1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
|
<!-- Copyright (c) 2020 ARM Limited. -->
<!-- -->
<!-- SPDX-License-Identifier: MIT -->
<!-- -->
<!-- HTML header for doxygen 1.8.13-->
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
<meta name="generator" content="Doxygen 1.8.13"/>
<meta name="robots" content="NOINDEX, NOFOLLOW" />
<meta name="viewport" content="width=device-width, initial-scale=1"/>
<title>ArmNN: src/backends/backendsCommon/test/OptimizedNetworkTests.cpp Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="navtree.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="resize.js"></script>
<script type="text/javascript" src="navtreedata.js"></script>
<script type="text/javascript" src="navtree.js"></script>
<script type="text/javascript">
$(document).ready(initResizable);
</script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
extensions: ["tex2jax.js"],
jax: ["input/TeX","output/HTML-CSS"],
});
</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
<link href="stylesheet.css" rel="stylesheet" type="text/css"/>
</head>
<body>
<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
<div id="titlearea">
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
<img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/>
<td style="padding-left: 0.5em;">
<div id="projectname">
 <span id="projectnumber">22.05</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('_optimized_network_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">OptimizedNetworkTests.cpp</div> </div>
</div><!--header-->
<div class="contents">
<a href="_optimized_network_tests_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd. 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="backends_2backends_common_2test_2_common_test_utils_8hpp.xhtml">CommonTestUtils.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="_graph_8hpp.xhtml">Graph.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_network_8hpp.xhtml">Network.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_ref_workload_factory_8hpp.xhtml">reference/RefWorkloadFactory.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 <doctest/doctest.h></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"><a class="line" href="_optimized_network_tests_8cpp.xhtml#a833eb192dab47109dc8413ee16a6ad57"> 15</a></span> <a class="code" href="_optimized_network_tests_8cpp.xhtml#a833eb192dab47109dc8413ee16a6ad57">TEST_SUITE</a>(<span class="stringliteral">"OptimizedNetwork"</span>)</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> {</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> TEST_CASE(<span class="stringliteral">"SerializeToDot"</span>)</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> {</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  <span class="comment">// build up the structure of the network</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</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="comment">//Defines layers.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <span class="keyword">auto</span> input = net->AddInputLayer(0);</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="keyword">auto</span> add = net->AddAdditionLayer();</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keyword">auto</span> output = net->AddOutputLayer(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="comment">// Connects layers.</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  input->GetOutputSlot(0).Connect(add->GetInputSlot(0));</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  input->GetOutputSlot(0).Connect(add->GetInputSlot(1));</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  add->GetOutputSlot(0).Connect(output->GetInputSlot(0));</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> shape({4});</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  input->GetOutputSlot(0).SetTensorInfo(info);</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  add->GetOutputSlot(0).SetTensorInfo(info);</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>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options));</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>  std::vector<armnn::BackendId> backends = {<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a>};</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a> optimizedNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a>(*net, backends, runtime->GetDeviceSpec());</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>  std::ostringstream ss;</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  optimizedNet->SerializeToDot(ss);</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">auto</span> inputId = input->GetGuid();</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">auto</span> addId = add->GetGuid();</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keyword">auto</span> outputId = output->GetGuid();</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>  std::stringstream expected;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  expected <<</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="stringliteral">"digraph Optimized {\n"</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="stringliteral">" node [shape=\"record\"];\n"</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="stringliteral">" edge [fontsize=8 fontcolor=\"blue\" fontname=\"arial-bold\"];\n"</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="stringliteral">" "</span> << inputId << <span class="stringliteral">" [label=\"{Input|Guid : "</span> << inputId << <span class="stringliteral">"\\lLayerType : Input\\l"</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="stringliteral">"BackendID : CpuRef\\l}\"];\n"</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="stringliteral">" "</span> << addId << <span class="stringliteral">" [label=\"{Addition|Guid : "</span> << addId << <span class="stringliteral">"\\lLayerType : Addition\\l"</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="stringliteral">"BackendID : CpuRef\\l}\"];\n"</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="stringliteral">" "</span> << outputId << <span class="stringliteral">" [label=\"{Output|Guid : "</span> << outputId << <span class="stringliteral">"\\lLayerType : Output\\l"</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="stringliteral">"BackendID : CpuRef\\l}\"];\n"</span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="stringliteral">" "</span> << inputId << <span class="stringliteral">" -> "</span> << addId << <span class="stringliteral">" [label=< [4] >];\n"</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="stringliteral">" "</span> << inputId << <span class="stringliteral">" -> "</span> << addId << <span class="stringliteral">" [label=< [4] >];\n"</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="stringliteral">" "</span> << addId << <span class="stringliteral">" -> "</span> << outputId << <span class="stringliteral">" [label=< [4] >];\n"</span></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="stringliteral">"}\n"</span>;</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>  CHECK(ss.str() == expected.str());</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> }</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> TEST_CASE(<span class="stringliteral">"OptimizeValidateDeviceNonSupportLayerNoFallback"</span>)</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> {</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="comment">// build up the structure of the network</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>());</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* input = net->AddInputLayer(0);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="comment">// This layer configuration isn't supported by CpuAcc and isn't allowed to fall back, so Optimize will return null.</span></div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a> descriptor;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* normalize = net->AddNormalizationLayer(descriptor);</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* output = net->AddOutputLayer(0);</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>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(normalize-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  normalize-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> </div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 1, 4, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>));</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  normalize-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 1, 4, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>));</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options));</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>  std::vector<armnn::BackendId> backends = { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a> };</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  std::vector<std::string> errMessages;</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="keywordflow">try</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  {</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec(), <a class="code" href="structarmnn_1_1_optimizer_options.xhtml">armnn::OptimizerOptions</a>(), errMessages);</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  FAIL(<span class="stringliteral">"Should have thrown an exception."</span>);</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  }</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>&)</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  {</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="comment">// Different exceptions are thrown on different backends</span></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>  CHECK(errMessages.size() > 0);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> }</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> TEST_CASE(<span class="stringliteral">"OptimizeValidateDeviceNonSupportLayerWithFallback"</span>)</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> {</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="comment">// build up the structure of the network</span></div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>());</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> </div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* input = net->AddInputLayer(0);</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> </div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="comment">// This layer configuration isn't supported by CpuAcc but it allows to fallback to CpuRef.</span></div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a> descriptor;</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* normalize = net->AddNormalizationLayer(descriptor);</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> </div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* output = net->AddOutputLayer(0);</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>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(normalize-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  normalize-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> </div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 1, 4, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>));</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  normalize-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 1, 4, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>));</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> </div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options));</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>  std::vector<armnn::BackendId> backends = { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a>, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a> };</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a>(*net, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  REQUIRE(optNet);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> </div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph = <a class="code" href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">GetGraphForTesting</a>(optNet.get());</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a5a989a5f9aeb2935ba932b7f8312fe0c">AllocateDynamicBuffers</a>();</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> </div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& layer : graph)</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>  <span class="comment">// If NEON is enabled, Input and Output layers are supported by CpuAcc,</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="comment">// the other layers are supported by CpuRef.</span></div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="comment">// If NEON is not enabled, all layers are supported by CpuRef.</span></div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> <span class="preprocessor">#if defined(ARMCOMPUTENEON_ENABLED)</span></div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordflow">if</span> (layer->GetType() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>)</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  {</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  CHECK(layer->GetBackendId() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a>);</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">else</span> <span class="keywordflow">if</span> (layer->GetType() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aac61f2e17250a818dee4d12b112aa88f">armnn::LayerType::Normalization</a>)</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  {</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  CHECK(layer->GetBackendId() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a>);</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="preprocessor">#else</span></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  CHECK(layer->GetBackendId() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a>);</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  }</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> }</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> TEST_CASE(<span class="stringliteral">"OptimizeValidateWorkloadsUndefinedComputeDevice"</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">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> desc({3, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> </div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="comment">// build up the structure of the network</span></div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>());</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> </div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a> nmDesc;</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a> acDesc;</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> </div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="comment">// in</span></div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="comment">// |</span></div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="comment">// nm</span></div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="comment">// / |</span></div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="comment">// ac |</span></div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="comment">// \ |</span></div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="comment">// ml</span></div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="comment">// |</span></div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="comment">// sm</span></div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="comment">// |</span></div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="comment">// ot</span></div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer = net->AddInputLayer(0, <span class="stringliteral">"in"</span>);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(desc);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> </div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> normLayer = net->AddNormalizationLayer(nmDesc, <span class="stringliteral">"nm"</span>);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> </div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(normLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  normLayer-><a class="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>(desc);</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>  layer = net->AddActivationLayer(acDesc, <span class="stringliteral">"ac"</span>);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  normLayer-><a class="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>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(desc);</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> </div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* prevLayer = layer;</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  layer = net->AddMultiplicationLayer(<span class="stringliteral">"ml"</span>);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> </div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  prevLayer-><a class="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>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  normLayer-><a class="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>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(desc);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> </div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  prevLayer = layer;</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::SoftmaxDescriptor</a> softmaxDescriptor;</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  layer = net->AddSoftmaxLayer(softmaxDescriptor, <span class="stringliteral">"sm"</span>);</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>  prevLayer-><a class="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>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(desc);</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>  prevLayer = layer;</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  layer = net->AddOutputLayer(0, <span class="stringliteral">"ot"</span>);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> </div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  prevLayer-><a class="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>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> </div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options));</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>  std::vector<armnn::BackendId> backends = { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">armnn::Compute::Undefined</a> };</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  std::vector<std::string> errMessages;</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span> </div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  {</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec(), <a class="code" href="structarmnn_1_1_optimizer_options.xhtml">armnn::OptimizerOptions</a>(), errMessages);</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  FAIL(<span class="stringliteral">"Should have thrown an exception."</span>);</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  }</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>&)</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  {</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="comment">// Different exceptions are thrown on different backends</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>  CHECK(errMessages.size() > 0);</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> }</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span> </div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> TEST_CASE(<span class="stringliteral">"OptimizeValidateWorkloadsUndefinedComputeDeviceWithFallback"</span>)</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> {</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> desc({3, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> </div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="comment">// build up the structure of the network</span></div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>());</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span> </div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a> nmDesc;</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a> acDesc;</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> </div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="comment">// in</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="comment">// |</span></div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="comment">// nm</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="comment">// / |</span></div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="comment">// ac |</span></div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="comment">// \ |</span></div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="comment">// ml</span></div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="comment">// |</span></div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="comment">// sm</span></div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="comment">// |</span></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="comment">// ot</span></div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer = net->AddInputLayer(0, <span class="stringliteral">"in"</span>);</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(desc);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span> </div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> normLayer = net->AddNormalizationLayer(nmDesc, <span class="stringliteral">"nm"</span>);</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> </div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(normLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  normLayer-><a class="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>(desc);</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span> </div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  layer = net->AddActivationLayer(acDesc, <span class="stringliteral">"ac"</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>  normLayer-><a class="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>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(desc);</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* prevLayer = layer;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  layer = net->AddMultiplicationLayer(<span class="stringliteral">"ml"</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>  prevLayer-><a class="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>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  normLayer-><a class="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>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(desc);</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> </div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  prevLayer = layer;</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::SoftmaxDescriptor</a> softmaxDescriptor;</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  layer = net->AddSoftmaxLayer(softmaxDescriptor, <span class="stringliteral">"sm"</span>);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> </div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  prevLayer-><a class="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>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(desc);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span> </div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  prevLayer = layer;</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  layer = net->AddOutputLayer(0, <span class="stringliteral">"ot"</span>);</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span> </div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  prevLayer-><a class="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>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> </div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options));</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> </div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  std::vector<armnn::BackendId> backends = { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">armnn::Compute::Undefined</a>, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a> };</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>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a>(*net, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  CHECK(optNet);</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span> </div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph = <a class="code" href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">GetGraphForTesting</a>(optNet.get());</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a5a989a5f9aeb2935ba932b7f8312fe0c">AllocateDynamicBuffers</a>();</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> </div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="comment">// validate workloads</span></div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <a class="code" href="classarmnn_1_1_ref_workload_factory.xhtml">armnn::RefWorkloadFactory</a> fact;</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& layer : graph)</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  {</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  CHECK(layer->GetBackendId() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a>);</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  CHECK_NOTHROW(</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  layer->CreateWorkload(fact));</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> }</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> TEST_CASE(<span class="stringliteral">"OptimizeValidateWorkloadsDuplicateComputeDeviceWithFallback"</span>)</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> {</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="comment">// build up the structure of the network</span></div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>());</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span> </div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* input = net->AddInputLayer(0);</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="comment">// This layer configuration isn't supported by CpuAcc but it allows to fallback to CpuRef.</span></div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a> descriptor;</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* normalize = net->AddNormalizationLayer(descriptor);</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span> </div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* output = net->AddOutputLayer(0);</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span> </div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(normalize-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  normalize-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span> </div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 1, 4, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>));</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  normalize-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 1, 4, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>));</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span> </div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options));</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span> </div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  std::vector<armnn::BackendId> backends = { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a>,</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>,</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a> };</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> </div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a>(*net, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  REQUIRE(optNet);</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span> </div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph = <a class="code" href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">GetGraphForTesting</a>(optNet.get());</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a5a989a5f9aeb2935ba932b7f8312fe0c">AllocateDynamicBuffers</a>();</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span> </div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& layer : graph)</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>  <span class="comment">// If NEON is enabled, Input and Output layers are supported by CpuAcc,</span></div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <span class="comment">// the other layers are supported by CpuRef.</span></div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="comment">// If only CL is enabled, Input and Output layers are supported by GpuAcc,</span></div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="comment">// the other layers are supported by CpuRef.</span></div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="comment">// If neither NEON, nor CL is enabled, all layers are supported by CpuRef.</span></div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span> <span class="preprocessor">#if defined(ARMCOMPUTENEON_ENABLED)</span></div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <span class="keywordflow">if</span> (layer->GetType() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a>)</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  {</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  CHECK(layer->GetBackendId() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a>);</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  }</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (layer->GetType() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>)</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  {</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  CHECK(layer->GetBackendId() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a>);</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  }</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (layer->GetType() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aac61f2e17250a818dee4d12b112aa88f">armnn::LayerType::Normalization</a>)</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  {</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  CHECK(layer->GetBackendId() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a>);</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  }</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span> <span class="preprocessor">#elif defined(ARMCOMPUTECL_ENABLED)</span></div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="keywordflow">if</span> (layer->GetType() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a>)</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  {</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  CHECK(layer->GetBackendId() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a>);</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  }</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (layer->GetType() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>)</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  {</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  CHECK(layer->GetBackendId() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>);</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  }</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (layer->GetType() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aac61f2e17250a818dee4d12b112aa88f">armnn::LayerType::Normalization</a>)</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  {</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  CHECK(layer->GetBackendId() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a>);</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="preprocessor">#else</span></div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  CHECK(layer->GetBackendId() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a>);</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  }</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span> }</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span> </div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span> TEST_CASE(<span class="stringliteral">"OptimizeNetworkCopy"</span>)</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span> {</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a> runtime = <a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options);</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  std::vector<armnn::NetworkId> networkIds;</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span> </div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"convolution2d"</span>);</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 5, 5, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 2, 2, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span> </div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> </div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span> </div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements());</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> </div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = 2;</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = 2;</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span> </div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> </div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <a class="code" href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> convLayer =</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  network->AddConvolution2dLayer(descriptor,</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  weights,</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a>(biases),</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  layerName.c_str());</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  <a class="code" href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span> </div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer->GetInputSlot(0));</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  convLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));</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>  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="l00418"></a><span class="lineno"> 418</span>  convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);</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>  std::vector<armnn::BackendId> preferredBackends { <span class="stringliteral">"CpuRef"</span> };</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <a class="code" href="namespacearmnn.xhtml#a5b6893cda5b69359a4244c06054da18f">armnn::ModelOptions</a> modelOptions;</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <a class="code" href="structarmnn_1_1_optimizer_options.xhtml">armnn::OptimizerOptions</a> optimizerOptions(<span class="keyword">false</span>, <span class="keyword">false</span>, <span class="keyword">false</span>, <span class="keyword">false</span>, modelOptions);</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  std::vector<std::string> errorMessages;</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>  <span class="comment">// optimize the network.</span></div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network,</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  preferredBackends,</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  runtime->GetDeviceSpec(),</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  optimizerOptions,</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<std::vector<std::string></a>&>(errorMessages));</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span> </div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < 2; ++i)</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  {</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <a class="code" href="namespacearmnn.xhtml#a5b6893cda5b69359a4244c06054da18f">armnn::ModelOptions</a> optimizedModelOptions;</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="keyword">auto</span> copy = <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a>(<span class="keyword">new</span> <a class="code" href="classarmnn_1_1_i_optimized_network.xhtml">armnn::IOptimizedNetwork</a>(*optNet.get(), optimizedModelOptions),</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  &<a class="code" href="classarmnn_1_1_i_optimized_network.xhtml#a58ee539cf95c1e99fe4f54ef6e8bbd05">armnn::IOptimizedNetwork::Destroy</a>);</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span> </div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  CHECK(copy);</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>  <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">armnn::NetworkId</a> netId;</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  std::string errorMessage;</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>  CHECK(<a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">armnn::Status::Success</a> == runtime->LoadNetwork(netId, std::move(copy), errorMessage));</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span> </div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <span class="comment">// Record the networkID for the loaded network</span></div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  networkIds.emplace_back(netId);</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>  <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">armnn::NetworkId</a> optNetId;</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  std::string errorMessage;</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span> </div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="comment">// Load the original optNet</span></div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  CHECK(<a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">armnn::Status::Success</a> == runtime->LoadNetwork(optNetId, std::move(optNet), errorMessage));</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span> </div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  std::vector<float> inputData = GenerateRandomData<float>(runtime->GetInputTensorInfo(optNetId, 0).GetNumElements());</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  std::vector<float> outputData(runtime->GetOutputTensorInfo(optNetId, 0).GetNumElements());</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span> </div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = runtime->GetInputTensorInfo(optNetId, 0);</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">armnn::InputTensors</a> inputTensors</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  {</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  {</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(inputTensorInfo, inputData.data())</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  }</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  };</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">armnn::OutputTensors</a> outputTensors</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  {</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  {</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  0, <a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(optNetId, 0), outputData.data())</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  }</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  };</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  runtime->EnqueueWorkload(optNetId, inputTensors, outputTensors);</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  runtime->UnloadNetwork(optNetId);</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span> </div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <span class="comment">// Record the networkID for the loaded network</span></div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < networkIds.size(); ++i)</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  {</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">armnn::NetworkId</a> netId = networkIds[i];</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  std::vector<float> copyOutputData(runtime->GetOutputTensorInfo(netId, 0).GetNumElements());</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span> </div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo2 = runtime->GetInputTensorInfo(netId, 0);</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  inputTensorInfo2.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">armnn::InputTensors</a> copyInputTensors</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  {</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  {</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(inputTensorInfo2, inputData.data())</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  }</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>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">armnn::OutputTensors</a> copyOutputTensors</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  {</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  {</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  0, <a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), copyOutputData.data())</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>  };</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  runtime->EnqueueWorkload(netId, copyInputTensors, copyOutputTensors);</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  runtime->UnloadNetwork(netId);</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span> </div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <span class="comment">// Check results are identical to "original" version</span></div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> j = 0; j < outputData.size(); ++j)</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  {</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  CHECK(outputData[j] == copyOutputData[j]);</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  }</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  }</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span> }</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span> </div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span> }</div><div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Convolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00537">Descriptors.hpp:537</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00547">Descriptors.hpp:547</a></div></div>
<div class="ttc" id="_ref_workload_factory_8hpp_xhtml"><div class="ttname"><a href="_ref_workload_factory_8hpp.xhtml">RefWorkloadFactory.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Convolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00549">Descriptors.hpp:549</a></div></div>
<div class="ttc" id="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#l00049">Runtime.cpp:49</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00066">INetwork.hpp:66</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a></div><div class="ttdoc">CPU Execution: Reference C++ kernels. </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="_deprecated_8hpp_xhtml_ab66a241a0ed3ee89c866e777b035d0ed"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_BEGIN</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00033">Deprecated.hpp:33</a></div></div>
<div class="ttc" id="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="namespacearmnn_xhtml_a5b6893cda5b69359a4244c06054da18f"><div class="ttname"><a href="namespacearmnn.xhtml#a5b6893cda5b69359a4244c06054da18f">armnn::ModelOptions</a></div><div class="ttdeci">std::vector< BackendOptions > ModelOptions</div><div class="ttdef"><b>Definition:</b> <a href="_backend_options_8hpp_source.xhtml#l00018">BackendOptions.hpp:18</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a></div><div class="ttdoc">A Convolution2dDescriptor for the Convolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00499">Descriptors.hpp:499</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</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#l00033">IRuntime.hpp:33</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aac61f2e17250a818dee4d12b112aa88f"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aac61f2e17250a818dee4d12b112aa88f">armnn::LayerType::Normalization</a></div></div>
<div class="ttc" id="_optimized_network_tests_8cpp_xhtml_a833eb192dab47109dc8413ee16a6ad57"><div class="ttname"><a href="_optimized_network_tests_8cpp.xhtml#a833eb192dab47109dc8413ee16a6ad57">TEST_SUITE</a></div><div class="ttdeci">TEST_SUITE("OptimizedNetwork")</div><div class="ttdef"><b>Definition:</b> <a href="_optimized_network_tests_8cpp_source.xhtml#l00015">OptimizedNetworkTests.cpp:15</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_aa01bce88f89975a5a031db4cc8861527"><div class="ttname"><a href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">armnn::InputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class ConstTensor > > InputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00392">Tensor.hpp:392</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Convolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00533">Descriptors.hpp:533</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::Convolution2dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation along y axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00545">Descriptors.hpp:545</a></div></div>
<div class="ttc" id="backends_2backends_common_2test_2_common_test_utils_8hpp_xhtml"><div class="ttname"><a href="backends_2backends_common_2test_2_common_test_utils_8hpp.xhtml">CommonTestUtils.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &tensorInfo)=0</div></div>
<div class="ttc" id="classarmnn_1_1_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#l00319">Tensor.hpp:319</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Convolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00535">Descriptors.hpp:535</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00539">Descriptors.hpp:539</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38"><div class="ttname"><a href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">armnn::Status::Success</a></div></div>
<div class="ttc" id="_deprecated_8hpp_xhtml_ad762b11b48e5c1d1c1743f529485728a"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_END</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00034">Deprecated.hpp:34</a></div></div>
<div class="ttc" id="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#l01847">Network.cpp:1847</a></div></div>
<div class="ttc" id="_graph_8hpp_xhtml"><div class="ttname"><a href="_graph_8hpp.xhtml">Graph.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_optimized_network_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_optimized_network.xhtml">armnn::IOptimizedNetwork</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00799">INetwork.hpp:799</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">armnn::Compute::Undefined</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a0d8160388a127c1a23b37bc88dc6e2ec"><div class="ttname"><a href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">armnn::NetworkId</a></div><div class="ttdeci">int NetworkId</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00027">IRuntime.hpp:27</a></div></div>
<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00327">Tensor.hpp:327</a></div></div>
<div class="ttc" id="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#l00393">Tensor.hpp:393</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#l00242">INetwork.hpp:242</a></div></div>
<div class="ttc" id="classarmnn_1_1_ref_workload_factory_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_workload_factory.xhtml">armnn::RefWorkloadFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_workload_factory_8hpp_source.xhtml#l00030">RefWorkloadFactory.hpp:30</a></div></div>
<div class="ttc" id="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="structarmnn_1_1_optimizer_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_optimizer_options.xhtml">armnn::OptimizerOptions</a></div><div class="ttdoc">ArmNN performs an optimization on each model/network before it gets loaded for execution. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00137">INetwork.hpp:137</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#l00036">Descriptors.hpp:36</a></div></div>
<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00541">Descriptors.hpp:541</a></div></div>
<div class="ttc" id="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#l00030">Graph.hpp:30</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#l00077">IRuntime.hpp:77</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a6a2659750d6161b693d0e51616791959"><div class="ttname"><a href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">armnn::GetGraphForTesting</a></div><div class="ttdeci">Graph & GetGraphForTesting(IOptimizedNetwork *optNet)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8cpp_source.xhtml#l00049">TestUtils.cpp:49</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::Convolution2dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation along x axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00543">Descriptors.hpp:543</a></div></div>
<div class="ttc" id="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="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot & GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8ffca1e21bdfa7f945617acd606aac91"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">armnn::TensorInfo::SetConstant</a></div><div class="ttdeci">void SetConstant(const bool IsConstant=true)</div><div class="ttdoc">Marks the data corresponding to this tensor info as constant. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00514">Tensor.cpp:514</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot & GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index. </div></div>
<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00241">INetwork.hpp:241</a></div></div>
<div class="ttc" id="classarmnn_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="structarmnn_1_1_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a></div><div class="ttdoc">A NormalizationDescriptor for the NormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00734">Descriptors.hpp:734</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_a5a989a5f9aeb2935ba932b7f8312fe0c"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a5a989a5f9aeb2935ba932b7f8312fe0c">armnn::Graph::AllocateDynamicBuffers</a></div><div class="ttdeci">Status AllocateDynamicBuffers()</div><div class="ttdoc">Allocates memory for all tensors under output tensor handers of each layer. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_8cpp_source.xhtml#l00181">Graph.cpp:181</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a464f0ff87b1aabf71febaa71321dd40b"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create(NetworkOptions networkOptions={})</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00476">Network.cpp:476</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_optimized_network_xhtml_a58ee539cf95c1e99fe4f54ef6e8bbd05"><div class="ttname"><a href="classarmnn_1_1_i_optimized_network.xhtml#a58ee539cf95c1e99fe4f54ef6e8bbd05">armnn::IOptimizedNetwork::Destroy</a></div><div class="ttdeci">static void Destroy(IOptimizedNetwork *network)</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00500">Network.cpp:500</a></div></div>
<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::SoftmaxDescriptor</a></div><div class="ttdoc">A SoftmaxDescriptor for the SoftmaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00150">Descriptors.hpp:150</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Convolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00531">Descriptors.hpp:531</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
</div><!-- fragment --></div><!-- contents -->
</div><!-- doc-content -->
<!-- start footer part -->
<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
<ul>
<li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_0f3cdec46afbc61a1ded8e1687c9c9a0.xhtml">backends</a></li><li class="navelem"><a class="el" href="dir_797a213d7d01b98ef12d53b0820ea64e.xhtml">backendsCommon</a></li><li class="navelem"><a class="el" href="dir_28bfe507f7e135bdae07c2a6b7f66696.xhtml">test</a></li><li class="navelem"><a class="el" href="_optimized_network_tests_8cpp.xhtml">OptimizedNetworkTests.cpp</a></li>
<li class="footer">Generated on Tue May 24 2022 11:27:13 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>
|
