = <span class="keyword">true</span>;</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> </div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetInputShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {1, 4, 4, 3}); } <span class="comment">// NHWCin</span></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetOutputShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {1, 4, 4, 3}); } <span class="comment">// NHWCout</span></div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  constexpr <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 48; <span class="comment">// batchIn * heightIn * widthIn * channelIn</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  constexpr <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 48; <span class="comment">// batchOut * heightOut * widthOut * channelOut</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* AddReceiverLayer(<a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>* network,</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  {</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keywordflow">return</span> network-><a class="code" href="classarmnn_1_1_i_network.xhtml#ae24e82cf1ae2a71c5cd976edfb192fc0">AddMultiplicationLayer</a>(name);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  }</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> };</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span> </div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> <span class="keyword">struct </span><a class="code" href="_addition_test_impl_8cpp.xhtml#a5e9b2ce84031d422f4d7c3e8f5b50caa">AdditionTest</a></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">using</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4">LayerType</a> = <a class="code" href="classarmnn_1_1_addition_layer.xhtml">armnn::AdditionLayer</a>;</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keyword">static</span> std::string GetReceiverLayerName() { <span class="keywordflow">return</span> <span class="stringliteral">"Addition"</span>; };</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> isElementWise = <span class="keyword">true</span>;</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>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetInputShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {1, 4, 4, 3}); } <span class="comment">// NHWCin</span></div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetOutputShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {1, 4, 4, 3}); } <span class="comment">// NHWCout</span></div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span> </div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  constexpr <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 48; <span class="comment">// batchIn * heightIn * widthIn * channelIn</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  constexpr <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 48; <span class="comment">// batchOut * heightOut * widthOut * channelOut</span></div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span> </div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* AddReceiverLayer(<a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>* network,</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  {</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keywordflow">return</span> network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a4812e0137ee610310d23059efed2cb84">AddAdditionLayer</a>(name);</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> };</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> </div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span> <span class="keyword">struct </span><a class="code" href="_subtraction_test_impl_8cpp.xhtml#a997567716b084a181a143f5f89eaa8b8">SubtractionTest</a></div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> {</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keyword">using</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4">LayerType</a> = <a class="code" href="classarmnn_1_1_subtraction_layer.xhtml">armnn::SubtractionLayer</a>;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keyword">static</span> std::string GetReceiverLayerName() { <span class="keywordflow">return</span> <span class="stringliteral">"Subtraction"</span>; };</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> isElementWise = <span class="keyword">true</span>;</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> </div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetInputShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {1, 4, 4, 3}); } <span class="comment">// NHWCin</span></div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetOutputShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {1, 4, 4, 3}); } <span class="comment">// NHWCout</span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> </div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  constexpr <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 48; <span class="comment">// batchIn * heightIn * widthIn * channelIn</span></div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  constexpr <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 48; <span class="comment">// batchOut * heightOut * widthOut * channelOut</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">static</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* AddReceiverLayer(<a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>* network,</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  {</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordflow">return</span> network-><a class="code" href="classarmnn_1_1_i_network.xhtml#ab6d332d9c4b4f04c23f40f04f7f56d0d">AddSubtractionLayer</a>(name);</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> };</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> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span> <span class="keyword">struct </span><a class="code" href="_division_test_impl_8cpp.xhtml#a6e7cf17bea3cc66f9b44510b443fbef7">DivisionTest</a></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> {</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="keyword">using</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4">LayerType</a> = <a class="code" href="classarmnn_1_1_division_layer.xhtml">armnn::DivisionLayer</a>;</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="keyword">static</span> std::string GetReceiverLayerName() { <span class="keywordflow">return</span> <span class="stringliteral">"Division"</span>; };</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> isElementWise = <span class="keyword">true</span>;</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> </div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetInputShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {1, 4, 4, 3}); } <span class="comment">// NHWCin</span></div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> GetOutputShape() { <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>( {1, 4, 4, 3}); } <span class="comment">// NHWCout</span></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>  constexpr <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 48; <span class="comment">// batchIn * heightIn * widthIn * channelIn</span></div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  constexpr <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 48; <span class="comment">// batchOut * heightOut * widthOut * channelOut</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> </div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* AddReceiverLayer(<a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>* network,</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  {</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="keywordflow">return</span> network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a82a5bc0d24f4c4eb1fbf793e156a5193">AddDivisionLayer</a>(name);</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  }</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> };</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> </div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span> } <span class="comment">// namespace</span></div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> </div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> <span class="keyword">template</span><<span class="keyword">typename</span> LayerTest,</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnType></div><div class="line"><a name="l00268"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a0030acf0024b5557d31c1276dabdb7fa"> 268</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="_fuse_activation_tests_8cpp.xhtml#a0030acf0024b5557d31c1276dabdb7fa">CreatNetwork</a>(<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor, <span class="keywordtype">bool</span> preventFusing)</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="comment">// Create a network</span></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> </div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> </div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* receiverLayer = LayerTest::AddReceiverLayer(network.get(),</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="stringliteral">"receiverLayer"</span>);</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span> </div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activationLayer = network->AddActivationLayer(activationDescriptor,</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="stringliteral">"activation"</span>);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> </div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output2Layer = preventFusing?network->AddOutputLayer(1):<span class="keyword">nullptr</span>;</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> </div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="comment">// Define layers information</span></div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo(LayerTest::GetInputShape(), ArmnnType, g_qScale, g_qOffset);</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(LayerTest::GetOutputShape(), ArmnnType, g_qScale, g_qOffset);</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="comment">// Set layer information</span></div><div class="line"><a name="l00289"></a><span class="lineno"> 289</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="l00290"></a><span class="lineno"> 290</span>  receiverLayer-><a class="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="l00291"></a><span class="lineno"> 291</span>  activationLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span> </div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="comment">// Connect layers</span></div><div class="line"><a name="l00294"></a><span class="lineno"> 294</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>(receiverLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  receiverLayer-><a class="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>(activationLayer->GetInputSlot(0));</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  activationLayer->GetOutputSlot(0).Connect(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span> </div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="keywordflow">if</span> (LayerTest::isElementWise)</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  {</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</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>(receiverLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</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>  <span class="keywordflow">if</span> (preventFusing)</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>  receiverLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output2Layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  }</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span> </div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="keywordflow">return</span> network;</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> }</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span> </div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span> <span class="keyword">template</span><<span class="keyword">typename</span> LayerTest,</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnType,</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keyword">typename</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4">LayerType</a> = <span class="keyword">typename</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4">LayerTest::LayerType</a>,</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keyword">typename</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnType></a>></div><div class="line"><a name="l00314"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a108a90e253b3610f93e16a3581eba4bd"> 314</a></span> <span class="keywordtype">void</span> <a class="code" href="_fuse_activation_tests_8cpp.xhtml#a108a90e253b3610f93e16a3581eba4bd">FuseActivationIntoPreviousLayerTest</a>(<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor, <span class="keywordtype">float</span> tolerance, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456ae">armnn::Compute</a></div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span> backendId)</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span> {</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="comment">// FIRST NETWORK: Fused</span></div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <span class="comment">// Construct ArmNN network</span></div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> networkFused = CreatNetwork<LayerTest, ArmnnType>(activationDescriptor, <span class="keyword">false</span>);</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> </div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="comment">// Create ArmNN runtime</span></div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> run = <a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(<a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a>()); <span class="comment">// default options</span></div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> </div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="comment">// Optimise ArmNN network</span></div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNetFused = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*networkFused, {backendId}, run->GetDeviceSpec());</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> </div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graphFused = PolymorphicDowncast<OptimizedNetwork*>(optNetFused.get())->GetGraph();</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>  <span class="keyword">auto</span> checkFusedConv2d = [](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer)-><span class="keywordtype">bool</span> {</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  <span class="keywordflow">return</span> IsLayerOfType<LayerType>(layer) &&</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  (layer->GetNameStr() == <span class="stringliteral">"fused-activation-into-receiverLayer"</span>);</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  };</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span> </div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  BOOST_CHECK_MESSAGE(3 == graphFused.GetNumLayers(), LayerTest::GetReceiverLayerName());</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graphFused.cbegin(),</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  graphFused.cend(),</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  checkFusedConv2d,</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span> </div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="comment">// Load network into runtime</span></div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> networkIdentifier;</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  BOOST_TEST(run->LoadNetwork(networkIdentifier, std::move(optNetFused)) == <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a>);</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span> </div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="comment">//Creates structures for inputs and outputs.</span></div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  std::vector<float> data = GetVector<float>(LayerTest::inputSize, 1.0f, 0.1f);</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  std::vector<T> inputDataFused = armnnUtils::QuantizedVector<T>(data, g_qScale, g_qOffset);</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  std::vector<T> outputDataFused(LayerTest::outputSize);</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span> </div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensorsFused{</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  {0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(run->GetInputTensorInfo(networkIdentifier, 0), inputDataFused.data())}};</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensorsFused{</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  {0, <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(run->GetOutputTensorInfo(networkIdentifier, 0), outputDataFused.data())}};</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> </div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="comment">// Execute network</span></div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  run->EnqueueWorkload(networkIdentifier, inputTensorsFused, outputTensorsFused);</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> </div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="comment">// SECOND NETWORK: NotFused</span></div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="comment">// Construct ArmNN network</span></div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> networkNotFused = CreatNetwork<LayerTest, ArmnnType>(activationDescriptor, <span class="keyword">true</span>);</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span> </div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="comment">// Create ArmNN runtime</span></div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runNotFused = <a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(<a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a>()); <span class="comment">// default options</span></div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> </div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <span class="comment">// Optimise ArmNN network</span></div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNetNotFused = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*networkNotFused, {backendId}, runNotFused->GetDeviceSpec());</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span> </div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graphNotFused = PolymorphicDowncast<OptimizedNetwork*>(optNetNotFused.get())->GetGraph();</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span> </div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  BOOST_CHECK(5 == graphNotFused.GetNumLayers());</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graphNotFused.cbegin(),</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  graphNotFused.cend(),</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  &IsLayerOfType<armnn::InputLayer>,</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  &IsLayerOfType<LayerType>,</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  &IsLayerOfType<armnn::ActivationLayer>,</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  &IsLayerOfType<armnn::OutputLayer>,</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  &IsLayerOfType<armnn::OutputLayer>));</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span> </div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <span class="comment">// Load network into runtime</span></div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> networkIdentifierNotFused;</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  BOOST_TEST(runNotFused->LoadNetwork(networkIdentifierNotFused, std::move(optNetNotFused)) == <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a>);</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span> </div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="comment">//Creates structures for inputs and outputs.</span></div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  std::vector<T> inputDataNotFused = armnnUtils::QuantizedVector<T>(data, g_qScale, g_qOffset);</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  std::vector<T> outputDataNotFused(LayerTest::outputSize);</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  std::vector<T> outputData2NotFused(LayerTest::outputSize);</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> </div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensorsNotFused{</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  {0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(runNotFused->GetInputTensorInfo(networkIdentifierNotFused, 0), inputDataNotFused.data())}};</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensorsNotFused{</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  {0, <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 0), outputDataNotFused.data())},</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  {1, <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 1), outputData2NotFused.data())}};</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span> </div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="comment">// Execute network</span></div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  runNotFused->EnqueueWorkload(networkIdentifierNotFused, inputTensorsNotFused, outputTensorsNotFused);</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span> </div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="comment">// Check the output of the fused-activation matches with the output of the activation in the "NotFused" network</span></div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> n = 0; n < outputDataFused.size(); ++n)</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  {</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  BOOST_CHECK_CLOSE(static_cast<float>(outputDataFused[n]), static_cast<float>(outputDataNotFused[n]),</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  T(tolerance));</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  }</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span> }</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> </div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span> <span class="preprocessor">#if defined(ARMCOMPUTENEON_ENABLED)</span></div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span> <span class="comment">// ReLu fused into Receiver Layers Float32</span></div><div class="line"><a name="l00407"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a52b964f8b1cb25130ee86a9772cb6bde"> 407</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUIntoConvFloat32CpuAccTest)</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span> {</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span> </div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a>);</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span> }</div><div class="line"><a name="l00415"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a73ae9212f028bcab59bf8448ce5ccaf2"> 415</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUIntoDWConvFloat32CpuAccTest)</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span> {</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> </div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  FuseActivationIntoPreviousLayerTest<DepthwiseConvolution2dTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a>);</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span> }</div><div class="line"><a name="l00423"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a0e3f1d0722e356e8d0b36f5e7256883a"> 423</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUIntoFullyConnectedFloat32CpuAccTest)</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span> {</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span> </div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  FuseActivationIntoPreviousLayerTest<FullyConnectedTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a>);</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span> }</div><div class="line"><a name="l00431"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a6eeefca7bb8a258d108f0b4105a7c6eb"> 431</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUIntoBatchNormFloat32CpuAccTest)</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>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span> </div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  FuseActivationIntoPreviousLayerTest<BatchNormTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a>);</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span> }</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span> </div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span> <span class="comment">// BoundedReLu fused into Receiver Layers Float32</span></div><div class="line"><a name="l00441"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a4c34e26c3bd6ea757dc72a9d0f12cee6"> 441</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseBoundedReLUIntoConvFloat32CpuAccTest)</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span> {</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">ActivationFunction::BoundedReLu</a>;</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -1.0f;</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span> </div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</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"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a158c41dfc8aacd95847cc269bde2fa63"> 451</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseBoundedReLUIntoDWConvFloat32CpuAccTest)</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span> {</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">ActivationFunction::BoundedReLu</a>;</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -1.0f;</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> </div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  FuseActivationIntoPreviousLayerTest < DepthwiseConvolution2dTest < DataType::Float32 > , <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a> ></div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a>);</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span> }</div><div class="line"><a name="l00461"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a1f0f0640dc1fd7addd9273e15618e76e"> 461</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseBoundedReLUIntoFullyConnectedFloat32CpuAccTest)</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span> {</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">ActivationFunction::BoundedReLu</a>;</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -1.0f;</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>  FuseActivationIntoPreviousLayerTest<FullyConnectedTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a>);</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span> }</div><div class="line"><a name="l00471"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a0c5c547517e2b8951f55a0d751541360"> 471</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseBoundedReLUIntoBatchNormFloat32CpuAccTest)</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>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">ActivationFunction::BoundedReLu</a>;</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -1.0f;</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>  FuseActivationIntoPreviousLayerTest<BatchNormTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a>);</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> }</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span> </div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span> <span class="comment">// ReLU fused into Receiver Layers QAsymmU8</span></div><div class="line"><a name="l00483"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#ab993fbdc11c14ee31aafc9044ea8ddc0"> 483</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUIntoConvQAsymmU8CpuAccTest)</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>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span> </div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::QAsymmU8>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>></div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a>);</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span> }</div><div class="line"><a name="l00491"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a58c908f99f9385cf513ee8ae563ad756"> 491</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUIntoDWConvQAsymmU8CpuAccTest)</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span> {</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span> </div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  FuseActivationIntoPreviousLayerTest<DepthwiseConvolution2dTest<DataType::QAsymmU8>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>></div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a>);</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span> }</div><div class="line"><a name="l00499"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#ae187acf2e09c51e208b16dc236ba5eb4"> 499</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUIntoFullyConnectedQAsymmU8CpuAccTest)</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span> {</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span> </div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  FuseActivationIntoPreviousLayerTest<FullyConnectedTest<DataType::QAsymmU8>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>></div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a>);</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span> }</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span> </div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span> <span class="comment">// HardSwish fused into Receiver Layers Float32</span></div><div class="line"><a name="l00509"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a5ba6b56d2d6335e1e429ceb6c9b67aaa"> 509</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseHardSwishIntoConvFloat32CpuAccTest)</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>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186">ActivationFunction::HardSwish</a>;</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span> </div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a>);</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span> }</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span> </div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> <span class="comment">// TanH fused into Receiver Layers Float32</span></div><div class="line"><a name="l00519"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a382f3969ad4e672d46cfa11818e628e0"> 519</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseTanHIntoConvFloat32CpuAccTest)</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="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">ActivationFunction::TanH</a>;</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span> </div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a>);</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span> }</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span> <span class="preprocessor">#endif</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> <span class="preprocessor">#if defined(ARMCOMPUTECL_ENABLED)</span></div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span> <span class="comment">// ReLu fused into Receiver Layers Float32</span></div><div class="line"><a name="l00531"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a02571a096ef18583178c8377caa54c24"> 531</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUIntoConvFloat32GpuAccTest)</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span> {</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span> </div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span> }</div><div class="line"><a name="l00539"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#ae1e8962ebfec010045479f4b5b842e6f"> 539</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUIntoDWConvFloat32GpuAccTest)</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>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span> </div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  FuseActivationIntoPreviousLayerTest<DepthwiseConvolution2dTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span> }</div><div class="line"><a name="l00547"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a603878ba70ee12bde4d0f401a70b4393"> 547</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUIntoFullyConnectedFloat32GpuAccTest)</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span> {</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span> </div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  FuseActivationIntoPreviousLayerTest<FullyConnectedTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span> }</div><div class="line"><a name="l00555"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a08a7f797220eb1ace680badb0101496d"> 555</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUIntoBatchNormFloat32GpuAccTest)</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span> {</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span> </div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  FuseActivationIntoPreviousLayerTest<BatchNormTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span> }</div><div class="line"><a name="l00563"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#acf18c8a40e18ebf61215c0c9c87096fa"> 563</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUIntoMulFloat32GpuAccTest)</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span> {</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span> </div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  FuseActivationIntoPreviousLayerTest<MultiplicationTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span> }</div><div class="line"><a name="l00571"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a541f66c4f1cbf8f5e3db2a569b751031"> 571</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUIntoAddFloat32GpuAccTest)</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span> {</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span> </div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  FuseActivationIntoPreviousLayerTest<AdditionTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span> }</div><div class="line"><a name="l00579"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a5893f048d3665ee387b462f072b97fa3"> 579</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUIntoSubFloat32GpuAccTest)</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>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span> </div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  FuseActivationIntoPreviousLayerTest<SubtractionTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span> }</div><div class="line"><a name="l00587"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a8bdbd678d7dfd7496fde2d70002cd217"> 587</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUIntoDivFloat32GpuAccTest)</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span> {</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span> </div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  FuseActivationIntoPreviousLayerTest<DivisionTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span> }</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span> </div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span> <span class="comment">// BoundedReLu fused into Receiver Layers Float32</span></div><div class="line"><a name="l00597"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#afd8fb92603361c68761bff9cbac5ffe7"> 597</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseBoundedReLUIntoConvFloat32GpuAccTest)</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_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">ActivationFunction::BoundedReLu</a>;</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -1.0f;</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span> </div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span> }</div><div class="line"><a name="l00607"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#aceb960477d1e77912008669c98f50230"> 607</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseBoundedReLUIntoDWConvFloat32GpuAccTest)</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span> {</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">ActivationFunction::BoundedReLu</a>;</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -1.0f;</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span> </div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  FuseActivationIntoPreviousLayerTest<DepthwiseConvolution2dTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span> }</div><div class="line"><a name="l00617"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#abf1693da46acc6f81dfc6df85ad2d1f0"> 617</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseBoundedReLUIntoFullyConnectedFloat32GpuAccTest)</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span> {</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">ActivationFunction::BoundedReLu</a>;</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -1.0f;</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span> </div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  FuseActivationIntoPreviousLayerTest<FullyConnectedTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span> }</div><div class="line"><a name="l00627"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a9817386c9a30225c7603c2a7834d31e2"> 627</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseBoundedReLUIntoBatchNormFloat32GpuAccTest)</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span> {</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">ActivationFunction::BoundedReLu</a>;</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -1.0f;</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span> </div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  FuseActivationIntoPreviousLayerTest<BatchNormTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span> }</div><div class="line"><a name="l00637"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#ae169ee5010d71528a84077e05a51ffd1"> 637</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseBoundedReLUIntoMulFloat32GpuAccTest)</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>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">ActivationFunction::BoundedReLu</a>;</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -1.0f;</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>  FuseActivationIntoPreviousLayerTest<MultiplicationTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</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"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#aa15470d3b7258faa8d4cbda900ecbbc5"> 647</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseBoundedReLUIntoAddFloat32GpuAccTest)</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span> {</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">ActivationFunction::BoundedReLu</a>;</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -1.0f;</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span> </div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  FuseActivationIntoPreviousLayerTest<AdditionTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span> }</div><div class="line"><a name="l00657"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a94ac546b5e5302df31cb709668ae8648"> 657</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseBoundedReLUIntoSubFloat32GpuAccTest)</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span> {</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">ActivationFunction::BoundedReLu</a>;</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -1.0f;</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span> </div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  FuseActivationIntoPreviousLayerTest<SubtractionTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span> }</div><div class="line"><a name="l00667"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#af25fb06869a9449ace91382698ccf24d"> 667</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseBoundedReLUIntoDivFloat32GpuAccTest)</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>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">ActivationFunction::BoundedReLu</a>;</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -1.0f;</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span> </div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  FuseActivationIntoPreviousLayerTest<DivisionTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</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> <span class="comment">// ReLU fused into Receiver Layers QAsymmU8</span></div><div class="line"><a name="l00679"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a07ae85caee1337e34ceabd0a2e294c8e"> 679</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUQIntoConvAsymmU8GpuAccTest)</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>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span> </div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::QAsymmU8>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>></div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span> }</div><div class="line"><a name="l00687"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#ab9a23bfd314a265196bb602d4e80d518"> 687</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUQIntoDWConvAsymmU8GpuAccTest)</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span> {</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span> </div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  FuseActivationIntoPreviousLayerTest<DepthwiseConvolution2dTest<DataType::QAsymmU8>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>></div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span> }</div><div class="line"><a name="l00695"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a47a389a8a9f3ecfe7b522ffc5b94987f"> 695</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseReLUQIntoFullyConnectedAsymmU8GpuAccTest)</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span> {</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>;</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span> </div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  FuseActivationIntoPreviousLayerTest<FullyConnectedTest<DataType::QAsymmU8>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>></div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span> }</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span> </div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span> <span class="comment">// HardSwish fused into Receiver Layers Float32</span></div><div class="line"><a name="l00705"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a27314f0efb0487f219baf64f3e6e3ef0"> 705</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseHardSwishIntoConvFloat32GpuAccTest)</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span> {</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186">ActivationFunction::HardSwish</a>;</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span> </div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span> }</div><div class="line"><a name="l00713"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a4700b6ffbf89cf325e0ed02131027828"> 713</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseHardSwishIntoMulFloat32GpuAccTest)</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span> {</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186">ActivationFunction::HardSwish</a>;</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span> </div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  FuseActivationIntoPreviousLayerTest<MultiplicationTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span> }</div><div class="line"><a name="l00721"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#adf092f47c6c970c1902bb230b6f5178d"> 721</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseHardSwishIntoAddFloat32GpuAccTest)</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span> {</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186">ActivationFunction::HardSwish</a>;</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span> </div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  FuseActivationIntoPreviousLayerTest<AdditionTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</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"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a7306c4b163752b20ba9e44368a23b2e9"> 729</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseHardSwishIntoSubFloat32GpuAccTest)</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span> {</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186">ActivationFunction::HardSwish</a>;</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span> </div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  FuseActivationIntoPreviousLayerTest<SubtractionTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span> }</div><div class="line"><a name="l00737"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#aacc44727d8a24fa529c0f72ced770e1d"> 737</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseHardSwishIntoDivFloat32GpuAccTest)</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span> {</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186">ActivationFunction::HardSwish</a>;</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span> </div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  FuseActivationIntoPreviousLayerTest<DivisionTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span> }</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> <span class="comment">// TanH fused into Receiver Layers Float32</span></div><div class="line"><a name="l00747"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#ab4c9e7d291dd788d170601dc52abadda"> 747</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseTanHIntoConvFloat32GpuAccTest)</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span> {</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">ActivationFunction::TanH</a>;</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span> </div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  FuseActivationIntoPreviousLayerTest<Convolution2dTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span> }</div><div class="line"><a name="l00755"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#ae6a076fdb33ca7ce7fde87b34d986f38"> 755</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseTanHIntoMulFloat32GpuAccTest)</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span> {</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">ActivationFunction::TanH</a>;</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span> </div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  FuseActivationIntoPreviousLayerTest<MultiplicationTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span> }</div><div class="line"><a name="l00763"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a4f6e6d2a18b2873775a0b514a3c8740b"> 763</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseTanHIntoAddFloat32GpuAccTest)</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span> {</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">ActivationFunction::TanH</a>;</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span> </div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  FuseActivationIntoPreviousLayerTest<AdditionTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span> }</div><div class="line"><a name="l00771"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#a7384c2b3e8c0861adf801b824860b4d5"> 771</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseTanHIntoSubFloat32GpuAccTest)</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span> {</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">ActivationFunction::TanH</a>;</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span> </div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  FuseActivationIntoPreviousLayerTest<SubtractionTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span> }</div><div class="line"><a name="l00779"></a><span class="lineno"><a class="line" href="_fuse_activation_tests_8cpp.xhtml#ab987c797a4dabd4874ac9d3a31b1ac21"> 779</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FuseTanHIntoDivFloat32GpuAccTest)</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>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">ActivationFunction::TanH</a>;</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span> </div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  FuseActivationIntoPreviousLayerTest<DivisionTest<DataType::Float32>, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>></div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  (activationDescriptor, 0.0001f, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span> }</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span> <span class="preprocessor">#endif</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> <a class="code" href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a>()</div><div class="ttc" id="_output_shape_of_squeeze_8cpp_xhtml_ae3a6cb217a792718f2bd0e8f45e3ca9e"><div class="ttname"><a href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE(TensorflowLiteParser)</div></div> +<div class="ttc" id="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#l00449">Descriptors.hpp:449</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">armnn::ActivationFunction::ReLu</a></div></div> +<div class="ttc" id="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#l00451">Descriptors.hpp:451</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_runtime_xhtml_ad44ecd3700748dc30dc4bbe34ba5bde7"><div class="ttname"><a href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a></div><div class="ttdeci">static IRuntimePtr Create(const CreationOptions &options)</div><div class="ttdef"><b>Definition:</b> <a href="_runtime_8cpp_source.xhtml#l00032">Runtime.cpp:32</a></div></div> +<div class="ttc" id="classarmnn_1_1_batch_normalization_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_batch_normalization_layer.xhtml">armnn::BatchNormalizationLayer</a></div><div class="ttdoc">This layer represents a batch normalization operation. </div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_layer_8hpp_source.xhtml#l00015">BatchNormalizationLayer.hpp:15</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00061">INetwork.hpp:61</a></div></div> +<div class="ttc" id="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#l00501">Descriptors.hpp:501</a></div></div> +<div class="ttc" id="_fully_connected_test_impl_8cpp_xhtml_a834305b5bfdbee9e753bb7ad299944cf"><div class="ttname"><a href="_fully_connected_test_impl_8cpp.xhtml#a834305b5bfdbee9e753bb7ad299944cf">FullyConnectedTest</a></div><div class="ttdeci">LayerTestResult< T, 2 > FullyConnectedTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool biasEnabled)</div><div class="ttdef"><b>Definition:</b> <a href="_fully_connected_test_impl_8cpp_source.xhtml#l00071">FullyConnectedTestImpl.cpp:71</a></div></div> +<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional</a></div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00270">Optional.hpp:270</a></div></div> +<div class="ttc" id="structarmnn_1_1_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#l00503">Descriptors.hpp:503</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div> +<div class="ttc" id="_quantize_helper_8hpp_xhtml"><div class="ttname"><a href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_depthwise_convolution2d_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml">armnn::DepthwiseConvolution2dLayer</a></div><div class="ttdoc">This layer represents a depthwise convolution 2d operation. </div><div class="ttdef"><b>Definition:</b> <a href="_depthwise_convolution2d_layer_8hpp_source.xhtml#l00015">DepthwiseConvolution2dLayer.hpp:15</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#l00403">Descriptors.hpp:403</a></div></div> +<div class="ttc" id="_division_test_impl_8cpp_xhtml_a6e7cf17bea3cc66f9b44510b443fbef7"><div class="ttname"><a href="_division_test_impl_8cpp.xhtml#a6e7cf17bea3cc66f9b44510b443fbef7">DivisionTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > DivisionTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_division_test_impl_8cpp_source.xhtml#l00062">DivisionTestImpl.cpp:62</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a150468a02bd7b2d2d061c4aaaee939f0"><div class="ttname"><a href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a></div><div class="ttdeci">std::unique_ptr< IRuntime, void(*)(IRuntime *runtime)> IRuntimePtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00025">IRuntime.hpp:25</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::BatchNormalizationDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00636">Descriptors.hpp:636</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a0743ed5e860c316a20b68ca96301b411"><div class="ttname"><a href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a></div><div class="ttdeci">typename ResolveTypeImpl< DT >::Type ResolveType</div><div class="ttdef"><b>Definition:</b> <a href="_resolve_type_8hpp_source.xhtml#l00073">ResolveType.hpp:73</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml">armnn::INetwork</a></div><div class="ttdoc">Main network class which provides the interface for building up a neural network. ...</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00105">INetwork.hpp:105</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a8d1067e754512c434da1238b67ad26ea"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a8d1067e754512c434da1238b67ad26ea">armnn::INetwork::AddBatchNormalizationLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddBatchNormalizationLayer(const BatchNormalizationDescriptor &desc, const ConstTensor &mean, const ConstTensor &variance, const ConstTensor &beta, const ConstTensor &gamma, const char *name=nullptr)=0</div><div class="ttdoc">Adds a batch normalization layer to the network. </div></div> +<div class="ttc" id="namespacearmnn_xhtml_aa01bce88f89975a5a031db4cc8861527"><div class="ttname"><a href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">armnn::InputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class ConstTensor > > InputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00340">Tensor.hpp:340</a></div></div> +<div class="ttc" id="_resolve_type_8hpp_xhtml"><div class="ttname"><a href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a></div></div> +<div class="ttc" id="_fuse_activation_tests_8cpp_xhtml_a0030acf0024b5557d31c1276dabdb7fa"><div class="ttname"><a href="_fuse_activation_tests_8cpp.xhtml#a0030acf0024b5557d31c1276dabdb7fa">CreatNetwork</a></div><div class="ttdeci">INetworkPtr CreatNetwork(ActivationDescriptor activationDescriptor, bool preventFusing)</div><div class="ttdef"><b>Definition:</b> <a href="_fuse_activation_tests_8cpp_source.xhtml#l00268">FuseActivationTests.cpp:268</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a83015160d8c67d5d77735eb0d4033d9a"><div class="ttname"><a href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">armnn::NetworkId</a></div><div class="ttdeci">int NetworkId</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00020">IRuntime.hpp:20</a></div></div> +<div class="ttc" id="_test_utils_8hpp_xhtml"><div class="ttname"><a href="_test_utils_8hpp.xhtml">TestUtils.hpp</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2020 ARM Limited. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00025">00_introduction.dox:25</a></div></div> +<div class="ttc" id="_addition_test_impl_8cpp_xhtml_a5e9b2ce84031d422f4d7c3e8f5b50caa"><div class="ttname"><a href="_addition_test_impl_8cpp.xhtml#a5e9b2ce84031d422f4d7c3e8f5b50caa">AdditionTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > AdditionTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_addition_test_impl_8cpp_source.xhtml#l00022">AdditionTestImpl.cpp:22</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="classarmnn_1_1_i_network_xhtml_a4839e4ec3f07974c57ca2c856b40cd57"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a4839e4ec3f07974c57ca2c856b40cd57">armnn::INetwork::AddFullyConnectedLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddFullyConnectedLayer(const FullyConnectedDescriptor &fullyConnectedDescriptor, const ConstTensor &weights, const Optional< ConstTensor > &biases, const char *name=nullptr)=0</div><div class="ttdoc">Adds a fully connected layer to the network. </div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &tensorInfo)=0</div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a073e2f61f527d7d3801c26bdbd37dd7e"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a073e2f61f527d7d3801c26bdbd37dd7e">armnn::INetwork::AddConvolution2dLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddConvolution2dLayer(const Convolution2dDescriptor &convolution2dDescriptor, const ConstTensor &weights, const Optional< ConstTensor > &biases, const char *name=nullptr)=0</div><div class="ttdoc">Adds a 2D convolution layer to the network. </div></div> +<div class="ttc" id="classarmnn_1_1_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and a mutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00306">Tensor.hpp:306</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a4812e0137ee610310d23059efed2cb84"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a4812e0137ee610310d23059efed2cb84">armnn::INetwork::AddAdditionLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddAdditionLayer(const char *name=nullptr)=0</div><div class="ttdoc">Adds an addition layer to the network. </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#l00441">Descriptors.hpp:441</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#l00493">Descriptors.hpp:493</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38"><div class="ttname"><a href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">armnn::Status::Success</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456ae"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456ae">armnn::Compute</a></div><div class="ttdeci">Compute</div><div class="ttdoc">The Compute enum is now deprecated and it is now being replaced by BackendId. </div><div class="ttdef"><b>Definition:</b> <a href="_backend_id_8hpp_source.xhtml#l00021">BackendId.hpp:21</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00032">Types.hpp:32</a></div></div> +<div class="ttc" id="classarmnn_1_1_fully_connected_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_fully_connected_layer.xhtml">armnn::FullyConnectedLayer</a></div><div class="ttdoc">This layer represents a fully connected operation. </div><div class="ttdef"><b>Definition:</b> <a href="_fully_connected_layer_8hpp_source.xhtml#l00015">FullyConnectedLayer.hpp:15</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a82e98ef05fd67036d1195ba17174d685"><div class="ttname"><a href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a></div><div class="ttdeci">IOptimizedNetworkPtr Optimize(const INetwork &network, const std::vector< BackendId > &backendPreferences, const IDeviceSpec &deviceSpec, const OptimizerOptions &options=OptimizerOptions(), Optional< std::vector< std::string > &> messages=EmptyOptional())</div><div class="ttdoc">Create an optimized version of the network. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l01011">Network.cpp:1011</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#l00384">Descriptors.hpp:384</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#l00397">Descriptors.hpp:397</a></div></div> +<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00314">Tensor.hpp:314</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a8f091a512915d1cb29a4ebf13dfc53ea"><div class="ttname"><a href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">armnn::OutputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class Tensor > > OutputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00341">Tensor.hpp:341</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a674efcf6cbdb9e831d653ff0e821fb38"><div class="ttname"><a href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a></div><div class="ttdeci">std::unique_ptr< IOptimizedNetwork, void(*)(IOptimizedNetwork *network)> IOptimizedNetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00600">INetwork.hpp:600</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a></div><div class="ttdoc">GPU Execution: OpenCL: ArmCompute. </div></div> +<div class="ttc" id="namespacearmnn_xhtml_a10d15f3df1ab52b3b915a4be1dbf386b"><div class="ttname"><a href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">armnn::BOOST_AUTO_TEST_CASE</a></div><div class="ttdeci">BOOST_AUTO_TEST_CASE(CheckConvolution2dLayer)</div><div class="ttdef"><b>Definition:</b> <a href="_const_tensor_layer_visitor_8cpp_source.xhtml#l00268">ConstTensorLayerVisitor.cpp:268</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a></div><div class="ttdoc">An ActivationDescriptor for the ActivationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00020">Descriptors.hpp:20</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">armnn::ActivationFunction::BoundedReLu</a></div><div class="ttdoc">min(a, max(b, input)) ReLu1 & ReLu6. </div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a78367a5054c92d435f4f5c7e10ec65b8"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a78367a5054c92d435f4f5c7e10ec65b8">armnn::INetwork::AddDepthwiseConvolution2dLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddDepthwiseConvolution2dLayer(const DepthwiseConvolution2dDescriptor &convolution2dDescriptor, const ConstTensor &weights, const Optional< ConstTensor > &biases, const char *name=nullptr)=0</div><div class="ttdoc">Adds a 2D depthwise convolution layer to the network. </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#l00443">Descriptors.hpp:443</a></div></div> +<div class="ttc" id="classarmnn_1_1_graph_xhtml"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml">armnn::Graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00029">Graph.hpp:29</a></div></div> +<div class="ttc" id="_fuse_activation_tests_8cpp_xhtml_a108a90e253b3610f93e16a3581eba4bd"><div class="ttname"><a href="_fuse_activation_tests_8cpp.xhtml#a108a90e253b3610f93e16a3581eba4bd">FuseActivationIntoPreviousLayerTest</a></div><div class="ttdeci">void FuseActivationIntoPreviousLayerTest(ActivationDescriptor activationDescriptor, float tolerance, armnn::Compute backendId)</div><div class="ttdef"><b>Definition:</b> <a href="_fuse_activation_tests_8cpp_source.xhtml#l00314">FuseActivationTests.cpp:314</a></div></div> +<div class="ttc" id="_i_network_8hpp_xhtml"><div class="ttname"><a href="_i_network_8hpp.xhtml">INetwork.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_addition_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_addition_layer.xhtml">armnn::AdditionLayer</a></div><div class="ttdoc">This layer represents an addition operation. </div><div class="ttdef"><b>Definition:</b> <a href="_addition_layer_8hpp_source.xhtml#l00013">AdditionLayer.hpp:13</a></div></div> +<div class="ttc" id="structarmnn_1_1_i_runtime_1_1_creation_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00042">IRuntime.hpp:42</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml_a017b2990003a014234f13e999dc7c689"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">armnn::ActivationDescriptor::m_A</a></div><div class="ttdeci">float m_A</div><div class="ttdoc">Alpha upper bound value used by the activation functions. (BoundedReLu, Linear, TanH, Elu). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00045">Descriptors.hpp:45</a></div></div> +<div class="ttc" id="_profiler_tests_8cpp_xhtml_af7f71af5c6c124222dd1c42c5df892f4"><div class="ttname"><a href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE_END()</div></div> +<div class="ttc" id="classarmnn_1_1_subtraction_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_subtraction_layer.xhtml">armnn::SubtractionLayer</a></div><div class="ttdoc">This layer represents a subtraction operation. </div><div class="ttdef"><b>Definition:</b> <a href="_subtraction_layer_8hpp_source.xhtml#l00014">SubtractionLayer.hpp:14</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#l00495">Descriptors.hpp:495</a></div></div> +<div class="ttc" id="_subtraction_test_impl_8cpp_xhtml_a997567716b084a181a143f5f89eaa8b8"><div class="ttname"><a href="_subtraction_test_impl_8cpp.xhtml#a997567716b084a181a143f5f89eaa8b8">SubtractionTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > SubtractionTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_subtraction_test_impl_8cpp_source.xhtml#l00107">SubtractionTestImpl.cpp:107</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a></div><div class="ttdoc">CPU Execution: NEON: ArmCompute. </div></div> +<div class="ttc" id="_network_8hpp_xhtml"><div class="ttname"><a href="_network_8hpp.xhtml">Network.hpp</a></div></div> +<div class="ttc" id="_test_utils_8hpp_xhtml_a0eedb278f57355b47fa983450d4e378c"><div class="ttname"><a href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a></div><div class="ttdeci">bool CheckSequence(const armnn::Graph::ConstIterator first, const armnn::Graph::ConstIterator last)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8hpp_source.xhtml#l00021">TestUtils.hpp:21</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot & GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div> +<div class="ttc" id="classarmnn_1_1_division_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_division_layer.xhtml">armnn::DivisionLayer</a></div><div class="ttdoc">This layer represents a division operation. </div><div class="ttdef"><b>Definition:</b> <a href="_division_layer_8hpp_source.xhtml#l00014">DivisionLayer.hpp:14</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div> +<div class="ttc" id="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="_layers_fwd_8hpp_xhtml"><div class="ttname"><a href="_layers_fwd_8hpp.xhtml">LayersFwd.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_convolution2d_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_convolution2d_layer.xhtml">armnn::Convolution2dLayer</a></div><div class="ttdoc">This layer represents a convolution 2d operation. </div><div class="ttdef"><b>Definition:</b> <a href="_convolution2d_layer_8hpp_source.xhtml#l00015">Convolution2dLayer.hpp:15</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00101">INetwork.hpp:101</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_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="classarmnn_1_1_multiplication_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_multiplication_layer.xhtml">armnn::MultiplicationLayer</a></div><div class="ttdoc">This layer represents a multiplication operation. </div><div class="ttdef"><b>Definition:</b> <a href="_multiplication_layer_8hpp_source.xhtml#l00014">MultiplicationLayer.hpp:14</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186">armnn::ActivationFunction::HardSwish</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a464f0ff87b1aabf71febaa71321dd40b"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create(NetworkOptions networkOptions={})</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00046">Network.cpp:46</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a82a5bc0d24f4c4eb1fbf793e156a5193"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a82a5bc0d24f4c4eb1fbf793e156a5193">armnn::INetwork::AddDivisionLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddDivisionLayer(const char *name=nullptr)=0</div><div class="ttdoc">Adds a division layer to the network. </div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml_a28c4c9cb15f6be3499abbc46b356060b"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">armnn::ActivationDescriptor::m_B</a></div><div class="ttdeci">float m_B</div><div class="ttdoc">Beta lower bound value used by the activation functions. (BoundedReLu, Linear, TanH). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00047">Descriptors.hpp:47</a></div></div> +<div class="ttc" id="_multiplication_test_impl_8cpp_xhtml_ab7950f4e2ffcdf27eb2b81408c47c720"><div class="ttname"><a href="_multiplication_test_impl_8cpp.xhtml#ab7950f4e2ffcdf27eb2b81408c47c720">MultiplicationTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > MultiplicationTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_multiplication_test_impl_8cpp_source.xhtml#l00020">MultiplicationTestImpl.cpp:20</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a9ed9dc40170e362160eb6e6e7edda209"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a9ed9dc40170e362160eb6e6e7edda209">DepthwiseConvolution2dTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > DepthwiseConvolution2dTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03545">Conv2dTestImpl.cpp:3545</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml_af10fa7883e3579950f477bee92a64844"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">armnn::ActivationDescriptor::m_Function</a></div><div class="ttdeci">ActivationFunction m_Function</div><div class="ttdoc">The activation function to use (Sigmoid, TanH, Linear, ReLu, BoundedReLu, SoftReLu, LeakyReLu, Abs, Sqrt, Square, Elu). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00043">Descriptors.hpp:43</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#l00455">Descriptors.hpp:455</a></div></div> +<div class="ttc" id="classarmnn_1_1_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml">armnn::Layer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00210">Layer.hpp:210</a></div></div> +<div class="ttc" id="classarmnn_1_1_optimizer_xhtml"><div class="ttname"><a href="classarmnn_1_1_optimizer.xhtml">armnn::Optimizer</a></div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_8hpp_source.xhtml#l00014">Optimizer.hpp:14</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#l00621">Descriptors.hpp:621</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="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4">armnn::LayerType</a></div><div class="ttdeci">LayerType</div><div class="ttdef"><b>Definition:</b> <a href="_internal_types_8hpp_source.xhtml#l00086">InternalTypes.hpp:86</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">armnn::ActivationFunction::TanH</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_ae24e82cf1ae2a71c5cd976edfb192fc0"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#ae24e82cf1ae2a71c5cd976edfb192fc0">armnn::INetwork::AddMultiplicationLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddMultiplicationLayer(const char *name=nullptr)=0</div><div class="ttdoc">Adds a multiplication layer to the network. </div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_ab6d332d9c4b4f04c23f40f04f7f56d0d"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#ab6d332d9c4b4f04c23f40f04f7f56d0d">armnn::INetwork::AddSubtractionLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddSubtractionLayer(const char *name=nullptr)=0</div><div class="ttdoc">Adds a subtraction layer to the network. </div></div> +</div><!-- fragment --></div><!-- contents --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for treeview function! --> + <ul> + <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_e0a84d05c80a2ef4231141dcbbeac5c8.xhtml">armnn</a></li><li class="navelem"><a class="el" href="dir_9d86fd1fbecbedf5bdb69c7e7235fe5f.xhtml">test</a></li><li class="navelem"><a class="el" href="dir_f1cd0e6da811a659c139424442adfb5f.xhtml">optimizations</a></li><li class="navelem"><a class="el" href="_fuse_activation_tests_8cpp.xhtml">FuseActivationTests.cpp</a></li> + <li class="footer">Generated on Thu Nov 26 2020 11:04:22 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> |