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
|
<!-- Copyright (c) 2020 ARM Limited. -->
<!-- -->
<!-- SPDX-License-Identifier: MIT -->
<!-- -->
<!-- HTML header for doxygen 1.8.13-->
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
<meta name="generator" content="Doxygen 1.8.13"/>
<meta name="robots" content="NOINDEX, NOFOLLOW" />
<meta name="viewport" content="width=device-width, initial-scale=1"/>
<title>ArmNN: src/armnn/test/optimizations/PermuteAndBatchToSpaceAsDepthToSpaceTests.cpp Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="navtree.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="resize.js"></script>
<script type="text/javascript" src="navtreedata.js"></script>
<script type="text/javascript" src="navtree.js"></script>
<script type="text/javascript">
$(document).ready(initResizable);
</script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
extensions: ["tex2jax.js"],
jax: ["input/TeX","output/HTML-CSS"],
});
</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
<link href="stylesheet.css" rel="stylesheet" type="text/css"/>
</head>
<body>
<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
<div id="titlearea">
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
<img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/>
<td style="padding-left: 0.5em;">
<div id="projectname">
 <span id="projectnumber">21.02</span>
</div>
</td>
</tr>
</tbody>
</table>
</div>
<!-- end header part -->
<!-- Generated by Doxygen 1.8.13 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
<script type="text/javascript" src="menudata.js"></script>
<script type="text/javascript" src="menu.js"></script>
<script type="text/javascript">
$(function() {
initMenu('',true,false,'search.php','Search');
$(document).ready(function() { init_search(); });
});
</script>
<div id="main-nav"></div>
</div><!-- top -->
<div id="side-nav" class="ui-resizable side-nav-resizable">
<div id="nav-tree">
<div id="nav-tree-contents">
<div id="nav-sync" class="sync"></div>
</div>
</div>
<div id="splitbar" style="-moz-user-select:none;"
class="ui-resizable-handle">
</div>
</div>
<script type="text/javascript">
$(document).ready(function(){initNavTree('_permute_and_batch_to_space_as_depth_to_space_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">PermuteAndBatchToSpaceAsDepthToSpaceTests.cpp</div> </div>
</div><!--header-->
<div class="contents">
<a href="_permute_and_batch_to_space_as_depth_to_space_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 © 2019 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 "../TestUtils.hpp"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_network_8hpp.xhtml">Network.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_optimizer_8hpp.xhtml">Optimizer.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 <boost/test/unit_test.hpp></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="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <a class="code" href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a>(<a class="code" href="classarmnn_1_1_optimizer.xhtml">Optimizer</a>)</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn_1_1optimizations.xhtml">armnn::optimizations</a>;</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> </div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> {</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"></span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment">/// Shared function for the below tests, so that we test the same network in both cases.</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"></span>std::unique_ptr<NetworkImpl> CreateTestNetworkImpl()</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> {</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  std::unique_ptr<NetworkImpl> network(<span class="keyword">new</span> <a class="code" href="classarmnn_1_1_network_impl.xhtml">NetworkImpl</a>());</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keyword">auto</span> input = network->AddInputLayer(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo({ 1, 2, 3, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  input->GetOutputSlot(0).SetTensorInfo(inputInfo);</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="comment">// Insert Permute which swaps batches and channels dimensions</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="keyword">auto</span> permute = network->AddPermuteLayer(<a class="code" href="structarmnn_1_1_permute_descriptor.xhtml">PermuteDescriptor</a>(<a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a>{ 3, 1, 2, 0 }), <span class="stringliteral">"permute"</span>);</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> permuteInfo({ 4, 2, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  permute->GetOutputSlot(0).SetTensorInfo(permuteInfo);</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  input->GetOutputSlot(0).Connect(permute->GetInputSlot(0));</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> </div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="comment">// Insert BatchToSpace</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">BatchToSpaceNdDescriptor</a> batchToSpaceDesc;</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">m_BlockShape</a> = { 2, 2 };</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="keyword">auto</span> batchToSpace = network->AddBatchToSpaceNdLayer(batchToSpaceDesc, <span class="stringliteral">"batchToSpace"</span>);</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> batchToSpaceInfo({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  batchToSpace->GetOutputSlot(0).SetTensorInfo(batchToSpaceInfo);</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  permute->GetOutputSlot(0).Connect(batchToSpace->GetInputSlot(0));</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> </div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keyword">auto</span> output = network->AddOutputLayer(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  batchToSpace->GetOutputSlot(0).Connect(output->GetInputSlot(0));</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordflow">return</span> network;</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> }</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="comment"></span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="comment">/// Shared function for the below tests, so that we test the same network in both cases.</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="comment"></span>std::unique_ptr<NetworkImpl> CreateTransposeTestNetworkImpl()</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> {</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="comment">// Create a network</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  std::unique_ptr<NetworkImpl> network(<span class="keyword">new</span> <a class="code" href="classarmnn_1_1_network_impl.xhtml">NetworkImpl</a>());</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keyword">auto</span> input = network->AddInputLayer(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo({ 1, 2, 3, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  input->GetOutputSlot(0).SetTensorInfo(inputInfo);</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="comment">// Insert Permute which swaps batches and channels dimensions</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keyword">auto</span> permute = network->AddTransposeLayer(<a class="code" href="structarmnn_1_1_transpose_descriptor.xhtml">TransposeDescriptor</a>(<a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a>{ 3, 1, 2, 0 }), <span class="stringliteral">"permute"</span>);</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> permuteInfo({ 4, 2, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  permute->GetOutputSlot(0).SetTensorInfo(permuteInfo);</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  input->GetOutputSlot(0).Connect(permute->GetInputSlot(0));</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="comment">// Insert BatchToSpace</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">BatchToSpaceNdDescriptor</a> batchToSpaceDesc;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">m_BlockShape</a> = { 2, 2 };</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keyword">auto</span> batchToSpace = network->AddBatchToSpaceNdLayer(batchToSpaceDesc, <span class="stringliteral">"batchToSpace"</span>);</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> batchToSpaceInfo({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  batchToSpace->GetOutputSlot(0).SetTensorInfo(batchToSpaceInfo);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  permute->GetOutputSlot(0).Connect(batchToSpace->GetInputSlot(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="keyword">auto</span> output = network->AddOutputLayer(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  batchToSpace->GetOutputSlot(0).Connect(output->GetInputSlot(0));</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordflow">return</span> network;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> }</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> </div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> } <span class="comment">// namespace</span></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> <span class="comment"></span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> <span class="comment">/// Tests that the optimization performed by PermuteAndBatchToSpaceAsDepthToSpace is as expected.</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> <span class="comment">/// Note this does not ensure the correctness of the optimization - that is done in the below test.</span></div><div class="line"><a name="l00086"></a><span class="lineno"><a class="line" href="_permute_and_batch_to_space_as_depth_to_space_tests_8cpp.xhtml#ac9ae3545393ad3f2dac5c8f789bbbfbf"> 86</a></span> <span class="comment"></span><a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(PermuteAndBatchToSpaceAsDepthToSpaceOptimizerTest)</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>  std::unique_ptr<NetworkImpl> network = CreateTestNetworkImpl();</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph = network.get()->GetGraph();</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>  <span class="comment">// Confirm initial graph is as we expect</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(), &IsLayerOfType<InputLayer>, &IsLayerOfType<PermuteLayer>,</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  &IsLayerOfType<BatchToSpaceNdLayer>, &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="comment">// Perform the optimization which should merge the two layers into a DepthToSpace</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <a class="code" href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a>(graph, <a class="code" href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">MakeOptimizations</a>(<a class="code" href="namespacearmnn_1_1optimizations.xhtml#a17d1279f5f8e3b92c328b1ed3b6fd549">PermuteAndBatchToSpaceAsDepthToSpace</a>()));</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="comment">// Check that the replacement has been made as expected</span></div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keyword">auto</span> checkDepthToSpace = [](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer) -> <span class="keywordtype">bool</span> {</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordflow">return</span> IsLayerOfType<DepthToSpaceLayer>(layer) &&</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  static_cast<const DepthToSpaceLayer*>(layer)->GetParameters().m_BlockSize == 2 &&</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_depth_to_space_layer.xhtml">DepthToSpaceLayer</a>*<span class="keyword">></span>(layer)->GetParameters().m_DataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a> &&</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  layer->GetOutputHandler().GetTensorInfo() == <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</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>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(), &IsLayerOfType<InputLayer>, checkDepthToSpace,</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> </div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="comment">// Check the new layer has the two merged layers listed as related layers</span></div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  std::list<std::string> testRelatedLayers = { <span class="stringliteral">"batchToSpace"</span>, <span class="stringliteral">"permute"</span> };</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  BOOST_TEST(CheckRelatedLayers<DepthToSpaceLayer>(graph, testRelatedLayers));</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"></span></div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> <span class="comment">/// Tests that the optimization performed by PermuteAndBatchToSpaceAsDepthToSpace is as expected.</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> <span class="comment">/// Note this does not ensure the correctness of the optimization - that is done in the below test.</span></div><div class="line"><a name="l00116"></a><span class="lineno"><a class="line" href="_permute_and_batch_to_space_as_depth_to_space_tests_8cpp.xhtml#ad5937fc491fa54298cbd8fa7d599993f"> 116</a></span> <span class="comment"></span><a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(TransposeAndBatchToSpaceAsDepthToSpaceOptimizerTest)</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> {</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  std::unique_ptr<NetworkImpl> network = CreateTransposeTestNetworkImpl();</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph = network.get()->GetGraph();</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> </div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="comment">// Confirm initial graph is as we expect</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(), &IsLayerOfType<InputLayer>, &IsLayerOfType<TransposeLayer>,</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  &IsLayerOfType<BatchToSpaceNdLayer>, &IsLayerOfType<OutputLayer>));</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>  <span class="comment">// Perform the optimization which should merge the two layers into a DepthToSpace</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <a class="code" href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a>(graph, <a class="code" href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">MakeOptimizations</a>(<a class="code" href="namespacearmnn_1_1optimizations.xhtml#a98f54d4391347d517c7a7869e7707203">TransposeAndBatchToSpaceAsDepthToSpace</a>()));</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>  <span class="comment">// Check that the replacement has been made as expected</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="keyword">auto</span> checkDepthToSpace = [](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer) -> <span class="keywordtype">bool</span> {</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordflow">return</span> IsLayerOfType<DepthToSpaceLayer>(layer) &&</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  static_cast<const DepthToSpaceLayer*>(layer)->GetParameters().m_BlockSize == 2 &&</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_depth_to_space_layer.xhtml">DepthToSpaceLayer</a>*<span class="keyword">></span>(layer)->GetParameters().m_DataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a> &&</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  layer->GetOutputHandler().GetTensorInfo() == <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</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> </div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(), graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(), &IsLayerOfType<InputLayer>, checkDepthToSpace,</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> </div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="comment">// Check the new layer has the two merged layers listed as related layers</span></div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  std::list<std::string> testRelatedLayers = { <span class="stringliteral">"batchToSpace"</span>, <span class="stringliteral">"permute"</span> };</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  BOOST_TEST(CheckRelatedLayers<DepthToSpaceLayer>(graph, testRelatedLayers));</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> </div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> <span class="comment">// This unit test needs the reference backend, it's not available if the reference backend is not built</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> <span class="preprocessor">#if defined(ARMNNREF_ENABLED)</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> <span class="comment"></span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> <span class="comment">/// Shared function for the below tests, so that we test the same network in both cases.</span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="comment"></span><a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> CreateTestNetwork()</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> {</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="comment">// Create a network</span></div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> </div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keyword">auto</span> input = network->AddInputLayer(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo({ 1, 2, 3, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  input->GetOutputSlot(0).SetTensorInfo(inputInfo);</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="comment">// Insert Permute which swaps batches and channels dimensions</span></div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keyword">auto</span> permute = network->AddPermuteLayer(<a class="code" href="structarmnn_1_1_permute_descriptor.xhtml">PermuteDescriptor</a>(<a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a>{ 3, 1, 2, 0 }), <span class="stringliteral">"permute"</span>);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> permuteInfo({ 4, 2, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  permute->GetOutputSlot(0).SetTensorInfo(permuteInfo);</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  input->GetOutputSlot(0).Connect(permute->GetInputSlot(0));</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> </div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="comment">// Insert BatchToSpace</span></div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">BatchToSpaceNdDescriptor</a> batchToSpaceDesc;</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">m_BlockShape</a> = { 2, 2 };</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="keyword">auto</span> batchToSpace = network->AddBatchToSpaceNdLayer(batchToSpaceDesc, <span class="stringliteral">"batchToSpace"</span>);</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> batchToSpaceInfo({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  batchToSpace->GetOutputSlot(0).SetTensorInfo(batchToSpaceInfo);</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  permute->GetOutputSlot(0).Connect(batchToSpace->GetInputSlot(0));</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> </div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keyword">auto</span> output = network->AddOutputLayer(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  batchToSpace->GetOutputSlot(0).Connect(output->GetInputSlot(0));</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> </div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keywordflow">return</span> network;</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> }</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> <span class="comment"></span></div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> <span class="comment">/// Shared function for the below tests, so that we test the same network in both cases.</span></div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> <span class="comment"></span><a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> CreateTransposeTestNetwork()</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>  <span class="comment">// Create a network</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> </div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keyword">auto</span> input = network->AddInputLayer(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo({ 1, 2, 3, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  input->GetOutputSlot(0).SetTensorInfo(inputInfo);</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> </div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="comment">// Insert Permute which swaps batches and channels dimensions</span></div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keyword">auto</span> permute = network->AddTransposeLayer(<a class="code" href="structarmnn_1_1_transpose_descriptor.xhtml">TransposeDescriptor</a>(<a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a>{ 3, 1, 2, 0 }), <span class="stringliteral">"permute"</span>);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> permuteInfo({ 4, 2, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  permute->GetOutputSlot(0).SetTensorInfo(permuteInfo);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  input->GetOutputSlot(0).Connect(permute->GetInputSlot(0));</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="comment">// Insert BatchToSpace</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">BatchToSpaceNdDescriptor</a> batchToSpaceDesc;</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">m_BlockShape</a> = { 2, 2 };</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  batchToSpaceDesc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keyword">auto</span> batchToSpace = network->AddBatchToSpaceNdLayer(batchToSpaceDesc, <span class="stringliteral">"batchToSpace"</span>);</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> batchToSpaceInfo({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  batchToSpace->GetOutputSlot(0).SetTensorInfo(batchToSpaceInfo);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  permute->GetOutputSlot(0).Connect(batchToSpace->GetInputSlot(0));</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>  <span class="keyword">auto</span> output = network->AddOutputLayer(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  batchToSpace->GetOutputSlot(0).Connect(output->GetInputSlot(0));</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> </div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordflow">return</span> network;</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> <span class="comment"></span></div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> <span class="comment">/// Tests that a optimization performed by PermuteAndBatchToSpaceAsDepthToSpace does not change the behaviour</span></div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> <span class="comment">/// of the network (i.e. it still produces the correct output).</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> <span class="comment"></span><a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(PermuteAndBatchToSpaceAsDepthToSpaceCorrectnessTest)</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> {</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = CreateTestNetwork();</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> </div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime = <a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(<a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a>());</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optimizedNetwork = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">Compute::CpuRef</a> }, runtime->GetDeviceSpec());</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="comment">// Confirm that the optimization has actually taken place</span></div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& optGraph = <a class="code" href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">GetGraphForTesting</a>(optimizedNetwork.get());</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(optGraph.cbegin(), optGraph.cend(), &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  &IsLayerOfType<DepthToSpaceLayer>, &IsLayerOfType<OutputLayer>));</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>  <span class="comment">// Load the graph into a runtime so we can check it produces the correct output</span></div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  runtime->LoadNetwork(netId, std::move(optimizedNetwork));</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> </div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  std::vector<float> inputData{</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="comment">// Each row here is a row of pixels where each pixel has 4 channels</span></div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="comment">// clang-format off</span></div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  1.0f, 2.0f, 3.0f, 4.0f, 10.0f, 20.0f, 30.0f, 40.0f, 100.0f, 200.0f, 300.0f, 400.0f,</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  -1.0f, -2.0f, -3.0f, -4.0f, -10.0f, -20.0f, -30.0f, -40.0f, -100.0f, -200.0f, -300.0f, -400.0f,</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="comment">// clang-format on</span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  };</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> input(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 3, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>), inputData);</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputs = { { 0, input } };</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  std::vector<float> outputData(4 * 6);</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a> output(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>), outputData.data());</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputs = { { 0, output } };</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  runtime->EnqueueWorkload(netId, inputs, outputs);</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> </div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="comment">// Check the output is as expected.</span></div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="comment">// Note this output has been generated by running the network *without* the optimization.</span></div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  std::vector<float> expectedOutput = {</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="comment">// Rows and columns here match exactly with the tensor, as there is only 1 channel.</span></div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="comment">// clang-format off</span></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  1.0f, 2.0f, 10.0f, 20.0f, 100.0f, 200.0f,</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  3.0f, 4.0f, 30.0f, 40.0f, 300.0f, 400.0f,</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> </div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  -1.0f, -2.0f, -10.0f, -20.0f, -100.0f, -200.0f,</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  -3.0f, -4.0f, -30.0f, -40.0f, -300.0f, -400.0f,</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="comment">// clang-format on</span></div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  };</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  BOOST_TEST(outputData == expectedOutput);</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> <span class="comment"></span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> <span class="comment">/// Tests that a optimization performed by PermuteAndBatchToSpaceAsDepthToSpace does not change the behaviour</span></div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> <span class="comment">/// of the network (i.e. it still produces the correct output).</span></div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> <span class="comment"></span><a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(TransposeAndBatchToSpaceAsDepthToSpaceCorrectnessTest)</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="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = CreateTransposeTestNetwork();</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> </div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime = <a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(<a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a>());</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optimizedNetwork = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">Compute::CpuRef</a> }, runtime->GetDeviceSpec());</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span> </div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="comment">// Confirm that the optimization has actually taken place</span></div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& optGraph = <a class="code" href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">GetGraphForTesting</a>(optimizedNetwork.get());</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(optGraph.cbegin(), optGraph.cend(), &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  &IsLayerOfType<DepthToSpaceLayer>, &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> </div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="comment">// Load the graph into a runtime so we can check it produces the correct output</span></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  runtime->LoadNetwork(netId, std::move(optimizedNetwork));</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>  std::vector<float> inputData{</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="comment">// Each row here is a row of pixels where each pixel has 4 channels</span></div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="comment">// clang-format off</span></div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  1.0f, 2.0f, 3.0f, 4.0f, 10.0f, 20.0f, 30.0f, 40.0f, 100.0f, 200.0f, 300.0f, 400.0f,</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  -1.0f, -2.0f, -3.0f, -4.0f, -10.0f, -20.0f, -30.0f, -40.0f, -100.0f, -200.0f, -300.0f, -400.0f,</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="comment">// clang-format on</span></div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  };</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> input(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 3, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>), inputData);</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputs = { { 0, input } };</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  std::vector<float> outputData(4 * 6);</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a> output(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 4, 6, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>), outputData.data());</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputs = { { 0, output } };</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  runtime->EnqueueWorkload(netId, inputs, outputs);</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> </div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="comment">// Check the output is as expected.</span></div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="comment">// Note this output has been generated by running the network *without* the optimization.</span></div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  std::vector<float> expectedOutput = {</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="comment">// Rows and columns here match exactly with the tensor, as there is only 1 channel.</span></div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="comment">// clang-format off</span></div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  1.0f, 2.0f, 10.0f, 20.0f, 100.0f, 200.0f,</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  3.0f, 4.0f, 30.0f, 40.0f, 300.0f, 400.0f,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span> </div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  -1.0f, -2.0f, -10.0f, -20.0f, -100.0f, -200.0f,</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  -3.0f, -4.0f, -30.0f, -40.0f, -300.0f, -400.0f,</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="comment">// clang-format on</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>  BOOST_TEST(outputData == expectedOutput);</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="preprocessor">#endif</span></div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> </div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span> <a class="code" href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a>()</div><div class="ttc" id="_output_shape_of_squeeze_8cpp_xhtml_ae3a6cb217a792718f2bd0e8f45e3ca9e"><div class="ttname"><a href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE(TensorflowLiteParser)</div></div>
<div class="ttc" id="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#l00037">Runtime.cpp:37</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="namespacearmnn_xhtml_aa7427025a851113a492de0b68b23d22a"><div class="ttname"><a href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">armnn::MakeOptimizations</a></div><div class="ttdeci">Optimizer::Optimizations MakeOptimizations(Args &&... args)</div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_8hpp_source.xhtml#l00043">Optimizer.hpp:43</a></div></div>
<div class="ttc" id="namespacearmnn_1_1optimizations_xhtml_a98f54d4391347d517c7a7869e7707203"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml#a98f54d4391347d517c7a7869e7707203">armnn::optimizations::TransposeAndBatchToSpaceAsDepthToSpace</a></div><div class="ttdeci">OptimizeForConnection< TransposeLayer, BatchToSpaceNdLayer, PermuteAndBatchToSpaceAsDepthToSpaceImpl< TransposeLayer > > TransposeAndBatchToSpaceAsDepthToSpace</div><div class="ttdef"><b>Definition:</b> <a href="_permute_and_batch_to_space_as_depth_to_space_8hpp_source.xhtml#l00104">PermuteAndBatchToSpaceAsDepthToSpace.hpp:104</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="namespacearmnn_1_1optimizations_xhtml"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml">armnn::optimizations</a></div><div class="ttdef"><b>Definition:</b> <a href="_add_broadcast_reshape_layer_8hpp_source.xhtml#l00014">AddBroadcastReshapeLayer.hpp:14</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_a98b1109a9006f8cc7d4566146a3bd737"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">armnn::Graph::cbegin</a></div><div class="ttdeci">ConstIterator cbegin() const</div><div class="ttdoc">Returns const iterator pointing to the beginning of the list. Lowercase for range-based for loops...</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00172">Graph.hpp:172</a></div></div>
<div class="ttc" id="classarmnn_1_1_optimizer_xhtml_a1f48ba622b76ea04d15c9b62f642bf08"><div class="ttname"><a href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a></div><div class="ttdeci">static void Pass(Graph &graph, const Optimizations &optimizations)</div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_8cpp_source.xhtml#l00016">Optimizer.cpp:16</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#l00026">IRuntime.hpp:26</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::BatchToSpaceNdDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00700">Descriptors.hpp:700</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_aa01bce88f89975a5a031db4cc8861527"><div class="ttname"><a href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">armnn::InputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class ConstTensor > > InputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00340">Tensor.hpp:340</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a83015160d8c67d5d77735eb0d4033d9a"><div class="ttname"><a href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">armnn::NetworkId</a></div><div class="ttdeci">int NetworkId</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00020">IRuntime.hpp:20</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__software__tools_8dox_source.xhtml#l00006">01_00_software_tools.dox:6</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">armnn::BatchToSpaceNdDescriptor</a></div><div class="ttdoc">A BatchToSpaceNdDescriptor for the BatchToSpaceNdLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00673">Descriptors.hpp:673</a></div></div>
<div class="ttc" id="classarmnn_1_1_network_impl_xhtml"><div class="ttname"><a href="classarmnn_1_1_network_impl.xhtml">armnn::NetworkImpl</a></div><div class="ttdoc">Private implementation of INetwork. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8hpp_source.xhtml#l00031">Network.hpp:31</a></div></div>
<div class="ttc" id="_optimizer_8hpp_xhtml"><div class="ttname"><a href="_optimizer_8hpp.xhtml">Optimizer.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and a mutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00306">Tensor.hpp:306</a></div></div>
<div class="ttc" id="namespacearmnn_1_1optimizations_xhtml_a17d1279f5f8e3b92c328b1ed3b6fd549"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml#a17d1279f5f8e3b92c328b1ed3b6fd549">armnn::optimizations::PermuteAndBatchToSpaceAsDepthToSpace</a></div><div class="ttdeci">OptimizeForConnection< PermuteLayer, BatchToSpaceNdLayer, PermuteAndBatchToSpaceAsDepthToSpaceImpl< PermuteLayer > > PermuteAndBatchToSpaceAsDepthToSpace</div><div class="ttdef"><b>Definition:</b> <a href="_permute_and_batch_to_space_as_depth_to_space_8hpp_source.xhtml#l00102">PermuteAndBatchToSpaceAsDepthToSpace.hpp:102</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#l01502">Network.cpp:1502</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml_a02e143524aefddd40b485fcf7dea6696"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">armnn::BatchToSpaceNdDescriptor::m_BlockShape</a></div><div class="ttdeci">std::vector< unsigned int > m_BlockShape</div><div class="ttdoc">Block shape values. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00696">Descriptors.hpp:696</a></div></div>
<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00314">Tensor.hpp:314</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a8f091a512915d1cb29a4ebf13dfc53ea"><div class="ttname"><a href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">armnn::OutputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class Tensor > > OutputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00341">Tensor.hpp:341</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a674efcf6cbdb9e831d653ff0e821fb38"><div class="ttname"><a href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a></div><div class="ttdeci">std::unique_ptr< IOptimizedNetwork, void(*)(IOptimizedNetwork *network)> IOptimizedNetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00174">INetwork.hpp:174</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a10d15f3df1ab52b3b915a4be1dbf386b"><div class="ttname"><a href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">armnn::BOOST_AUTO_TEST_CASE</a></div><div class="ttdeci">BOOST_AUTO_TEST_CASE(CheckConvolution2dLayer)</div><div class="ttdef"><b>Definition:</b> <a href="_const_tensor_layer_visitor_8cpp_source.xhtml#l00268">ConstTensorLayerVisitor.cpp:268</a></div></div>
<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00212">Types.hpp:212</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml">armnn::Graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00029">Graph.hpp:29</a></div></div>
<div class="ttc" id="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#l00043">IRuntime.hpp:43</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#l00025">TestUtils.cpp:25</a></div></div>
<div class="ttc" id="_profiler_tests_8cpp_xhtml_af7f71af5c6c124222dd1c42c5df892f4"><div class="ttname"><a href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE_END()</div></div>
<div class="ttc" id="_network_8hpp_xhtml"><div class="ttname"><a href="_network_8hpp.xhtml">Network.hpp</a></div></div>
<div class="ttc" id="_test_utils_8hpp_xhtml_a0eedb278f57355b47fa983450d4e378c"><div class="ttname"><a href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a></div><div class="ttdeci">bool CheckSequence(const armnn::Graph::ConstIterator first, const armnn::Graph::ConstIterator last)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8hpp_source.xhtml#l00021">TestUtils.hpp:21</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_descriptor.xhtml">armnn::TransposeDescriptor</a></div><div class="ttdoc">A TransposeDescriptor for the TransposeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01263">Descriptors.hpp:1263</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_graph_xhtml_a02fd29b6dc3e21fbe4484362d85893bc"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">armnn::Graph::cend</a></div><div class="ttdeci">ConstIterator cend() const</div><div class="ttdoc">Returns const iterator pointing to the end of the list. Lowercase for range-based for loops...</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00174">Graph.hpp:174</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00173">INetwork.hpp:173</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a464f0ff87b1aabf71febaa71321dd40b"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create(NetworkOptions networkOptions={})</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00510">Network.cpp:510</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml">armnn::Layer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00210">Layer.hpp:210</a></div></div>
<div class="ttc" id="classarmnn_1_1_optimizer_xhtml"><div class="ttname"><a href="classarmnn_1_1_optimizer.xhtml">armnn::Optimizer</a></div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_8hpp_source.xhtml#l00014">Optimizer.hpp:14</a></div></div>
<div class="ttc" id="classarmnn_1_1_depth_to_space_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_depth_to_space_layer.xhtml">armnn::DepthToSpaceLayer</a></div><div class="ttdoc">This layer represents a DepthToSpace operation. </div><div class="ttdef"><b>Definition:</b> <a href="_depth_to_space_layer_8hpp_source.xhtml#l00014">DepthToSpaceLayer.hpp:14</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
<div class="ttc" id="structarmnn_1_1_permute_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_permute_descriptor.xhtml">armnn::PermuteDescriptor</a></div><div class="ttdoc">A PermuteDescriptor for the PermuteLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00118">Descriptors.hpp:118</a></div></div>
</div><!-- fragment --></div><!-- contents -->
</div><!-- doc-content -->
<!-- start footer part -->
<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
<ul>
<li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_e0a84d05c80a2ef4231141dcbbeac5c8.xhtml">armnn</a></li><li class="navelem"><a class="el" href="dir_9d86fd1fbecbedf5bdb69c7e7235fe5f.xhtml">test</a></li><li class="navelem"><a class="el" href="dir_f1cd0e6da811a659c139424442adfb5f.xhtml">optimizations</a></li><li class="navelem"><a class="el" href="_permute_and_batch_to_space_as_depth_to_space_tests_8cpp.xhtml">PermuteAndBatchToSpaceAsDepthToSpaceTests.cpp</a></li>
<li class="footer">Generated on Thu Feb 25 2021 17:27:29 for ArmNN by
<a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 </li>
</ul>
</div>
</body>
</html>
|
