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author | Jan Eilers <jan.eilers@arm.com> | 2021-02-25 17:44:00 +0000 |
---|---|---|
committer | Jan Eilers <jan.eilers@arm.com> | 2021-02-25 18:27:49 +0000 |
commit | fd627ffaec8fd8801d980b4c91ee7c0607ab6aaf (patch) | |
tree | eb4bc8f9b411f30c7655616142b5a4bdd3a1acd0 /21.02/_caffe_parser_8cpp_source.xhtml | |
parent | fb14ebbd68e04876809145296af96f6f41857418 (diff) | |
download | armnn-fd627ffaec8fd8801d980b4c91ee7c0607ab6aaf.tar.gz |
IVGCVSW-5687 Update Doxygen Docu
* Update Doxygen Documentation for 21.02 release
Signed-off-by: Jan Eilers <jan.eilers@arm.com>
Change-Id: I9ed2f9caab038836ea99d7b378d7899fe431a4e5
Diffstat (limited to '21.02/_caffe_parser_8cpp_source.xhtml')
-rw-r--r-- | 21.02/_caffe_parser_8cpp_source.xhtml | 271 |
1 files changed, 271 insertions, 0 deletions
diff --git a/21.02/_caffe_parser_8cpp_source.xhtml b/21.02/_caffe_parser_8cpp_source.xhtml new file mode 100644 index 0000000000..7d42ad3670 --- /dev/null +++ b/21.02/_caffe_parser_8cpp_source.xhtml @@ -0,0 +1,271 @@ +<!-- 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/armnnCaffeParser/CaffeParser.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('_caffe_parser_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">CaffeParser.cpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_caffe_parser_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> <span class="preprocessor">#include "<a class="code" href="_caffe_parser_8hpp.xhtml">CaffeParser.hpp</a>"</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_record_by_record_caffe_parser_8hpp.xhtml">RecordByRecordCaffeParser.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="include_2armnn_caffe_parser_2_version_8hpp.xhtml">armnnCaffeParser/Version.hpp</a>"</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> </div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include "<a class="code" href="_descriptors_8hpp.xhtml">armnn/Descriptors.hpp</a>"</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include "<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>"</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include "<a class="code" href="_utils_8hpp.xhtml">armnn/Utils.hpp</a>"</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include "<a class="code" href="_exceptions_8hpp.xhtml">armnn/Exceptions.hpp</a>"</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include "<a class="code" href="_graph_topological_sort_8hpp.xhtml">GraphTopologicalSort.hpp</a>"</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include "<a class="code" href="_verification_helpers_8hpp.xhtml">VerificationHelpers.hpp</a>"</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> </div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <<a class="code" href="_assert_8hpp.xhtml">armnn/utility/Assert.hpp</a>></span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include <<a class="code" href="_numeric_cast_8hpp.xhtml">armnn/utility/NumericCast.hpp</a>></span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> </div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include <fmt/format.h></span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment">// Caffe</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "caffe/proto/caffe.pb.h"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="comment">// ProtoBuf</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include <google/protobuf/io/coded_stream.h></span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include <google/protobuf/io/zero_copy_stream.h></span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include <google/protobuf/io/zero_copy_stream_impl.h></span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include <google/protobuf/text_format.h></span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include <google/protobuf/stubs/common.h></span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include <google/protobuf/stubs/once.h></span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include <google/protobuf/io/coded_stream.h></span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include <google/protobuf/descriptor.h></span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include <google/protobuf/generated_message_reflection.h></span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor">#include <google/protobuf/reflection_ops.h></span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#include <google/protobuf/wire_format.h></span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> </div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="preprocessor">#include <cmath></span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="preprocessor">#include <iostream></span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="preprocessor">#include <sstream></span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="preprocessor">#include <queue></span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="preprocessor">#include <fcntl.h></span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="comment"></span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="comment">/// Caffe networks are loaded from protobuf files (binary or text) using the protobuf library and the generated</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="comment">/// code from caffe.pb.h. This gives us a caffe::NetParameter which is an in-memory version of the file.</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="comment">/// This contains a flat list of Caffe 'layers' (e.g. convolution, pooling etc.).</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="comment">/// Each layer has inputs (called "bottoms") and outputs (called "tops"). Data flows from bottom to top.</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> <span class="comment">/// The bottoms of a layer refer to the tops of other layers, not their names.</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="comment">/// The names of layers seem to be arbitrary (you could rename a layer and the network wouldn't</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="comment">/// need any other changes).</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="comment">///</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> <span class="comment">/// Some layers (e.g. Relu) can be configured so that their top and bottom are both the same. This is called an</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> <span class="comment">/// "in-place" layer and is a Caffe runtime feature used to reduce memory usage by modifying tensors in-place.</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="comment">/// This isn't relevant to the parser and so we preprocess these layers to convert them to regular layers, to result</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> <span class="comment">/// in a consistent graph structure.</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> <span class="comment"></span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn_caffe_parser.xhtml">armnnCaffeParser</a></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> {</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> <span class="keyword">using namespace </span><a class="code" href="namespacecaffe.xhtml">caffe</a>;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> <span class="keyword">using namespace </span><a class="code" href="namespacestd.xhtml">std</a>;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> <span class="keyword">using namespace </span><a class="code" href="namespacegoogle_1_1protobuf_1_1io.xhtml">google::protobuf::io</a>;</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> ICaffeParser::ICaffeParser() : pCaffeParserImpl(new RecordByRecordCaffeParser()) {}</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> ICaffeParser::~ICaffeParser() = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> </div><div class="line"><a name="l00070"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#a706b8481b6bd660dd3c3898fdf7a2993"> 70</a></span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml">ICaffeParser</a>* ICaffeParser::CreateRaw()</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> {</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordflow">return</span> <span class="keyword">new</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml">ICaffeParser</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> </div><div class="line"><a name="l00075"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#abd42446e41480b0cc9df7ce06af412e3"> 75</a></span> <a class="code" href="namespacearmnn_caffe_parser.xhtml#a33c76910f1980ffaa41c22e0151cce2a">ICaffeParserPtr</a> ICaffeParser::Create()</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> {</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_caffe_parser.xhtml#a33c76910f1980ffaa41c22e0151cce2a">ICaffeParserPtr</a>(CreateRaw(), &ICaffeParser::Destroy);</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> }</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#a5e8137c09390352d2f8b420d147d3b2e"> 80</a></span> <span class="keywordtype">void</span> ICaffeParser::Destroy(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml">ICaffeParser</a>* parser)</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> {</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keyword">delete</span> parser;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> }</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> </div><div class="line"><a name="l00085"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#add49602ee9cd2bd16c1c4ccd25555d8e"> 85</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> ICaffeParser::CreateNetworkFromTextFile(</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile,</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keyword">const</span> std::map<std::string, armnn::TensorShape>& inputShapes,</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keyword">const</span> std::vector<std::string>& requestedOutputs)</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> {</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keywordflow">return</span> pCaffeParserImpl-><a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#add49602ee9cd2bd16c1c4ccd25555d8e">CreateNetworkFromTextFile</a>(graphFile, inputShapes, requestedOutputs);</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> }</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> </div><div class="line"><a name="l00093"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#a56e7dd134fad3b70cb926b447fe2d16e"> 93</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> ICaffeParser::CreateNetworkFromBinaryFile(</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile,</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keyword">const</span> std::map<std::string, armnn::TensorShape>& inputShapes,</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keyword">const</span> std::vector<std::string>& requestedOutputs)</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="keywordflow">return</span> pCaffeParserImpl->CreateNetworkFromBinaryFile(graphFile, inputShapes,requestedOutputs);</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> }</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> </div><div class="line"><a name="l00101"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#a5448fd503a576d279c71aa8340e84b7f"> 101</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> ICaffeParser::CreateNetworkFromString(</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* protoText,</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keyword">const</span> std::map<std::string, armnn::TensorShape>& inputShapes,</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keyword">const</span> std::vector<std::string>& requestedOutputs)</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>  <span class="keywordflow">return</span> pCaffeParserImpl->CreateNetworkFromString(protoText, inputShapes, requestedOutputs);</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> }</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> </div><div class="line"><a name="l00109"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#a8b053a6c449d0814cc831c916c126668"> 109</a></span> <a class="code" href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">BindingPointInfo</a> ICaffeParser::GetNetworkInputBindingInfo(<span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> <span class="keyword"></span>{</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keywordflow">return</span> pCaffeParserImpl->GetNetworkInputBindingInfo(name);</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> </div><div class="line"><a name="l00114"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#a4b1fdcb1985af12dd1848a9ffa5d3271"> 114</a></span> <a class="code" href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">BindingPointInfo</a> ICaffeParser::GetNetworkOutputBindingInfo(<span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> <span class="keyword"></span>{</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keywordflow">return</span> pCaffeParserImpl->GetNetworkOutputBindingInfo(name);</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> </div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> <span class="keyword">namespace</span></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> </div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> <span class="keyword">const</span> <span class="keywordtype">float</span>* GetArrayPtrFromBlob(<span class="keyword">const</span> LayerParameter& layerParam, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> blobIndex)</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> {</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keyword">auto</span> nBlobs = layerParam.blobs_size();</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keywordflow">if</span> (blobIndex >= armnn::numeric_cast<unsigned int>(nBlobs))</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  {</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  fmt::format(<span class="stringliteral">"Expected data blob at index {} in layer {} not found. nBlobs={}. {}"</span>,</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  blobIndex,</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  layerParam.name(),</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  nBlobs,</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  }</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> </div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keyword">const</span> BlobProto& blob = layerParam.blobs(armnn::numeric_cast<int>(blobIndex));</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="keyword">const</span> <span class="keywordtype">float</span>* arrayPtr = blob.data().data();</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keywordflow">return</span> arrayPtr;</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> }</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> <span class="keywordtype">void</span> GetDataFromBlob(<span class="keyword">const</span> LayerParameter& layerParam, vector<float>& outData, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> blobIndex)</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>  <span class="keyword">auto</span> nBlobs = layerParam.blobs_size();</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keywordflow">if</span> (blobIndex >= armnn::numeric_cast<unsigned int>(nBlobs))</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  {</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  fmt::format(<span class="stringliteral">"Expected data blob at index {} in layer {} not found. {}"</span>,</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  blobIndex,</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  layerParam.name(),</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  }</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> </div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keyword">const</span> BlobProto& blob = layerParam.blobs(armnn::numeric_cast<int>(blobIndex));</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keywordtype">size_t</span> blobSize = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">size_t</span>>(blob.data_size());</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keywordflow">if</span> (blobSize != outData.size())</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  {</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  fmt::format(<span class="stringliteral">"Data blob at index {} in layer {} has an unexpected size. "</span></div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="stringliteral">"Expected {} elements but got {} elements. {}"</span>,</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  blobIndex,</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  layerParam.name(),</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  outData.size(),</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  blobSize,</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  }</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> </div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keywordtype">int</span> outSizeInt = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">int</span>>(outData.size());</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < outSizeInt; ++i)</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  {</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  outData[<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(i)] = blob.data(i);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  }</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> }</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> </div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> <span class="keywordtype">size_t</span> SizeOfVectorData(<span class="keyword">const</span> vector<T>& vec)</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> {</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordflow">return</span> vec.size() * <span class="keyword">sizeof</span>(T);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> }</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> </div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> <span class="keywordtype">void</span> ValidateNumInputsOutputs(<span class="keyword">const</span> caffe::LayerParameter& layerParameter,</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numInputs,</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numOutputs)</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> {</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keywordtype">int</span> numInputsActual = layerParameter.bottom_size();</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keywordflow">if</span> (numInputs != armnn::numeric_cast<unsigned int>(numInputsActual))</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="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  fmt::format(<span class="stringliteral">"Invalid number of inputs requested {} for layer {} "</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="stringliteral">"while only {} present. {}"</span>,</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  numInputs,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  layerParameter.name(),</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  numInputsActual,</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keywordtype">int</span> numOutputsActual = layerParameter.top_size();</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keywordflow">if</span> (numOutputs != armnn::numeric_cast<unsigned int>(numOutputsActual))</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  {</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  fmt::format(<span class="stringliteral">"Invalid number of outputs requested {} for layer {} "</span></div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="stringliteral">"while only {} present. {}"</span>,</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  numOutputs,</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  layerParameter.name(),</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  numOutputsActual,</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> }</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> </div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ParamType, <span class="keyword">typename</span> ExtractOptional, <span class="keyword">typename</span> ExtractFallback, <span class="keyword">typename</span> ValueType></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> ValueType GetOptionalWithFallback(<span class="keyword">const</span> ParamType& param,</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  ExtractOptional extractOptional,</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  ExtractFallback extractFallback,</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  ValueType defaultValue)</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> {</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keyword">auto</span> optValue = extractOptional(param, defaultValue);</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keywordflow">if</span> (optValue.first)</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">return</span> optValue.second;</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="keyword">auto</span> fallbackValue = extractFallback(param, defaultValue);</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keywordflow">return</span> fallbackValue.second;</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> }</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> </div><div class="line"><a name="l00225"></a><span class="lineno"><a class="line" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5"> 225</a></span> <span class="preprocessor">#define GET_OPTIONAL_WITH_VECTOR_FALLBACK(PARAM, \</span></div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> <span class="preprocessor"> PARAM_TYPE, \</span></div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> <span class="preprocessor"> OPTIONAL_VALUE, \</span></div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span> <span class="preprocessor"> FALLBACK_VECTOR, \</span></div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> <span class="preprocessor"> VALUE_TYPE, \</span></div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> <span class="preprocessor"> DEFAULT_VALUE) \</span></div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> <span class="preprocessor"> GetOptionalWithFallback( \</span></div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span> <span class="preprocessor"> PARAM, \</span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> <span class="preprocessor"> [](const PARAM_TYPE & param, VALUE_TYPE defaultValue) \</span></div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> <span class="preprocessor"> if (param.has_##OPTIONAL_VALUE ()) \</span></div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> <span class="preprocessor"> return std::make_pair(true, param.OPTIONAL_VALUE ()); \</span></div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span> <span class="preprocessor"> } \</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> <span class="preprocessor"> else \</span></div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> <span class="preprocessor"> return std::make_pair(false, defaultValue); \</span></div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span> <span class="preprocessor"> } \</span></div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> <span class="preprocessor"> }, \</span></div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span> <span class="preprocessor"> [](const PARAM_TYPE & param, VALUE_TYPE defaultValue) \</span></div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> <span class="preprocessor"> if (param.FALLBACK_VECTOR##_size() > 0) \</span></div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> <span class="preprocessor"> return std::make_pair(true, (param.FALLBACK_VECTOR ()).Get(0)); \</span></div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span> <span class="preprocessor"> } \</span></div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> <span class="preprocessor"> else \</span></div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> <span class="preprocessor"> return std::make_pair(false, defaultValue); \</span></div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span> <span class="preprocessor"> } \</span></div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span> <span class="preprocessor"> }, \</span></div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> <span class="preprocessor"> DEFAULT_VALUE)</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"><a class="line" href="_caffe_parser_8cpp.xhtml#a69f4a692d0095f6b19b0cd99cd75e465"> 257</a></span> <span class="preprocessor">#define GET_OPTIONAL_WITH_FALLBACK(PARAM, \</span></div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> <span class="preprocessor"> PARAM_TYPE, \</span></div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> <span class="preprocessor"> OPTIONAL_VALUE, \</span></div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> <span class="preprocessor"> FALLBACK_VALUE, \</span></div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> <span class="preprocessor"> VALUE_TYPE, \</span></div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> <span class="preprocessor"> DEFAULT_VALUE) \</span></div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> <span class="preprocessor"> GetOptionalWithFallback( \</span></div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span> <span class="preprocessor"> PARAM, \</span></div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> <span class="preprocessor"> [](const PARAM_TYPE & param, VALUE_TYPE defaultValue) \</span></div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> <span class="preprocessor"> if (param.has_##OPTIONAL_VALUE ()) \</span></div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> <span class="preprocessor"> return std::make_pair(true, param.OPTIONAL_VALUE ()); \</span></div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> <span class="preprocessor"> } \</span></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span> <span class="preprocessor"> else \</span></div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span> <span class="preprocessor"> return std::make_pair(false, defaultValue); \</span></div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> <span class="preprocessor"> } \</span></div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span> <span class="preprocessor"> }, \</span></div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span> <span class="preprocessor"> [](const PARAM_TYPE & param, VALUE_TYPE defaultValue) \</span></div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> <span class="preprocessor"> if (param.has_##FALLBACK_VALUE ()) \</span></div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> <span class="preprocessor"> return std::make_pair(true, param.FALLBACK_VALUE ()); \</span></div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> <span class="preprocessor"> } \</span></div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> <span class="preprocessor"> else \</span></div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span> <span class="preprocessor"> return std::make_pair(false, defaultValue); \</span></div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> <span class="preprocessor"> } \</span></div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span> <span class="preprocessor"> }, \</span></div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> <span class="preprocessor"> DEFAULT_VALUE)</span></div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span> </div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> } <span class="comment">// namespace <anonymous></span></div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> </div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span> <span class="keyword">const</span> std::map<std::string, ICaffeParser::CaffeParserImpl::OperationParsingFunction></div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  ICaffeParser::CaffeParserImpl::ms_CaffeLayerNameToParsingFunctions = {</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  { <span class="stringliteral">"Input"</span>, &CaffeParserImpl::ParseInputLayer },</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  { <span class="stringliteral">"Convolution"</span>, &CaffeParserImpl::ParseConvLayer },</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  { <span class="stringliteral">"Deconvolution"</span>,&CaffeParserImpl::ParseDeconvLayer },</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  { <span class="stringliteral">"Pooling"</span>, &CaffeParserImpl::ParsePoolingLayer },</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  { <span class="stringliteral">"ReLU"</span>, &CaffeParserImpl::ParseReluLayer },</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  { <span class="stringliteral">"LRN"</span>, &CaffeParserImpl::ParseLRNLayer },</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  { <span class="stringliteral">"InnerProduct"</span>, &CaffeParserImpl::ParseInnerProductLayer },</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  { <span class="stringliteral">"Softmax"</span>, &CaffeParserImpl::ParseSoftmaxLayer },</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  { <span class="stringliteral">"Eltwise"</span>, &CaffeParserImpl::ParseEltwiseLayer },</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  { <span class="stringliteral">"Concat"</span>, &CaffeParserImpl::ParseConcatLayer },</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  { <span class="stringliteral">"BatchNorm"</span>, &CaffeParserImpl::ParseBatchNormLayer },</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  { <span class="stringliteral">"Scale"</span>, &CaffeParserImpl::ParseScaleLayer },</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  { <span class="stringliteral">"Split"</span>, &CaffeParserImpl::ParseSplitLayer },</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  { <span class="stringliteral">"Dropout"</span>, &CaffeParserImpl::ParseDropoutLayer},</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  { <span class="stringliteral">"ArgMax"</span>, &CaffeParserImpl::ParseArgmaxLayer},</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> };</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span> </div><div class="line"><a name="l00310"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a4ddfa6cb51f928114a8151b8f455f115"> 310</a></span> ICaffeParser::CaffeParserImpl::CaffeParserImpl()</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  : m_Network(nullptr, nullptr)</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> </div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span> }</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span> </div><div class="line"><a name="l00316"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_caffe_parser.xhtml#a82fc903eb5648250a6d82371a94772a3"> 316</a></span> <a class="code" href="classarmnn_caffe_parser_1_1_caffe_parser.xhtml#a82fc903eb5648250a6d82371a94772a3">CaffeParser::CaffeParser</a>()</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span> : <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a4ddfa6cb51f928114a8151b8f455f115">CaffeParserImpl</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> </div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> }</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span> </div><div class="line"><a name="l00322"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a8b053a6c449d0814cc831c916c126668"> 322</a></span> <a class="code" href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">BindingPointInfo</a> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a8b053a6c449d0814cc831c916c126668">ICaffeParser::CaffeParserImpl::GetNetworkInputBindingInfo</a>(<span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> <span class="keyword"></span>{</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#afb7e4da478bab76261963479baad5788">GetBindingInfo</a>(name, <span class="stringliteral">"input"</span>, <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac62e2558c14e01605f2b4e1e21cdd1e8">m_NetworkInputsBindingInfo</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> </div><div class="line"><a name="l00327"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a4b1fdcb1985af12dd1848a9ffa5d3271"> 327</a></span> <a class="code" href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">BindingPointInfo</a> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a4b1fdcb1985af12dd1848a9ffa5d3271">ICaffeParser::CaffeParserImpl::GetNetworkOutputBindingInfo</a>(<span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span> <span class="keyword"></span>{</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#afb7e4da478bab76261963479baad5788">GetBindingInfo</a>(name, <span class="stringliteral">"output"</span>, <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a62d6d6cba9ed0d3ad63fffb40aec86b5">m_NetworkOutputsBindingInfo</a>);</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> }</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span> </div><div class="line"><a name="l00332"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#afb7e4da478bab76261963479baad5788"> 332</a></span> std::pair<armnn::LayerBindingId, armnn::TensorInfo> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#afb7e4da478bab76261963479baad5788">ICaffeParser::CaffeParserImpl::GetBindingInfo</a>(</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* bindingPointDesc,</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <span class="keyword">const</span> std::unordered_map<std::string, BindingPointInfo>& nameToBindingInfo)</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span> {</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="keyword">auto</span> it = nameToBindingInfo.find(layerName);</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keywordflow">if</span> (it == nameToBindingInfo.end())</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  {</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  fmt::format(<span class="stringliteral">"Unknown binding {} for layer '{}'. {}"</span>,</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  bindingPointDesc,</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  layerName,</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  }</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="keywordflow">return</span> it->second;</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> </div><div class="line"><a name="l00349"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856"> 349</a></span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">ICaffeParser::CaffeParserImpl::BlobShapeToTensorInfo</a>(<span class="keyword">const</span> caffe::BlobShape& blobShape)<span class="keyword"> const</span></div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span> <span class="keyword"></span>{</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  std::vector<unsigned int> shape;</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < blobShape.dim_size(); ++j)</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  {</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  shape.push_back(static_cast<unsigned int>(blobShape.dim(j)));</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  }</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">return</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(armnn::numeric_cast<unsigned int>(shape.size()), shape.data(), DataType::Float32);</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> </div><div class="line"><a name="l00360"></a><span class="lineno"><a class="line" href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed"> 360</a></span> BlobShape <a class="code" href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed">TensorDescToBlobShape</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& desc)</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span> {</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  BlobShape ret;</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < desc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++i)</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>  ret.add_dim(i);</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  ret.set_dim(armnn::numeric_cast<int>(i), desc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i]);</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  }</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span> </div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <span class="keywordflow">return</span> ret;</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> </div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span> <span class="comment">// Note: can move to CaffeParser when/if we optimise the text/string format</span></div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span> <span class="comment">// to load on a layer by layer basis</span></div><div class="line"><a name="l00374"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2f8fbd66c1a39a06d61fcb6536387d64"> 374</a></span> vector<const LayerParameter*> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2f8fbd66c1a39a06d61fcb6536387d64">ICaffeParser::CaffeParserImpl::GetInputs</a>(<span class="keyword">const</span> LayerParameter& layerParam)</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span> {</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  std::vector<const caffe::LayerParameter*> ret;</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  ret.reserve(armnn::numeric_cast<size_t>(layerParam.bottom_size()));</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < layerParam.bottom_size(); ++j)</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  {</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  std::string inputName = layerParam.bottom(j);</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="keyword">auto</span> inputIt = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a1424f1bfbfc81d317b51053bbb315ef1">m_CaffeLayersByTopName</a>.find(inputName);</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <span class="keywordflow">if</span> (inputIt == <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a1424f1bfbfc81d317b51053bbb315ef1">m_CaffeLayersByTopName</a>.end())</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  fmt::format(<span class="stringliteral">"Can't find Caffe layer with top called '{}', "</span></div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <span class="stringliteral">"which is listed as an input of '{}'. {}"</span>,</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  inputName,</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  layerParam.name(),</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  }</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  ret.push_back(inputIt->second);</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  }</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span> </div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span> }</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span> </div><div class="line"><a name="l00397"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a3a2636dd8414f2bb62c5fa097bdc9791"> 397</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a3a2636dd8414f2bb62c5fa097bdc9791">ICaffeParser::CaffeParserImpl::ParseInputLayer</a>(<span class="keyword">const</span> LayerParameter& layerParam)</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span> {</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layerParam.type() == <span class="stringliteral">"Input"</span>);</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  ValidateNumInputsOutputs(layerParam, 0, 1);</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>  <span class="keyword">const</span> InputParameter& param = layerParam.input_param();</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span> </div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> inputId = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a>>(</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac62e2558c14e01605f2b4e1e21cdd1e8">m_NetworkInputsBindingInfo</a>.size());</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> inputLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddInputLayer(inputId, layerParam.name().c_str());</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span> </div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="comment">// Decides the tensor info for this input. This can be specified in the Caffe network but can also</span></div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="comment">// be overriden by user input (m_inputShapes).</span></div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span> </div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <span class="keyword">const</span> BlobShape* originalShape = param.shape_size() > 0 && param.shape(0).dim_size() > 0 ?</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  &param.shape(0) : <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="keywordflow">if</span> (originalShape)</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  {</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  inputTensorInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">BlobShapeToTensorInfo</a>(*originalShape);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  }</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> </div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keyword">auto</span> overrideIt = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a76ea67f3f7d1d5835c5a92b65dc0854c">m_InputShapes</a>.find(layerParam.name());</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keywordflow">if</span> (overrideIt != <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a76ea67f3f7d1d5835c5a92b65dc0854c">m_InputShapes</a>.end())</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  {</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& overrideShape = overrideIt->second;</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="keywordflow">if</span> (originalShape &&</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  ( originalShape->dim(1) != overrideShape[1]</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  || originalShape->dim(2) != overrideShape[2]</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  || originalShape->dim(3) != overrideShape[3]))</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  {</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  fmt::format(<span class="stringliteral">"Parsed input shape for '{}' is incompatible with the override provided. {}"</span>,</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  layerParam.name(),</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  }</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(overrideShape);</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  }</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (!originalShape)</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  {</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  fmt::format(<span class="stringliteral">"No input descriptor given for '{}' and no input shape found in caffe model. {}"</span>,</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  layerParam.name(),</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  }</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2a1112c66d08e3760ecccf39c7854a90">TrackInputBinding</a>(inputLayer, inputId, inputTensorInfo);</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), inputLayer->GetOutputSlot(0));</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span> }</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span> </div><div class="line"><a name="l00447"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9fea304829fe514d664de515ca5c3918"> 447</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9fea304829fe514d664de515ca5c3918">ICaffeParser::CaffeParserImpl::AddConvLayerWithSplits</a>(<span class="keyword">const</span> caffe::LayerParameter& layerParam,</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a>& desc,</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelW,</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelH)</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span> {</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layerParam.type() == <span class="stringliteral">"Convolution"</span>);</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span> </div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  ConvolutionParameter convParam = layerParam.convolution_param();</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  BlobShape inputShape = <a class="code" href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed">TensorDescToBlobShape</a>(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>());</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numGroups = convParam.has_group() ? convParam.group() : 1;</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span> </div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <span class="comment">// asusme these were already verified by the caller ParseConvLayer() function</span></div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(numGroups < inputShape.dim(1));</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(numGroups > 1);</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span> </div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="comment">// Handle grouping</span></div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>& inputConnection = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0));</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span> </div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  vector<string> convLayerNames(numGroups);</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  vector<armnn::IConnectableLayer*> convLayers(numGroups);</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  convLayerNames[0] = layerParam.name();</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span> </div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <span class="comment">// This convolution is to be applied to chunks of the input data so add a splitter layer</span></div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span> </div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <span class="comment">// Redirect the convolution input to the splitter</span></div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitterDimSizes[4] = {<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(inputShape.dim(0)),</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  static_cast<unsigned int>(inputShape.dim(1)),</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  static_cast<unsigned int>(inputShape.dim(2)),</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  static_cast<unsigned int>(inputShape.dim(3))};</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span> </div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <span class="comment">// Split dimension 1 of the splitter output shape and conv input shapes</span></div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="comment">// according to the number of groups</span></div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> </div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  splitterDimSizes[1] /= numGroups;</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  inputShape.set_dim(1, splitterDimSizes[1]);</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>  <span class="comment">// This is used to describe how the input is to be split</span></div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">ViewsDescriptor</a> splitterDesc(numGroups);</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>  <span class="comment">// Create an output node for each group, giving each a unique name</span></div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g < numGroups; ++g)</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>  <span class="comment">// Work out the names of the splitter layers child convolutions</span></div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  stringstream ss;</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  ss << layerParam.name() << <span class="stringliteral">"_"</span> << g;</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  convLayerNames[g] = ss.str();</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span> </div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  splitterDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(g, 1, splitterDimSizes[1] * g);</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">// Set the size of the views.</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> dimIdx=0; dimIdx < 4; dimIdx++)</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>  splitterDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(g, dimIdx, splitterDimSizes[dimIdx]);</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>  <span class="keyword">const</span> std::string splitterLayerName = std::string(<span class="stringliteral">"splitter_"</span>) + layerParam.bottom(0);</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* splitterLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddSplitterLayer(splitterDesc, splitterLayerName.c_str());</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span> </div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  inputConnection.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(); i++)</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  {</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(i).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">BlobShapeToTensorInfo</a>(inputShape));</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  }</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span> </div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numFilters = convParam.num_output();</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span> </div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <span class="comment">// Populates convolution output tensor descriptor dimensions.</span></div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  BlobShape outputShape;</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  outputShape.add_dim(0);</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  outputShape.set_dim(0, inputShape.dim(0));</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  outputShape.add_dim(1);</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  <span class="comment">// Ensures that dimension 1 of the convolution output is split according to the number of groups.</span></div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  outputShape.set_dim(1, numFilters / numGroups);</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  outputShape.add_dim(2);</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  outputShape.set_dim(</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  2, (static_cast<int>(</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  static_cast<float>(inputShape.dim(2) + 2 * desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> - (desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> * (kernelH - 1) + 1)) /</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  static_cast<float>(desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>)) + 1));</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  outputShape.add_dim(3);</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  outputShape.set_dim(</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  3, (static_cast<int>(</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  static_cast<float>(inputShape.dim(3) + 2 * desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> - (desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> * (kernelW - 1) + 1)) /</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  static_cast<float>(desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>)) + 1));</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span> </div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="comment">// Load the weight data for ALL groups</span></div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  vector<float> weightData(armnn::numeric_cast<size_t>(numGroups *</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  inputShape.dim(1) * <span class="comment">// number of input channels</span></div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  outputShape.dim(1) * <span class="comment">// number of output channels</span></div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  kernelH *</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  kernelW));</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  GetDataFromBlob(layerParam, weightData, 0);</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span> </div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightDimSizes[4] = {</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(outputShape.dim(1)),</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  static_cast<unsigned int>(inputShape.dim(1)),</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  kernelH,</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  kernelW};</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span> </div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo;</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  vector<float> biasData;</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span> </div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <span class="keywordflow">if</span> (desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  {</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  biasData.resize(armnn::numeric_cast<size_t>(numGroups * outputShape.dim(1)), 1.f);</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  GetDataFromBlob(layerParam, biasData, 1);</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span> </div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasDimSizes[1] = {<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(outputShape.dim(1))};</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  biasInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, biasDimSizes, DataType::Float32);</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  }</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span> </div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numWeightsPerGroup = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(weightData.size()) / numGroups;</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numBiasesPerGroup = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(biasData.size()) / numGroups;</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span> </div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g < numGroups; ++g)</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  {</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <span class="comment">// Sets the slot index, group 0 should be connected to the 0th output of the splitter</span></div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <span class="comment">// group 1 should be connected to the 1st output of the splitter.</span></div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span> </div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <span class="comment">// Pulls out the weights for this group from that loaded from the model file earlier.</span></div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(4, weightDimSizes, DataType::Float32),</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  weightData.data() + numWeightsPerGroup * g);</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span> </div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* convLayer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalBiases;</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <span class="keywordflow">if</span> (desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  {</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <span class="comment">// Pulls out the biases for this group from that loaded from the model file earlier.</span></div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biases(biasInfo, biasData.data() + numBiasesPerGroup * g);</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  optionalBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(biases);</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  }</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  convLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddConvolution2dLayer(desc,</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  weights,</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  optionalBiases,</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  convLayerNames[g].c_str());</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  convLayers[g] = convLayer;</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span> </div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <span class="comment">// If we have more than one group then the input to the nth convolution the splitter layer's nth output,</span></div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  <span class="comment">// otherwise it's the regular input to this layer.</span></div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>& splitterInputConnection =</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  splitterLayer ? splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(g) : inputConnection;</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  splitterInputConnection.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer->GetInputSlot(0));</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  convLayer->GetOutputSlot(0).SetTensorInfo(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">BlobShapeToTensorInfo</a>(outputShape));</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  }</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span> </div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <span class="comment">// If the convolution was performed in chunks, add a layer to concatenate the results</span></div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span> </div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <span class="comment">// The merge input shape matches that of the convolution output</span></div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatDimSizes[4] = {<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(outputShape.dim(0)),</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  static_cast<unsigned int>(outputShape.dim(1)),</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  static_cast<unsigned int>(outputShape.dim(2)),</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  static_cast<unsigned int>(outputShape.dim(3))};</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span> </div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <span class="comment">// This is used to describe how the input is to be concatenated</span></div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> concatDesc(numGroups);</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span> </div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  <span class="comment">// Now create an input node for each group, using the name from</span></div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <span class="comment">// the output of the corresponding convolution</span></div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g < numGroups; ++g)</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  {</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  concatDesc.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(g, 1, concatDimSizes[1] * g);</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  }</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span> </div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  <span class="comment">// Make sure the output from the concat is the correct size to hold the data for all groups</span></div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  concatDimSizes[1] *= numGroups;</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  outputShape.set_dim(1, concatDimSizes[1]);</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span> </div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <span class="comment">// Finally add the concat layer</span></div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* concatLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddConcatLayer(concatDesc, layerParam.name().c_str());</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span> </div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <span class="keywordflow">if</span> (!concatLayer)</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  {</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  fmt::format(<span class="stringliteral">"Failed to create final concat layer for Split+Convolution+Concat. "</span></div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  <span class="stringliteral">"Layer={} #groups={} #filters={} {}"</span>,</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  layerParam.name(),</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  numGroups,</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  numFilters,</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  }</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span> </div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g < numGroups; ++g)</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  {</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  convLayers[g]->GetOutputSlot(0).Connect(concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(g));</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  }</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, concatDimSizes, DataType::Float32));</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span> }</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span> </div><div class="line"><a name="l00637"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77f532a6a82afeb6e79957726a9517a5"> 637</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77f532a6a82afeb6e79957726a9517a5">ICaffeParser::CaffeParserImpl::AddDeconvLayerWithSplits</a>(<span class="keyword">const</span> caffe::LayerParameter& layerParam,</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">armnn::TransposeConvolution2dDescriptor</a>& desc,</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelW,</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelH)</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span> {</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layerParam.type() == <span class="stringliteral">"Deconvolution"</span>);</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span> </div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  ConvolutionParameter convParam = layerParam.convolution_param();</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  BlobShape inputShape = <a class="code" href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed">TensorDescToBlobShape</a>(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>());</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numGroups = convParam.has_group() ? convParam.group() : 1;</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span> </div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  <span class="comment">// asusme these were already verified by the caller ParseDeconvLayer() function</span></div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(numGroups <= inputShape.dim(1));</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(numGroups > 1);</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span> </div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <span class="comment">// Handle grouping</span></div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>& inputConnection = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0));</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span> </div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  vector<string> convLayerNames(numGroups);</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  vector<armnn::IConnectableLayer*> convLayers(numGroups);</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  convLayerNames[0] = layerParam.name();</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span> </div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <span class="comment">// This deconvolution is to be applied to chunks of the input data so add a splitter layer</span></div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span> </div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  <span class="comment">// Redirect the deconvolution input to the splitter</span></div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitterDimSizes[4] = {<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(inputShape.dim(0)),</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  static_cast<unsigned int>(inputShape.dim(1)),</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  static_cast<unsigned int>(inputShape.dim(2)),</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  static_cast<unsigned int>(inputShape.dim(3))};</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span> </div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  <span class="comment">// Split dimension 1 of the splitter output shape and deconv input shapes</span></div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  <span class="comment">// according to the number of groups</span></div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span> </div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  splitterDimSizes[1] /= numGroups;</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  inputShape.set_dim(1, splitterDimSizes[1]);</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span> </div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <span class="comment">// This is used to describe how the input is to be split</span></div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">ViewsDescriptor</a> splitterDesc(numGroups);</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span> </div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  <span class="comment">// Create an output node for each group, giving each a unique name</span></div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g < numGroups; ++g)</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  {</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  <span class="comment">// Work out the names of the splitter layers child deconvolutions</span></div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  stringstream ss;</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  ss << layerParam.name() << <span class="stringliteral">"_"</span> << g;</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  convLayerNames[g] = ss.str();</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span> </div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  splitterDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(g, 1, splitterDimSizes[1] * g);</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span> </div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  <span class="comment">// Set the size of the views.</span></div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx=0; dimIdx < 4; dimIdx++)</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  {</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  splitterDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(g, dimIdx, splitterDimSizes[dimIdx]);</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  }</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  }</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span> </div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <span class="keyword">const</span> std::string splitterLayerName = std::string(<span class="stringliteral">"splitter_"</span>) + layerParam.bottom(0);</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* splitterLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddSplitterLayer(splitterDesc, splitterLayerName.c_str());</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span> </div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  inputConnection.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(); i++)</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  {</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(i).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">BlobShapeToTensorInfo</a>(inputShape));</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  }</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span> </div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numFilters = convParam.num_output();</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span> </div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  <span class="comment">// Populates deconvolution output tensor descriptor dimensions.</span></div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  BlobShape outputShape;</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  outputShape.add_dim(0);</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  outputShape.set_dim(0, inputShape.dim(0));</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  outputShape.add_dim(1);</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  <span class="comment">// Ensures that dimension 1 of the deconvolution output is split according to the number of groups.</span></div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  outputShape.set_dim(1, numFilters / numGroups);</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  outputShape.add_dim(2);</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  outputShape.set_dim(</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  2, (static_cast<int>(</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> * (inputShape.dim(2) - 1) - 2 * desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> + kernelH)));</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  outputShape.add_dim(3);</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  outputShape.set_dim(</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  3, (static_cast<int>(</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> * (inputShape.dim(3) - 1) - 2 * desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> + kernelW)));</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span> </div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  <span class="comment">// Load the weight data for ALL groups</span></div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  vector<float> weightData(armnn::numeric_cast<size_t>(numGroups *</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  inputShape.dim(1) * <span class="comment">// number of input channels</span></div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  outputShape.dim(1) * <span class="comment">// number of output channels</span></div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  kernelH *</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  kernelW));</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  GetDataFromBlob(layerParam, weightData, 0);</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span> </div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightDimSizes[4] = {</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(outputShape.dim(1)),</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  static_cast<unsigned int>(inputShape.dim(1)),</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  kernelH,</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  kernelW};</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span> </div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo;</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  vector<float> biasData;</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span> </div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  <span class="keywordflow">if</span> (desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  {</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  biasData.resize(armnn::numeric_cast<size_t>(numGroups * outputShape.dim(1)), 1.f);</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  GetDataFromBlob(layerParam, biasData, 1);</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span> </div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasDimSizes[1] = {<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(outputShape.dim(1))};</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  biasInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, biasDimSizes, DataType::Float32);</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  }</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span> </div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numWeightsPerGroup = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(weightData.size()) / numGroups;</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numBiasesPerGroup = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(biasData.size()) / numGroups;</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span> </div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g < numGroups; ++g)</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  {</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  <span class="comment">// Sets the slot index, group 0 should be connected to the 0th output of the splitter</span></div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <span class="comment">// group 1 should be connected to the 1st output of the splitter.</span></div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span> </div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  <span class="comment">// Pulls out the weights for this group from that loaded from the model file earlier.</span></div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(4, weightDimSizes, DataType::Float32),</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  weightData.data() + numWeightsPerGroup * g);</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span> </div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* deconvLayer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalBiases;</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  <span class="keywordflow">if</span> (desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  {</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  <span class="comment">// Pulls out the biases for this group from that loaded from the model file earlier.</span></div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biases(biasInfo, biasData.data() + numBiasesPerGroup * g);</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  optionalBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(biases);</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  }</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  deconvLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddTransposeConvolution2dLayer(desc,</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  weights,</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  optionalBiases,</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  convLayerNames[g].c_str());</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  convLayers[g] = deconvLayer;</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span> </div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  <span class="comment">// If we have more than one group then the input to the nth deconvolution the splitter layer's nth output,</span></div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  <span class="comment">// otherwise it's the regular input to this layer.</span></div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>& splitterInputConnection =</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  splitterLayer ? splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(g) : inputConnection;</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  splitterInputConnection.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(deconvLayer->GetInputSlot(0));</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  deconvLayer->GetOutputSlot(0).SetTensorInfo(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">BlobShapeToTensorInfo</a>(outputShape));</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  }</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span> </div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  <span class="comment">// If the deconvolution was performed in chunks, add a layer to concatenate the results</span></div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span> </div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  <span class="comment">// The merge input shape matches that of the deconvolution output</span></div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatDimSizes[4] = {<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(outputShape.dim(0)),</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  static_cast<unsigned int>(outputShape.dim(1)),</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  static_cast<unsigned int>(outputShape.dim(2)),</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  static_cast<unsigned int>(outputShape.dim(3))};</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span> </div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  <span class="comment">// This is used to describe how the input is to be concatenated</span></div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> concatDesc(numGroups);</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span> </div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  <span class="comment">// Now create an input node for each group, using the name from</span></div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  <span class="comment">// the output of the corresponding deconvolution</span></div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g < numGroups; ++g)</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  {</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  concatDesc.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(g, 1, concatDimSizes[1] * g);</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  }</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span> </div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  <span class="comment">// Make sure the output from the concat is the correct size to hold the data for all groups</span></div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  concatDimSizes[1] *= numGroups;</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  outputShape.set_dim(1, concatDimSizes[1]);</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span> </div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  <span class="comment">// Finally add the concat layer</span></div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* concatLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddConcatLayer(concatDesc, layerParam.name().c_str());</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span> </div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  <span class="keywordflow">if</span> (!concatLayer)</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  {</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  fmt::format(<span class="stringliteral">"Failed to create final concat layer for Split+Deconvolution+Concat. "</span></div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  <span class="stringliteral">"Layer={} #groups={} #filters={} {}"</span>,</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  layerParam.name(),</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  numGroups,</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  numFilters,</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  }</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span> </div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g < numGroups; ++g)</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  {</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  convLayers[g]->GetOutputSlot(0).Connect(concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(g));</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  }</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, concatDimSizes, DataType::Float32));</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span> }</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span> </div><div class="line"><a name="l00825"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#adcb87456482d5df17ef09eca1a808091"> 825</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#adcb87456482d5df17ef09eca1a808091">ICaffeParser::CaffeParserImpl::AddConvLayerWithDepthwiseConv</a>(<span class="keyword">const</span> caffe::LayerParameter& layerParam,</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a>& convDesc,</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelW,</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelH)</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span> {</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layerParam.type() == <span class="stringliteral">"Convolution"</span>);</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span> </div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  ConvolutionParameter convParam = layerParam.convolution_param();</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  BlobShape inputShape = <a class="code" href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed">TensorDescToBlobShape</a>(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>());</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span> </div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> desc;</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>;</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  desc.m_PadRight = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>;</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  desc.m_PadTop = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>;</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  desc.m_PadBottom = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>;</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  desc.m_StrideX = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>;</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  desc.m_StrideY = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>;</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  desc.m_DilationX = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a>;</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  desc.m_DilationY = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a>;</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  desc.m_BiasEnabled = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>;</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span> </div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numFilters = convParam.num_output();</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span> </div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  BlobShape outputShape;</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  outputShape.add_dim(0);</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  outputShape.set_dim(0, inputShape.dim(0));</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  outputShape.add_dim(1);</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  outputShape.set_dim(1, numFilters);</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  outputShape.add_dim(2);</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  outputShape.set_dim(</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  2, (static_cast<int>(</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  static_cast<float>(inputShape.dim(2) + 2 * desc.m_PadBottom - (desc.m_DilationX * (kernelH - 1) + 1)) /</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  static_cast<float>(desc.m_StrideY)) + 1));</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  outputShape.add_dim(3);</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  outputShape.set_dim(</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  3, (static_cast<int>(</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  static_cast<float>(inputShape.dim(3) + 2 * desc.m_PadRight - (desc.m_DilationY * (kernelW - 1) + 1)) /</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  static_cast<float>(desc.m_StrideX)) + 1));</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span> </div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  <span class="comment">// Load the weight data</span></div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  <span class="keywordtype">size_t</span> allWeightsSize = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">size_t</span>>(inputShape.dim(1) * kernelH * kernelW);</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  vector<float> weightData(allWeightsSize);</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span> </div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  GetDataFromBlob(layerParam, weightData, 0);</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span> </div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  <span class="comment">// depth multiplier will be 1 for the depthwise convolution</span></div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightDimSizes[4] = {</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(1), <span class="comment">// depth multiplier</span></div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  static_cast<unsigned int>(inputShape.dim(1)), <span class="comment">// #channels</span></div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  kernelH,</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  kernelW};</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span> </div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* returnLayer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(4, weightDimSizes, DataType::Float32), weightData.data());</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalBiases;</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  vector<float> biasData;</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  <span class="keywordflow">if</span> (desc.m_BiasEnabled)</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  {</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo;</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span> </div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  biasData.resize(armnn::numeric_cast<size_t>(outputShape.dim(1)), 1.f);</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  GetDataFromBlob(layerParam, biasData, 1);</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span> </div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasDimSizes[1] = {<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(outputShape.dim(1))};</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>  biasInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, biasDimSizes, DataType::Float32);</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span> </div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biases(biasInfo, biasData.data());</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  optionalBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(biases);</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  }</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  returnLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddDepthwiseConvolution2dLayer(desc,</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  weights,</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  optionalBiases,</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  layerParam.name().c_str());</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span> </div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  <span class="keywordflow">if</span> (!returnLayer)</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  {</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  fmt::format(<span class="stringliteral">"Failed to create depthwise convolution layer. "</span></div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  <span class="stringliteral">"Layer={} #filters={} {}"</span>,</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  layerParam.name(),</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  numFilters,</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  }</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>& inputConnection = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0));</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  inputConnection.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(returnLayer->GetInputSlot(0));</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  returnLayer->GetOutputSlot(0).SetTensorInfo(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">BlobShapeToTensorInfo</a>(outputShape));</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), returnLayer->GetOutputSlot(0));</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span> }</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span> </div><div class="line"><a name="l00915"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5cddc80538d5de7d36192e0fd2d09c63"> 915</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5cddc80538d5de7d36192e0fd2d09c63">ICaffeParser::CaffeParserImpl::ParseConvLayer</a>(<span class="keyword">const</span> LayerParameter& layerParam)</div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span> {</div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  <span class="comment">// Ignored Caffe Parameters</span></div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  <span class="comment">// * Weight Filler</span></div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  <span class="comment">// * Bias Filler</span></div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  <span class="comment">// * Engine</span></div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  <span class="comment">// * Force nd_im2col</span></div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  <span class="comment">// * Axis</span></div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span> </div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  <span class="comment">// Not Available ArmNN Interface Parameters</span></div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  <span class="comment">// * Rounding policy;</span></div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span> </div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layerParam.type() == <span class="stringliteral">"Convolution"</span>);</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span> </div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  ConvolutionParameter convParam = layerParam.convolution_param();</div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  BlobShape inputShape = <a class="code" href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed">TensorDescToBlobShape</a>(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>());</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numGroups = convParam.has_group() ? convParam.group() : 1;</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numFilters = convParam.num_output();</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span> </div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  <span class="keyword">const</span> <span class="keyword">auto</span> notFound = std::numeric_limits<unsigned int>::max();</div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span> </div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelH = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  kernel_h, kernel_size, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, notFound);</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelW = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  kernel_w, kernel_size, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, notFound);</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span> </div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideH = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  stride_h, stride, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 1u);</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideW = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  stride_w, stride, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 1u);</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span> </div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padH = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  pad_h, pad, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 0u);</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padW = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  pad_w, pad, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 0u);</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span> </div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dilationH = convParam.dilation_size() > 0 ? convParam.dilation(0) : 1;</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dilationW = convParam.dilation_size() > 1 ? convParam.dilation(1) :</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  convParam.dilation_size() > 0 ? convParam.dilation(0) : 1;</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span> </div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> convolution2dDescriptor;</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = padW;</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = padW;</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = padH;</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = padH;</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strideW;</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strideH;</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = dilationW;</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = dilationH;</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = convParam.has_bias_term() ? convParam.bias_term() : <span class="keyword">true</span>;</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span> </div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  <span class="keywordflow">if</span> (numGroups > numFilters)</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  {</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  fmt::format(<span class="stringliteral">"Error parsing Convolution: {}. "</span></div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  <span class="stringliteral">"The 'group'={} parameter cannot be larger than the "</span></div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  <span class="stringliteral">"number of filters supplied ='{}'. {}"</span>,</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  layerParam.name(),</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  numGroups,</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  numFilters,</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  }</div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span> </div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  <span class="keywordflow">if</span> (inputShape.dim_size() != 4)</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  {</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  fmt::format(<span class="stringliteral">"Convolution input shape is expected to have 4 dimensions. "</span></div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  <span class="stringliteral">"{}'s input has only {}. {}"</span>,</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  layerParam.name(),</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  inputShape.dim_size(),</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  }</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span> </div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  <span class="keywordflow">if</span> (numGroups > 1)</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  {</div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  <span class="keywordflow">if</span> (numGroups > inputShape.dim(1))</div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  {</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span>  fmt::format(<span class="stringliteral">"Error parsing Convolution: {}. "</span></div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span>  <span class="stringliteral">"The 'group'={} parameter cannot be larger than the "</span></div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span>  <span class="stringliteral">"channel of the input shape={} (in NCHW format). {}"</span>,</div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  layerParam.name(),</div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  numGroups,</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  inputShape.dim(1),</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  }</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (numGroups == inputShape.dim(1))</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>  {</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>  <span class="comment">// we use a depthwise convolution here, because the number of groups equals to the</span></div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  <span class="comment">// input channels</span></div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#adcb87456482d5df17ef09eca1a808091">AddConvLayerWithDepthwiseConv</a>(layerParam, convolution2dDescriptor, kernelW, kernelH);</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  }</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  {</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  <span class="comment">// we split the input by channels into channels/groups separate convolutions</span></div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  <span class="comment">// and concatenate the results afterwards</span></div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9fea304829fe514d664de515ca5c3918">AddConvLayerWithSplits</a>(layerParam, convolution2dDescriptor, kernelW, kernelH);</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  }</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  }</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span> </div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>  <span class="comment">// NOTE: at this point we only need to handle #group=1 case, all other cases should be</span></div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>  <span class="comment">// handled by the AddConvLayer* helpers</span></div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span> </div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>  <span class="comment">// Populate convolution output tensor descriptor dimensions</span></div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  BlobShape outputShape;</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  outputShape.add_dim(0);</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  outputShape.set_dim(0, inputShape.dim(0));</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>  outputShape.add_dim(1);</div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  outputShape.set_dim(1, numFilters);</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  outputShape.add_dim(2);</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  outputShape.set_dim(</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  2, (static_cast<int>(</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  static_cast<float>(inputShape.dim(2) + 2 * padH - (dilationH * (kernelH - 1) + 1)) /</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  static_cast<float>(strideH)) + 1));</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>  outputShape.add_dim(3);</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>  outputShape.set_dim(</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>  3, (static_cast<int>(</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  static_cast<float>(inputShape.dim(3) + 2 * padW - (dilationW * (kernelW - 1) + 1)) /</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  static_cast<float>(strideW)) + 1));</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span> </div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>  <span class="comment">// Load the weight data for ALL groups</span></div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>  vector<float> weightData(armnn::numeric_cast<size_t>(inputShape.dim(1) *</div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>  outputShape.dim(1) *</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>  kernelH *</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  kernelW));</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  GetDataFromBlob(layerParam, weightData, 0);</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span> </div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightDimSizes[4] = {</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>  <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(outputShape.dim(1)), <span class="comment">// output channels</span></div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  static_cast<unsigned int>(inputShape.dim(1)), <span class="comment">// input channels</span></div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>  kernelH,</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>  kernelW};</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span> </div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* returnLayer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span> </div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>  <span class="comment">// Pull out the weights for this group from that loaded from the model file earlier</span></div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(4, weightDimSizes, DataType::Float32), weightData.data());</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalBiases;</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>  vector<float> biasData;</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  <span class="keywordflow">if</span> (convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  {</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo;</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span> </div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>  biasData.resize(armnn::numeric_cast<size_t>(outputShape.dim(1)), 1.f);</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>  GetDataFromBlob(layerParam, biasData, 1);</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span> </div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasDimSizes[1] = {<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(outputShape.dim(1))};</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  biasInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, biasDimSizes, DataType::Float32);</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span> </div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>  <span class="comment">// Pull out the biases for this group from that loaded from the model file earlier</span></div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biases(biasInfo, biasData.data());</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>  optionalBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(biases);</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>  }</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  returnLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddConvolution2dLayer(convolution2dDescriptor,</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  weights,</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>  optionalBiases,</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  layerParam.name().c_str());</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span> </div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>& inputConnection = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0));</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>  inputConnection.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(returnLayer->GetInputSlot(0));</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>  returnLayer->GetOutputSlot(0).SetTensorInfo(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">BlobShapeToTensorInfo</a>(outputShape));</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span> </div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>  <span class="keywordflow">if</span> (!returnLayer)</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>  {</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  fmt::format(<span class="stringliteral">"Failed to create Convolution layer. "</span></div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>  <span class="stringliteral">"Layer={} #groups={} #filters={} {}"</span>,</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>  layerParam.name(),</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  numGroups,</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  numFilters,</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  }</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span> </div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), returnLayer->GetOutputSlot(0));</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span> }</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span> </div><div class="line"><a name="l01094"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac3f705f208b5ee9f540577524b2ad513"> 1094</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac3f705f208b5ee9f540577524b2ad513">ICaffeParser::CaffeParserImpl::ParseDeconvLayer</a>(<span class="keyword">const</span> LayerParameter& layerParam)</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span> {</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  <span class="comment">// Ignored Caffe Parameters</span></div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  <span class="comment">// * Weight Filler</span></div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>  <span class="comment">// * Bias Filler</span></div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  <span class="comment">// * Engine</span></div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>  <span class="comment">// * Force nd_im2col</span></div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>  <span class="comment">// * Axis</span></div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span> </div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>  <span class="comment">// Not Available ArmNN Interface Parameters</span></div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  <span class="comment">// * Rounding policy;</span></div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span> </div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layerParam.type() == <span class="stringliteral">"Deconvolution"</span>);</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>  ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span> </div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  ConvolutionParameter convParam = layerParam.convolution_param();</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  BlobShape inputShape = <a class="code" href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed">TensorDescToBlobShape</a>(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>());</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numGroups = convParam.has_group() ? convParam.group() : 1;</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numFilters = convParam.num_output();</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span> </div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>  <span class="keyword">const</span> <span class="keyword">auto</span> notFound = std::numeric_limits<unsigned int>::max();</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span> </div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelH = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>  kernel_h, kernel_size, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, notFound);</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelW = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>  kernel_w, kernel_size, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, notFound);</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span> </div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideH = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>  stride_h, stride, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 1u);</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideW = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  stride_w, stride, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 1u);</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span> </div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padH = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  pad_h, pad, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 0u);</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padW = <a class="code" href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a>(convParam, ConvolutionParameter,</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>  pad_w, pad, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 0u);</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span> </div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dilationH = convParam.dilation_size() > 0 ? convParam.dilation(0) : 1;</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dilationW = convParam.dilation_size() > 1 ? convParam.dilation(1) :</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  convParam.dilation_size() > 0 ? convParam.dilation(0) : 1;</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span> </div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  <span class="keywordflow">if</span> (dilationH != 1 || dilationW != 1) {</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  fmt::format(<span class="stringliteral">"Dilated decnvolution is not supported. "</span></div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  <span class="stringliteral">"{}'s input has dilation {} {}. {}"</span>,</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>  layerParam.name(),</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>  dilationW, dilationH,</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString());</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  }</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span> </div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  <a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">TransposeConvolution2dDescriptor</a> deconvolution2dDescriptor;</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  deconvolution2dDescriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = padW;</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  deconvolution2dDescriptor.m_PadRight = padW;</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  deconvolution2dDescriptor.m_PadTop = padH;</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  deconvolution2dDescriptor.m_PadBottom = padH;</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  deconvolution2dDescriptor.m_StrideX = strideW;</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  deconvolution2dDescriptor.m_StrideY = strideH;</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>  deconvolution2dDescriptor.m_BiasEnabled = convParam.has_bias_term() ? convParam.bias_term() : <span class="keyword">true</span>;</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span> </div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  <span class="keywordflow">if</span> (numGroups > numFilters)</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  {</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  fmt::format(<span class="stringliteral">"Error parsing Deconvolution: {}. "</span></div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  <span class="stringliteral">"The 'group'={} parameter cannot be larger than the "</span></div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  <span class="stringliteral">"number of filters supplied ='{}'. {}"</span>,</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  layerParam.name(),</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  numGroups,</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>  numFilters,</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>  }</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span> </div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>  <span class="keywordflow">if</span> (inputShape.dim_size() != 4)</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>  {</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  fmt::format(<span class="stringliteral">"Deconvolution input shape is expected to have 4 dimensions. "</span></div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  <span class="stringliteral">"{}'s input has only {}. {}"</span>,</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  layerParam.name(),</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  inputShape.dim_size(),</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  }</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span> </div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  <span class="keywordflow">if</span> (numGroups > 1)</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  {</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  <span class="keywordflow">if</span> (numGroups > inputShape.dim(1))</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  {</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  fmt::format(<span class="stringliteral">"Error parsing Deconvolution: {}. "</span></div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  <span class="stringliteral">"The 'group'={} parameter cannot be larger than the "</span></div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  <span class="stringliteral">"channel of the input shape={} (in NCHW format). {}"</span>,</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  layerParam.name(),</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  numGroups,</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  inputShape.dim(1),</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>  }</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  {</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>  <span class="comment">// we split the input by channels into channels/groups separate convolutions</span></div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  <span class="comment">// and concatenate the results afterwards</span></div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77f532a6a82afeb6e79957726a9517a5">AddDeconvLayerWithSplits</a>(layerParam, deconvolution2dDescriptor, kernelW, kernelH);</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  }</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  }</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span> </div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>  <span class="comment">// NOTE: at this point we only need to handle #group=1 case, all other cases should be</span></div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  <span class="comment">// handled by the AddDeconvLayer* helpers</span></div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span> </div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  <span class="comment">// Populate deconvolution output tensor descriptor dimensions</span></div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>  BlobShape outputShape;</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  outputShape.add_dim(0);</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  outputShape.set_dim(0, inputShape.dim(0));</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  outputShape.add_dim(1);</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  outputShape.set_dim(1, numFilters);</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  outputShape.add_dim(2);</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>  outputShape.set_dim(</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>  2, (static_cast<int>(</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>  strideH * (inputShape.dim(2) - 1) - 2 * padH + (dilationH * (kernelH - 1) + 1))));</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>  outputShape.add_dim(3);</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>  outputShape.set_dim(</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>  3, (static_cast<int>(</div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  strideW * (inputShape.dim(3) - 1) - 2 * padW + (dilationW * (kernelW - 1) + 1))));</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span> </div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>  <span class="comment">// Load the weight data for ALL groups</span></div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>  vector<float> weightData(armnn::numeric_cast<size_t>(inputShape.dim(1) *</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  outputShape.dim(1) *</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>  kernelH *</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>  kernelW));</div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>  GetDataFromBlob(layerParam, weightData, 0);</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span> </div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightDimSizes[4] = {</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>  <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(outputShape.dim(1)), <span class="comment">// output channels</span></div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  static_cast<unsigned int>(inputShape.dim(1)), <span class="comment">// input channels</span></div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  kernelH,</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  kernelW};</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span> </div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* returnLayer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span> </div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>  <span class="comment">// Pull out the weights for this group from that loaded from the model file earlier</span></div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(4, weightDimSizes, DataType::Float32), weightData.data());</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalBiases;</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>  vector<float> biasData;</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  <span class="keywordflow">if</span> (deconvolution2dDescriptor.m_BiasEnabled)</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  {</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo;</div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span> </div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>  biasData.resize(armnn::numeric_cast<size_t>(outputShape.dim(1)), 1.f);</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>  GetDataFromBlob(layerParam, biasData, 1);</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span> </div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasDimSizes[1] = {<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(outputShape.dim(1))};</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>  biasInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, biasDimSizes, DataType::Float32);</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span> </div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  <span class="comment">// Pull out the biases for this group from that loaded from the model file earlier</span></div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biases(biasInfo, biasData.data());</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>  optionalBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(biases);</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>  }</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>  returnLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddTransposeConvolution2dLayer(deconvolution2dDescriptor,</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>  weights,</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>  optionalBiases,</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  layerParam.name().c_str());</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span> </div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>& inputConnection = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0));</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>  inputConnection.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(returnLayer->GetInputSlot(0));</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  returnLayer->GetOutputSlot(0).SetTensorInfo(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">BlobShapeToTensorInfo</a>(outputShape));</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span> </div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  <span class="keywordflow">if</span> (!returnLayer)</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  {</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>  fmt::format(<span class="stringliteral">"Failed to create Deconvolution layer. "</span></div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>  <span class="stringliteral">"Layer={} #groups={} #filters={} {}"</span>,</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span>  layerParam.name(),</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>  numGroups,</div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  numFilters,</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>  }</div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span> </div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), returnLayer->GetOutputSlot(0));</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span> }</div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span> </div><div class="line"><a name="l01270"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab3329e4bcd8e42cd314f84c8260b06ad"> 1270</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab3329e4bcd8e42cd314f84c8260b06ad">ICaffeParser::CaffeParserImpl::ParsePoolingLayer</a>(<span class="keyword">const</span> LayerParameter& layerParam)</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span> {</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>  <span class="comment">// Ignored Caffe Parameters</span></div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  <span class="comment">// Stochastic Pooling</span></div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>  <span class="comment">// Engine</span></div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span> </div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>  ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>  PoolingParameter param = layerParam.pooling_param();</div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>();</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span> </div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>  <span class="keyword">const</span> <span class="keyword">auto</span> notFound = std::numeric_limits<unsigned int>::max();</div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span> </div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_h = <a class="code" href="_caffe_parser_8cpp.xhtml#a69f4a692d0095f6b19b0cd99cd75e465">GET_OPTIONAL_WITH_FALLBACK</a>(param, PoolingParameter,</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>  kernel_h, kernel_size, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, notFound);</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_w = <a class="code" href="_caffe_parser_8cpp.xhtml#a69f4a692d0095f6b19b0cd99cd75e465">GET_OPTIONAL_WITH_FALLBACK</a>(param, PoolingParameter,</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>  kernel_w, kernel_size, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, notFound);</div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span> </div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>  <span class="keywordflow">if</span> ((kernel_h == notFound || kernel_w == notFound) && param.has_global_pooling())</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>  {</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>  kernel_h = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>  kernel_w = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3];</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>  }</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span> </div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride_h = <a class="code" href="_caffe_parser_8cpp.xhtml#a69f4a692d0095f6b19b0cd99cd75e465">GET_OPTIONAL_WITH_FALLBACK</a>(param, PoolingParameter,</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>  stride_h, stride, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, notFound);</div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride_w = <a class="code" href="_caffe_parser_8cpp.xhtml#a69f4a692d0095f6b19b0cd99cd75e465">GET_OPTIONAL_WITH_FALLBACK</a>(param, PoolingParameter,</div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>  stride_h, stride, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, notFound);</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span> </div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>  <span class="keywordflow">if</span> ((stride_h == notFound || stride_w == notFound) && param.has_global_pooling())</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>  {</div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>  stride_h = 1;</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>  stride_w = 1;</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>  }</div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span> </div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pad_h = <a class="code" href="_caffe_parser_8cpp.xhtml#a69f4a692d0095f6b19b0cd99cd75e465">GET_OPTIONAL_WITH_FALLBACK</a>(param, PoolingParameter,</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>  pad_h, pad, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 0u);</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pad_w = <a class="code" href="_caffe_parser_8cpp.xhtml#a69f4a692d0095f6b19b0cd99cd75e465">GET_OPTIONAL_WITH_FALLBACK</a>(param, PoolingParameter,</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>  pad_w, pad, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>, 0u);</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span> </div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>  <span class="comment">// Populate Weight and Bias Filter Descriptor</span></div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pooling2dDescriptor;</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>  <span class="keywordflow">if</span> (param.has_pool())</div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  {</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>  PoolingParameter_PoolMethod p = param.pool();</div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>  <span class="keywordflow">switch</span> (p)</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>  {</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>  <span class="keywordflow">case</span> PoolingParameter_PoolMethod_MAX:</div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>  {</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233">PoolingAlgorithm::Max</a>;</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>  }</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>  <span class="keywordflow">case</span> PoolingParameter_PoolMethod_AVE:</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  {</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = PoolingAlgorithm::Average;</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>  }</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>  <span class="keywordflow">case</span> PoolingParameter_PoolMethod_STOCHASTIC:</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  {</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>  fmt::format(<span class="stringliteral">"Pooling Layer: Stochastic Pooling Not Supported. Layer={} {}"</span>,</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>  layerParam.name(),</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>  }</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>  {</div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>  fmt::format(<span class="stringliteral">"Pooling Layer: unknown pooling method: {} for layer: {} {}"</span>,</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>  p,</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>  layerParam.name(),</div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>  }</div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>  }</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span>  }</div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>  {</div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span>  fmt::format(<span class="stringliteral">"No Pooling Method Defined for {} {}"</span>,</div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>  layerParam.name(),</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>  }</div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span> </div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = pad_w;</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = pad_w;</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = pad_h;</div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = pad_h;</div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = stride_w;</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = stride_h;</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = kernel_w;</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = kernel_h;</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span> </div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#affb5b68b3eba3ed45a06c7cde7781962">m_OutputShapeRounding</a> = OutputShapeRounding::Ceiling;</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = PaddingMethod::IgnoreValue;</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span> </div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* poolingLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddPooling2dLayer(pooling2dDescriptor,</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>  layerParam.name().c_str());</div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span> </div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>  { inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0],</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>  inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1],</div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>  <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(ceil(</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>  static_cast<float>(inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2] + 2 * pad_h - kernel_h) /</div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span>  armnn::numeric_cast<float>(stride_h))) + 1,</div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span>  <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(ceil(</div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span>  static_cast<float>(inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3] + 2 * pad_w - kernel_w) /</div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span>  armnn::numeric_cast<float>(stride_w))) + 1 },</div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>  DataType::Float32);</div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span> </div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(poolingLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>  poolingLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), poolingLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span> }</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span> </div><div class="line"><a name="l01382"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5ce905b7412e68d588e08f4afc333aac"> 1382</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5ce905b7412e68d588e08f4afc333aac">ICaffeParser::CaffeParserImpl::ParseArgmaxLayer</a>(<span class="keyword">const</span> LayerParameter& layerParam)</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span> {</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span>  ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>  ArgMaxParameter param = layerParam.argmax_param();</div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span> </div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>  BlobShape inputShape = <a class="code" href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed">TensorDescToBlobShape</a>(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>());</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span> </div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> topK = param.has_top_k() ? param.top_k() : 1;</div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>  <span class="keywordflow">if</span> (topK != 1) {</div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>  fmt::format(<span class="stringliteral">"ArgMaxLayer: Only support top_k equals to 1. Layer={} {}"</span>,</div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>  layerParam.name(),</div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>  }</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span> </div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outMaxVal = param.has_out_max_val() ? param.out_max_val() : <span class="keyword">false</span>;</div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>  <span class="keywordflow">if</span> (outMaxVal) {</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>  fmt::format(<span class="stringliteral">"ArgMaxLayer: Does not support out_max_val. Layer={} {}"</span>,</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>  layerParam.name(),</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>  }</div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span> </div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>  <span class="keywordtype">int</span> axis = param.has_axis() ? param.axis() : 1;</div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>  <span class="keywordflow">if</span> (axis < 0) {</div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>  axis = inputShape.dim_size() - axis;</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  }</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span>  <span class="keywordflow">if</span> ((axis < 0) || (axis >= inputShape.dim_size())) {</div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>  fmt::format(<span class="stringliteral">"ArgMaxLayer: Invalid axis value which outside range of input dims. "</span></div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>  <span class="stringliteral">"{}'s input has input dim_size {}, requested axis: {}. {}"</span>,</div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span>  layerParam.name(),</div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span>  inputShape.dim_size(),</div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>  axis,</div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>  }</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span> </div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>  <a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml">ArgMinMaxDescriptor</a> desc;</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>  desc.<a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml#a214c3636fdf0ea5bac8edb42d0e6c7f0">m_Axis</a> = axis;</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>  desc.<a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml#abce784834696eb928c620f1fafe71a8d">m_Output_Type</a> = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>;</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>  desc.<a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml#ab1ae6f520bb1a4da191a0ae907477f23">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">ArgMinMaxFunction::Max</a>;</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span> </div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* argmaxLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddArgMinMaxLayer(desc,</div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>  layerParam.name().c_str());</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span> </div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape(static_cast<unsigned int>(inputShape.dim_size() - 1));</div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>  <span class="keywordtype">int</span> j = 0;</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>  <span class="comment">// remove the flatten axis</span></div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < inputShape.dim_size(); ++i)</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>  {</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span>  <span class="keywordflow">if</span> (i == axis) <span class="keywordflow">continue</span>;</div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>  outputShape[<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(j++)] = static_cast<unsigned int>(inputShape.dim(i));</div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>  }</div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(outputShape, DataType::Signed32);</div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span> </div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(argmaxLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>  argmaxLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), argmaxLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span> }</div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span> </div><div class="line"><a name="l01442"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a8449e66d395c0525561e3c67b100bafe"> 1442</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a8449e66d395c0525561e3c67b100bafe">ICaffeParser::CaffeParserImpl::ParseReluLayer</a>(<span class="keyword">const</span> LayerParameter& layerParam)</div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span> {</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>  ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span> </div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>  <span class="keyword">const</span> <span class="keywordtype">string</span>& name = layerParam.name();</div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span>  <span class="keyword">const</span> ReLUParameter& param = layerParam.relu_param();</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span> </div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> negativeSlope = param.negative_slope();</div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>  <span class="keywordflow">if</span> (negativeSlope == 0.0f)</div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>  {</div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::ReLu;</div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span>  }</div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span>  {</div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::LeakyReLu;</div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = negativeSlope;</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>  }</div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span> </div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>();</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> activationLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddActivationLayer(activationDescriptor, name.c_str());</div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(activationLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span>  activationLayer-><a class="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="l01465"></a><span class="lineno"> 1465</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), activationLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span> }</div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span> </div><div class="line"><a name="l01468"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a7785119cfebd2b02ba3be888965e52ba"> 1468</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a7785119cfebd2b02ba3be888965e52ba">ICaffeParser::CaffeParserImpl::ParseLRNLayer</a>(<span class="keyword">const</span> LayerParameter& layerParam)</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span> {</div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>  ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span> </div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span>  LRNParameter param = layerParam.lrn_param();</div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span> </div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>();</div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span> </div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>  <span class="comment">// Ignored BATCH NORMALIZATION Caffe Parameters.</span></div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>  <span class="comment">// Ignored MVN Caffe Parameters.</span></div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>  <span class="comment">// Ignored LRN Caffe Parameters.</span></div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>  <span class="comment">// Engine</span></div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span> </div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</span>  <a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml">NormalizationDescriptor</a> normalizationDescriptor;</div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span>  <span class="keywordflow">if</span> (param.has_norm_region())</div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>  {</div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span>  LRNParameter_NormRegion n = param.norm_region();</div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span>  <span class="keywordflow">switch</span> (n)</div><div class="line"><a name="l01486"></a><span class="lineno"> 1486</span>  {</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span>  <span class="keywordflow">case</span> LRNParameter_NormRegion_ACROSS_CHANNELS:</div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>  {</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">m_NormChannelType</a> = NormalizationAlgorithmChannel::Across;</div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>  }</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>  <span class="keywordflow">case</span> LRNParameter_NormRegion_WITHIN_CHANNEL:</div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span>  {</div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">m_NormChannelType</a> = NormalizationAlgorithmChannel::Within;</div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>  }</div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>  {</div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span>  fmt::format(<span class="stringliteral">"Unknown region {} for LRN layer {} {}"</span>,</div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>  n,</div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span>  layerParam.name(),</div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01504"></a><span class="lineno"> 1504</span>  }</div><div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>  }</div><div class="line"><a name="l01506"></a><span class="lineno"> 1506</span>  }</div><div class="line"><a name="l01507"></a><span class="lineno"> 1507</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span>  {</div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>  <span class="comment">// Caffe defaults to normalization across channels.</span></div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">m_NormChannelType</a> = NormalizationAlgorithmChannel::Across;</div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>  }</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span> </div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a05945f080edf694b631960728b87aadb">m_NormMethodType</a> = NormalizationAlgorithmMethod::LocalBrightness;</div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span>  <span class="keywordflow">if</span> (param.has_local_size())</div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span>  {</div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">m_NormSize</a> = param.local_size();</div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>  }</div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>  {</div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span>  fmt::format(<span class="stringliteral">"local_size not defined for LRN layer {} {}"</span>,</div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span>  layerParam.name(),</div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01524"></a><span class="lineno"> 1524</span>  }</div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span> </div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span>  <span class="keywordflow">if</span> (param.has_alpha())</div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span>  {</div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">m_Alpha</a> = param.alpha();</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">m_Alpha</a> /= <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">float</span>>(param.local_size());</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span>  }</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span>  {</div><div class="line"><a name="l01533"></a><span class="lineno"> 1533</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>  fmt::format(<span class="stringliteral">"Alpha not defined for LRN layer {} {}"</span>,</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span>  layerParam.name(),</div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span>  }</div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>  <span class="keywordflow">if</span> (param.has_beta())</div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>  {</div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = param.beta();</div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span>  }</div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>  {</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span>  fmt::format(<span class="stringliteral">"Beta not defined for LRN layer {} {}"</span>,</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>  layerParam.name(),</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>  }</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span> </div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</span>  <span class="keywordflow">if</span> (param.has_k())</div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>  {</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">m_K</a> = param.k();</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>  }</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>  {</div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">m_K</a> = 1;</div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>  }</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span> </div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> normLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddNormalizationLayer(normalizationDescriptor,</div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>  layerParam.name().c_str());</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(normLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</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>(inputInfo);</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span> </div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), normLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span> }</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span> </div><div class="line"><a name="l01567"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a34f6df4b84de1e269bcf02efeecc3892"> 1567</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a34f6df4b84de1e269bcf02efeecc3892">ICaffeParser::CaffeParserImpl::ParseInnerProductLayer</a>(<span class="keyword">const</span> LayerParameter& layerParam)</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span> {</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>  InnerProductParameter param = layerParam.inner_product_param();</div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span> </div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span>  ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span> </div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = param.num_output();</div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span> </div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span>  <span class="comment">// Ignored Caffe Parameters:</span></div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span>  <span class="comment">// Weight Filler</span></div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>  <span class="comment">// Bias Filler</span></div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span>  <span class="comment">// Engine</span></div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span>  <span class="comment">// Axis</span></div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span> </div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> tensorFullyConnectedDescriptor;</div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</span> </div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span>  <span class="keywordflow">if</span> (param.has_transpose())</div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span>  {</div><div class="line"><a name="l01585"></a><span class="lineno"> 1585</span>  <span class="comment">// If true, assumes transposed weights.</span></div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span>  tensorFullyConnectedDescriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> = param.transpose();</div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>  }</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01589"></a><span class="lineno"> 1589</span>  {</div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>  <span class="comment">// Caffe defaults to transposed.</span></div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>  tensorFullyConnectedDescriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>  }</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span> </div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>();</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span> </div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightInfo;</div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo;</div><div class="line"><a name="l01598"></a><span class="lineno"> 1598</span> </div><div class="line"><a name="l01599"></a><span class="lineno"> 1599</span>  <span class="comment">// Allows implicit flattening of extra dimensions.</span></div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 2; i < inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++i)</div><div class="line"><a name="l01602"></a><span class="lineno"> 1602</span>  {</div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</span>  inputSize *= inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i];</div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span>  }</div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span> </div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span>  <span class="keyword">const</span> <span class="keywordtype">float</span>* weightDataPtr = GetArrayPtrFromBlob(layerParam, 0);</div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> swTD[2] = { outputSize, inputSize };</div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(2, swTD, DataType::Float32), weightDataPtr);</div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span> </div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>  tensorFullyConnectedDescriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span>  <span class="comment">// Todo: check whether bias enabled.</span></div><div class="line"><a name="l01612"></a><span class="lineno"> 1612</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* fullyConnectedLayer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01613"></a><span class="lineno"> 1613</span>  <span class="keywordflow">if</span> (tensorFullyConnectedDescriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>  {</div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>  <span class="comment">// BIAS VALUE</span></div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span>  <span class="keyword">const</span> <span class="keywordtype">float</span>* biasDataPtr = GetArrayPtrFromBlob(layerParam, 1);</div><div class="line"><a name="l01617"></a><span class="lineno"> 1617</span> </div><div class="line"><a name="l01618"></a><span class="lineno"> 1618</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> sbTD[1] = { outputSize };</div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span> </div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biases(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, sbTD, DataType::Float32), biasDataPtr);</div><div class="line"><a name="l01621"></a><span class="lineno"> 1621</span> </div><div class="line"><a name="l01622"></a><span class="lineno"> 1622</span>  fullyConnectedLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddFullyConnectedLayer(tensorFullyConnectedDescriptor,</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>  weights,</div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(biases),</div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>  layerParam.name().c_str());</div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>  }</div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span>  {</div><div class="line"><a name="l01629"></a><span class="lineno"> 1629</span>  fullyConnectedLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddFullyConnectedLayer(tensorFullyConnectedDescriptor,</div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span>  weights,</div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>(),</div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span>  layerParam.name().c_str());</div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</span>  }</div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span> </div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo({ inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0], outputSize }, DataType::Float32);</div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span>  fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span> }</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span> </div><div class="line"><a name="l01641"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a1c0594bf03dfbb44029465d3466127b3"> 1641</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a1c0594bf03dfbb44029465d3466127b3">ICaffeParser::CaffeParserImpl::ParseSoftmaxLayer</a>(<span class="keyword">const</span> LayerParameter& layerParam)</div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span> {</div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span>  ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span> </div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span>  SoftmaxParameter param = layerParam.softmax_param();</div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span> </div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>();</div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span> </div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>  <span class="comment">// Ignored Caffe Parameters:</span></div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>  <span class="comment">// axis</span></div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>  <span class="comment">// Engine</span></div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span> </div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span>  <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::SoftmaxDescriptor</a> softmaxDescriptor;</div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>  softmaxDescriptor.<a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml#a214c3636fdf0ea5bac8edb42d0e6c7f0">m_Axis</a> = 1;</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> softmaxLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddSoftmaxLayer(</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span>  softmaxDescriptor,</div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>  layerParam.name().c_str());</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(softmaxLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>  softmaxLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), softmaxLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span> }</div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span> </div><div class="line"><a name="l01663"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a99a846a21b3a6ec97cc1d4344b91df36"> 1663</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a99a846a21b3a6ec97cc1d4344b91df36">ICaffeParser::CaffeParserImpl::ParseEltwiseLayer</a>(<span class="keyword">const</span> LayerParameter& layerParam)</div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span> {</div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>  ValidateNumInputsOutputs(layerParam, 2, 1);</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span> </div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>();</div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span> </div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span>  <span class="comment">// Ignored Caffe Parameters:</span></div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span>  <span class="comment">// coeff</span></div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span> </div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span>  EltwiseParameter_EltwiseOp operation = EltwiseParameter_EltwiseOp_SUM; <span class="comment">// Defaults to sum as per caffe.</span></div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span> </div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span>  <span class="keywordflow">if</span> (layerParam.has_eltwise_param() && layerParam.eltwise_param().has_operation())</div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>  {</div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span>  operation = layerParam.eltwise_param().operation();</div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>  }</div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span> </div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* newLayer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span>  <span class="keywordflow">switch</span> (operation)</div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span>  {</div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span>  <span class="keywordflow">case</span> EltwiseParameter_EltwiseOp_SUM:</div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>  {</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>  newLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddAdditionLayer(layerParam.name().c_str());</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>  }</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span>  <span class="keywordflow">case</span> EltwiseParameter_EltwiseOp_PROD:</div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</span>  {</div><div class="line"><a name="l01689"></a><span class="lineno"> 1689</span>  newLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddMultiplicationLayer(layerParam.name().c_str());</div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span>  }</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span>  {</div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span>  fmt::format(<span class="stringliteral">"Unsupported operation {} in Eltwise layer {} {}"</span>,</div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>  operation,</div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span>  layerParam.name(),</div><div class="line"><a name="l01698"></a><span class="lineno"> 1698</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>  }</div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>  }</div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span> </div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(newLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(1)).<a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(newLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span>  newLayer-><a class="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="l01705"></a><span class="lineno"> 1705</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), newLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span> }</div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span> </div><div class="line"><a name="l01708"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa0f0ff1cae05c1a0d7cc11b498714312"> 1708</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa0f0ff1cae05c1a0d7cc11b498714312">ICaffeParser::CaffeParserImpl::ParseConcatLayer</a>(<span class="keyword">const</span> LayerParameter& layerParam)</div><div class="line"><a name="l01709"></a><span class="lineno"> 1709</span> {</div><div class="line"><a name="l01710"></a><span class="lineno"> 1710</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numInputs = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(layerParam.bottom_size());</div><div class="line"><a name="l01711"></a><span class="lineno"> 1711</span>  <span class="comment">// We assume concat happens along the channel dimension, which is 1 in (0, 1, 2, 3).</span></div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatDim = 1;</div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numOfDims = 4;</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span> </div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span>  <span class="comment">// we only consider 4-D tensor here</span></div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> concatDescriptor(static_cast<uint32_t>(numInputs), numOfDims);</div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>  std::vector<unsigned int>mergeDimSizes(numOfDims, 0u);</div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span> </div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> mergeDim = 0;</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> viewIndex = 0; viewIndex < numInputs; ++viewIndex)</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>  {</div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>  layerParam.bottom(armnn::numeric_cast<int>(viewIndex))).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>();</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>  <span class="comment">// Checks whether the dimensions of the input tensors are actually 4.</span></div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>  <span class="keywordflow">if</span> (inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()!=4)</div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>  {</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>  fmt::format(<span class="stringliteral">"The number of dimensions for input tensors of "</span></div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>  <span class="stringliteral">"the concatenation op should be 4. Inputs of {} has "</span></div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>  <span class="stringliteral">"{} dimensions. {}"</span>,</div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span>  layerParam.name(),</div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span>  inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(),</div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>  }</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span> </div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span>  mergeDimSizes[0] = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0];</div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span>  mergeDimSizes[1] = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span>  mergeDimSizes[2] = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span>  mergeDimSizes[3] = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3];</div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span> </div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> j = 0; j < concatDim; ++j)</div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>  {</div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>  concatDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(viewIndex, j, 0);</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>  }</div><div class="line"><a name="l01745"></a><span class="lineno"> 1745</span> </div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span>  concatDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(viewIndex, concatDim, mergeDim);</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>  mergeDim += mergeDimSizes[concatDim];</div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span> </div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> j = concatDim+1; j < numOfDims; ++j)</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>  {</div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>  concatDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(viewIndex, j, 0);</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span>  }</div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span>  }</div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>  mergeDimSizes[concatDim] = mergeDim;</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span> </div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* concatlayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddConcatLayer(concatDescriptor, layerParam.name().c_str());</div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < numInputs; ++i)</div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span>  {</div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>& outputSlot = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(armnn::numeric_cast<int>(i)));</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span>  outputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(concatlayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(i));</div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>  }</div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span> </div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>  concatlayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(numOfDims, mergeDimSizes.data(), DataType::Float32));</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), concatlayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span> }</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span> </div><div class="line"><a name="l01767"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a940483591995bb812cfcd1595dba83c3"> 1767</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a940483591995bb812cfcd1595dba83c3">ICaffeParser::CaffeParserImpl::ParseBatchNormLayer</a>(<span class="keyword">const</span> LayerParameter& layerParam)</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span> {</div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span>  ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01770"></a><span class="lineno"> 1770</span> </div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>();</div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span> </div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span>  <span class="keywordtype">string</span> name = layerParam.name();</div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span> </div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>  BatchNormParameter param = layerParam.batch_norm_param();</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>  <span class="comment">// If use_global_stats is not explicitly set in the model, assume it to be true (its default value</span></div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span>  <span class="comment">// when the network is in the testing phase).</span></div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>  <span class="keywordflow">if</span> (param.has_use_global_stats())</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>  {</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>  <span class="keywordflow">if</span> (!param.use_global_stats())</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>  {</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>  fmt::format(<span class="stringliteral">"Error parsing Batch Norm layer '{}': "</span></div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>  <span class="stringliteral">"Parameter 'use_global_stats' is set to false, which is "</span></div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>  <span class="stringliteral">"unsupported (value used for training). {}"</span>,</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span>  name,</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>  }</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span>  }</div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span> </div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span>  <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">BatchNormalizationDescriptor</a> desc;</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>  desc.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a> = param.eps();</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span> </div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channels = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape[] = {channels};</div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span> </div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span>  vector<float> meanData(channels);</div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span>  GetDataFromBlob(layerParam, meanData, 0);</div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span> </div><div class="line"><a name="l01800"></a><span class="lineno"> 1800</span>  vector<float> varianceData(channels);</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>  GetDataFromBlob(layerParam, varianceData, 1);</div><div class="line"><a name="l01802"></a><span class="lineno"> 1802</span> </div><div class="line"><a name="l01803"></a><span class="lineno"> 1803</span>  <span class="comment">// Reads moving average factor and applies scaling (if required).</span></div><div class="line"><a name="l01804"></a><span class="lineno"> 1804</span>  <span class="keyword">const</span> BlobProto& blob = layerParam.blobs(armnn::numeric_cast<int>(2));</div><div class="line"><a name="l01805"></a><span class="lineno"> 1805</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> movingAverageFactor = blob.data(armnn::numeric_cast<int>(0));</div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span>  <span class="keywordflow">if</span>(movingAverageFactor != 0.0f)</div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>  {</div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> scaleFactor = 1.0f / movingAverageFactor;</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>  <span class="keyword">auto</span> scaleFunction = [scaleFactor](<span class="keywordtype">float</span> f) -> <span class="keywordtype">float</span> { <span class="keywordflow">return</span> f * scaleFactor; };</div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span> </div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>  std::transform(varianceData.begin(), varianceData.end(), varianceData.begin(), scaleFunction);</div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span>  std::transform(meanData.begin(), meanData.end(), meanData.begin(), scaleFunction);</div><div class="line"><a name="l01813"></a><span class="lineno"> 1813</span>  }</div><div class="line"><a name="l01814"></a><span class="lineno"> 1814</span> </div><div class="line"><a name="l01815"></a><span class="lineno"> 1815</span>  <span class="comment">// Identifies scale operation.</span></div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span>  vector<float> betaData(channels, 0.0f);</div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span>  vector<float> gammaData(channels, 1.0f);</div><div class="line"><a name="l01818"></a><span class="lineno"> 1818</span> </div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> mean(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), meanData);</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> variance(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), varianceData);</div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> beta(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), betaData);</div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> gamma(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), gammaData);</div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span> </div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> batchNormLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddBatchNormalizationLayer(desc,</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>  mean, variance, beta, gamma, name.c_str());</div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(batchNormLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span>  batchNormLayer-><a class="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="l01828"></a><span class="lineno"> 1828</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), batchNormLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01829"></a><span class="lineno"> 1829</span> }</div><div class="line"><a name="l01830"></a><span class="lineno"> 1830</span> </div><div class="line"><a name="l01831"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a95799625a4aae0ed73838cbfa3530c1b"> 1831</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a95799625a4aae0ed73838cbfa3530c1b">ICaffeParser::CaffeParserImpl::ParseScaleLayer</a>(<span class="keyword">const</span> LayerParameter& layerParam)</div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span> {</div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span>  <span class="comment">// Current unoptimal solution: add a batchnormalization layer with 0 mean and 1 variance.</span></div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>  ValidateNumInputsOutputs(layerParam, 1, 1);</div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span> </div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>();</div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span> </div><div class="line"><a name="l01838"></a><span class="lineno"> 1838</span>  <span class="keywordtype">string</span> name = layerParam.name();</div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span> </div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>  ScaleParameter param = layerParam.scale_param();</div><div class="line"><a name="l01841"></a><span class="lineno"> 1841</span>  <span class="keywordflow">if</span> (param.axis() != 1)</div><div class="line"><a name="l01842"></a><span class="lineno"> 1842</span>  {</div><div class="line"><a name="l01843"></a><span class="lineno"> 1843</span>  <span class="comment">// Would have to use something other than BatchNormalizationLayer in this case</span></div><div class="line"><a name="l01844"></a><span class="lineno"> 1844</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01845"></a><span class="lineno"> 1845</span>  fmt::format(<span class="stringliteral">"Loading Scale Layer: Only axis 1 is supported currently. "</span></div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span>  <span class="stringliteral">"Layer={} Axis={} {}"</span>,</div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>  layerParam.name(),</div><div class="line"><a name="l01848"></a><span class="lineno"> 1848</span>  param.axis(),</div><div class="line"><a name="l01849"></a><span class="lineno"> 1849</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01850"></a><span class="lineno"> 1850</span>  }</div><div class="line"><a name="l01851"></a><span class="lineno"> 1851</span> </div><div class="line"><a name="l01852"></a><span class="lineno"> 1852</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channels = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape[] = {channels};</div><div class="line"><a name="l01854"></a><span class="lineno"> 1854</span> </div><div class="line"><a name="l01855"></a><span class="lineno"> 1855</span>  <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">BatchNormalizationDescriptor</a> desc;</div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>  desc.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a> = 0.0f; <span class="comment">// Don't need epsilon if variance is 1.</span></div><div class="line"><a name="l01857"></a><span class="lineno"> 1857</span>  vector<float> meanData(channels, 0.0f);</div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</span>  vector<float> varianceData(channels, 1.0f);</div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</span>  vector<float> betaData(channels, 0.0f);</div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>  vector<float> gammaData(channels);</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span> </div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span>  GetDataFromBlob(layerParam, gammaData, 0);</div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span> </div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span>  <span class="keywordflow">if</span>(param.has_bias_term())</div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span>  {</div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span>  GetDataFromBlob(layerParam, betaData, 1);</div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>  }</div><div class="line"><a name="l01868"></a><span class="lineno"> 1868</span> </div><div class="line"><a name="l01869"></a><span class="lineno"> 1869</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> mean(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), meanData);</div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> variance(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), varianceData);</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> beta(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), betaData);</div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> gamma(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), gammaData);</div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span> </div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> batchNormLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddBatchNormalizationLayer(desc,</div><div class="line"><a name="l01875"></a><span class="lineno"> 1875</span>  mean, variance, beta, gamma, name.c_str());</div><div class="line"><a name="l01876"></a><span class="lineno"> 1876</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)).<a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(batchNormLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01877"></a><span class="lineno"> 1877</span>  batchNormLayer-><a class="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="l01878"></a><span class="lineno"> 1878</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), batchNormLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</span> }</div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span> </div><div class="line"><a name="l01881"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a3311e9dc3436fe83ef22c5f530fd3234"> 1881</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a3311e9dc3436fe83ef22c5f530fd3234">ICaffeParser::CaffeParserImpl::ParseSplitLayer</a>(<span class="keyword">const</span> caffe::LayerParameter& layerParam)</div><div class="line"><a name="l01882"></a><span class="lineno"> 1882</span> {</div><div class="line"><a name="l01883"></a><span class="lineno"> 1883</span>  <span class="comment">// Used in caffe to duplicate memory - not necessary in armnn.</span></div><div class="line"><a name="l01884"></a><span class="lineno"> 1884</span>  <span class="keywordflow">if</span> (layerParam.bottom_size() != 1)</div><div class="line"><a name="l01885"></a><span class="lineno"> 1885</span>  {</div><div class="line"><a name="l01886"></a><span class="lineno"> 1886</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span>  fmt::format(<span class="stringliteral">"Split layer '{}' should have exactly 1 bottom. "</span></div><div class="line"><a name="l01888"></a><span class="lineno"> 1888</span>  <span class="stringliteral">"#bottoms={} {}"</span>,</div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>  layerParam.name(),</div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span>  layerParam.bottom_size(),</div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>  }</div><div class="line"><a name="l01893"></a><span class="lineno"> 1893</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>& outputSlot = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0));</div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < layerParam.top_size(); i++)</div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>  {</div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(i), outputSlot);</div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span>  }</div><div class="line"><a name="l01898"></a><span class="lineno"> 1898</span> }</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span> </div><div class="line"><a name="l01900"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa4c22681675806fa2c5fbf403d49c628"> 1900</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa4c22681675806fa2c5fbf403d49c628">ICaffeParser::CaffeParserImpl::ParseDropoutLayer</a>(<span class="keyword">const</span> caffe::LayerParameter& layerParam)</div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span> {</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span>  <span class="comment">// Ignored for inference, so patch the single input to its single output.</span></div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span>  <span class="keywordflow">if</span> (layerParam.bottom_size() != 1 || layerParam.top_size() != 1)</div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>  {</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>  fmt::format(<span class="stringliteral">"Dropout layer '{}' should have exactly 1 bottom and 1 top. "</span></div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>  <span class="stringliteral">"#bottoms={} #tops={} {}"</span>,</div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>  layerParam.name(),</div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>  layerParam.bottom_size(),</div><div class="line"><a name="l01910"></a><span class="lineno"> 1910</span>  layerParam.top_size(),</div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01912"></a><span class="lineno"> 1912</span>  }</div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">SetArmnnOutputSlotForCaffeTop</a>(layerParam.top(0), <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(layerParam.bottom(0)));</div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span> }</div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span> </div><div class="line"><a name="l01916"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2a1112c66d08e3760ecccf39c7854a90"> 1916</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2a1112c66d08e3760ecccf39c7854a90">ICaffeParser::CaffeParserImpl::TrackInputBinding</a>(<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l01917"></a><span class="lineno"> 1917</span>  <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span>,</div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& tensorInfo)</div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span> {</div><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5f5e6255b21fdf458d3733bbdcdc4af5">TrackBindingPoint</a>(layer, <span class="keywordtype">id</span>, tensorInfo, layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>(), <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac62e2558c14e01605f2b4e1e21cdd1e8">m_NetworkInputsBindingInfo</a>);</div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span> }</div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span> </div><div class="line"><a name="l01923"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0c98e07875a82c71c65bbb53eb347561"> 1923</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0c98e07875a82c71c65bbb53eb347561">ICaffeParser::CaffeParserImpl::TrackOutputBinding</a>(<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>  <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span>,</div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& tensorInfo)</div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span> {</div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5f5e6255b21fdf458d3733bbdcdc4af5">TrackBindingPoint</a>(layer, <span class="keywordtype">id</span>, tensorInfo, layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>(), <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a62d6d6cba9ed0d3ad63fffb40aec86b5">m_NetworkOutputsBindingInfo</a>);</div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span> }</div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span> </div><div class="line"><a name="l01930"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5f5e6255b21fdf458d3733bbdcdc4af5"> 1930</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5f5e6255b21fdf458d3733bbdcdc4af5">ICaffeParser::CaffeParserImpl::TrackBindingPoint</a>(<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>  <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span>,</div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& tensorInfo,</div><div class="line"><a name="l01933"></a><span class="lineno"> 1933</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* bindingPointDesc,</div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>  std::unordered_map<std::string, BindingPointInfo>& nameToBindingInfo)</div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span> {</div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span>  <span class="keyword">const</span> std::string layerName = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>();</div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>  <span class="keyword">auto</span> it = nameToBindingInfo.find(layerName);</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>  <span class="keywordflow">if</span> (it == nameToBindingInfo.end())</div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>  {</div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>  nameToBindingInfo[layerName] = std::make_pair(<span class="keywordtype">id</span>, tensorInfo);</div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>  }</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01943"></a><span class="lineno"> 1943</span>  {</div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>  fmt::format(<span class="stringliteral">"Id {} used by more than one {} layer {}"</span>,</div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>  <span class="keywordtype">id</span>,</div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>  bindingPointDesc,</div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span>  }</div><div class="line"><a name="l01950"></a><span class="lineno"> 1950</span> }</div><div class="line"><a name="l01951"></a><span class="lineno"> 1951</span> </div><div class="line"><a name="l01952"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06"> 1952</a></span> <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>& <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">ICaffeParser::CaffeParserImpl::GetArmnnOutputSlotForCaffeTop</a>(<span class="keyword">const</span> std::string& caffeTopName)<span class="keyword"> const</span></div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span> <span class="keyword"></span>{</div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>  <span class="keyword">auto</span> it = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c99d40a72e6f0c6e4ad92d21e44edca">m_ArmnnOutputSlotForCaffeTop</a>.find(caffeTopName);</div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>  <span class="keywordflow">if</span> (it != <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c99d40a72e6f0c6e4ad92d21e44edca">m_ArmnnOutputSlotForCaffeTop</a>.end())</div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>  {</div><div class="line"><a name="l01957"></a><span class="lineno"> 1957</span>  <span class="keywordflow">return</span> *it->second;</div><div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>  }</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01960"></a><span class="lineno"> 1960</span>  {</div><div class="line"><a name="l01961"></a><span class="lineno"> 1961</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>  fmt::format(<span class="stringliteral">"Could not find armnn output slot for Caffe top '{}' {}"</span>,</div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>  caffeTopName,</div><div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>  }</div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span> }</div><div class="line"><a name="l01967"></a><span class="lineno"> 1967</span> </div><div class="line"><a name="l01968"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa"> 1968</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">ICaffeParser::CaffeParserImpl::SetArmnnOutputSlotForCaffeTop</a>(</div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span>  <span class="keyword">const</span> std::string& caffeTopName, <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>& armnnOutputSlot)</div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span> {</div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>  <span class="keyword">auto</span> it = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c99d40a72e6f0c6e4ad92d21e44edca">m_ArmnnOutputSlotForCaffeTop</a>.find(caffeTopName);</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>  <span class="keywordflow">if</span> (it == <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c99d40a72e6f0c6e4ad92d21e44edca">m_ArmnnOutputSlotForCaffeTop</a>.end())</div><div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>  {</div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c99d40a72e6f0c6e4ad92d21e44edca">m_ArmnnOutputSlotForCaffeTop</a>[caffeTopName] = &armnnOutputSlot;</div><div class="line"><a name="l01975"></a><span class="lineno"> 1975</span>  }</div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>  {</div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>  fmt::format(<span class="stringliteral">"Attempting to add duplicate entry for Caffe top '{}' {}"</span>,</div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>  caffeTopName,</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>  }</div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span> }</div><div class="line"><a name="l01984"></a><span class="lineno"> 1984</span> </div><div class="line"><a name="l01985"></a><span class="lineno"> 1985</span> <span class="comment">// Note: can move to CaffeParser when/if we optimise the text/string format</span></div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span> <span class="comment">// to load on a layer by layer basis</span></div><div class="line"><a name="l01987"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a89631aa06b5c628c46674c202b40dbc5"> 1987</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a89631aa06b5c628c46674c202b40dbc5">ICaffeParser::CaffeParserImpl::ResolveInPlaceLayers</a>(caffe::NetParameter& netParameter)</div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span> {</div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span>  <span class="comment">// Finds layers with the same top.</span></div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>  std::map<std::string, std::vector<caffe::LayerParameter*>> layersByTop;</div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> layerIdx = 0; layerIdx < netParameter.layer_size(); ++layerIdx)</div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>  {</div><div class="line"><a name="l01993"></a><span class="lineno"> 1993</span>  caffe::LayerParameter& layer = *netParameter.mutable_layer(layerIdx);</div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>  std::string name = layer.name();</div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < layer.top_size(); ++i)</div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>  {</div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>  layersByTop[layer.top(i)].push_back(&layer);</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>  }</div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span>  }</div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span> </div><div class="line"><a name="l02001"></a><span class="lineno"> 2001</span>  <span class="comment">// For each set of layers with the same top, resolves them to a linear chain rather than in-place layers.</span></div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>  <span class="comment">// Note that for 'regular' layers, there will be a single layer in each group and so this will be a no-op.</span></div><div class="line"><a name="l02003"></a><span class="lineno"> 2003</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> layersWithSameTopIt : layersByTop)</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>  {</div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span>  <span class="keyword">const</span> std::string& top = layersWithSameTopIt.first;</div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>  <span class="keyword">const</span> std::vector<caffe::LayerParameter*>& layersWithSameTop = layersWithSameTopIt.second;</div><div class="line"><a name="l02007"></a><span class="lineno"> 2007</span> </div><div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>  <span class="comment">// Chains the layers together in the order that they are listed in the prototxt (hopefully this is correct).</span></div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>  <span class="comment">// Note that the last layer will not have its top modified so that other layers will continue to reference it.</span></div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> layerIdx = 0; layerIdx < layersWithSameTop.size() - 1; ++layerIdx)</div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>  {</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>  caffe::LayerParameter& layer1 = *layersWithSameTop[layerIdx];</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>  caffe::LayerParameter& layer2 = *layersWithSameTop[layerIdx+1];</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>  <span class="keywordflow">if</span> (layer1.top_size() != 1)</div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>  {</div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span>  fmt::format(<span class="stringliteral">"Node '{}' is an in-place layer but doesn't have exactly one "</span></div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>  <span class="stringliteral">"top. It has {} instead. {}"</span>,</div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>  layer1.name(),</div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span>  layer1.top_size(),</div><div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>  }</div><div class="line"><a name="l02023"></a><span class="lineno"> 2023</span>  std::string newTop = layer1.name() + <span class="stringliteral">"_top"</span>;</div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>  layer1.set_top(0, newTop);</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>  <span class="keywordflow">if</span> (layer2.bottom_size() != 1 || layer2.bottom(0) != top)</div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>  {</div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>  fmt::format(<span class="stringliteral">"Node '{}' is an in-place layer but "</span></div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>  <span class="stringliteral">"doesn't have exactly one bottom, or it doesn't match its top. "</span></div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>  <span class="stringliteral">"#bottoms={}, first bottom is {}, top is {} {}"</span>,</div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span>  layer2.name(),</div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>  layer2.bottom(0),</div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span>  top,</div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>  }</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span>  layer2.set_bottom(0, newTop);</div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>  }</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span>  }</div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span> }</div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span> </div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span> <span class="comment">// Note: can move to CaffeParser when/if we optimise the text/string format</span></div><div class="line"><a name="l02042"></a><span class="lineno"> 2042</span> <span class="comment">// to load on a layer by layer basis</span></div><div class="line"><a name="l02043"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c5eed5d48d21a8b7e3bcd2cab519217"> 2043</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c5eed5d48d21a8b7e3bcd2cab519217">ICaffeParser::CaffeParserImpl::LoadNetParam</a>(NetParameter& netParameter)</div><div class="line"><a name="l02044"></a><span class="lineno"> 2044</span> {</div><div class="line"><a name="l02045"></a><span class="lineno"> 2045</span>  <span class="comment">// Caffe models sometimes have an implicit input layer.</span></div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>  <span class="comment">// In that case, add an explicit one.</span></div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span>  <span class="keywordflow">if</span> (netParameter.input_size() > 0)</div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span>  {</div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>  LayerParameter* newLayer = netParameter.add_layer();</div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span> </div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</span>  newLayer->set_type(<span class="stringliteral">"Input"</span>);</div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span>  newLayer->set_name(netParameter.input(0));</div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span>  newLayer->add_top(netParameter.input(0));</div><div class="line"><a name="l02054"></a><span class="lineno"> 2054</span> </div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span>  InputParameter* inputParam = newLayer->mutable_input_param();</div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span>  BlobShape* shape = inputParam->add_shape();</div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span> </div><div class="line"><a name="l02058"></a><span class="lineno"> 2058</span>  <span class="keywordtype">int</span> dim_size = netParameter.input_dim_size();</div><div class="line"><a name="l02059"></a><span class="lineno"> 2059</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < dim_size; ++i)</div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span>  {</div><div class="line"><a name="l02061"></a><span class="lineno"> 2061</span>  shape->add_dim(netParameter.input_dim(i));</div><div class="line"><a name="l02062"></a><span class="lineno"> 2062</span>  }</div><div class="line"><a name="l02063"></a><span class="lineno"> 2063</span>  }</div><div class="line"><a name="l02064"></a><span class="lineno"> 2064</span> </div><div class="line"><a name="l02065"></a><span class="lineno"> 2065</span>  <span class="comment">// Replaces in-place layers with regular ones to make the rest of the parsing easier.</span></div><div class="line"><a name="l02066"></a><span class="lineno"> 2066</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a89631aa06b5c628c46674c202b40dbc5">ResolveInPlaceLayers</a>(netParameter);</div><div class="line"><a name="l02067"></a><span class="lineno"> 2067</span> </div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span>  <span class="comment">// Creates a lookup of Caffe layers by name.</span></div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < netParameter.layer_size(); ++i)</div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>  {</div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span>  <span class="keyword">const</span> caffe::LayerParameter& layer = netParameter.layer(i);</div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < layer.top_size(); ++i)</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span>  {</div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a1424f1bfbfc81d317b51053bbb315ef1">m_CaffeLayersByTopName</a>[layer.top(i)] = &layer;</div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span>  }</div><div class="line"><a name="l02076"></a><span class="lineno"> 2076</span>  }</div><div class="line"><a name="l02077"></a><span class="lineno"> 2077</span> </div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>  <span class="comment">// Finds the output layers the user requested.</span></div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span>  std::vector<const caffe::LayerParameter*> targetLayers;</div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> std::string& requestedOutputName : <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a86cb41745deebd9b0ccf157d97d4d9ca">m_RequestedOutputs</a>)</div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>  {</div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span>  <span class="keyword">auto</span> nodeIt = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a1424f1bfbfc81d317b51053bbb315ef1">m_CaffeLayersByTopName</a>.find(requestedOutputName);</div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span>  <span class="keywordflow">if</span> (nodeIt == <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a1424f1bfbfc81d317b51053bbb315ef1">m_CaffeLayersByTopName</a>.end())</div><div class="line"><a name="l02084"></a><span class="lineno"> 2084</span>  {</div><div class="line"><a name="l02085"></a><span class="lineno"> 2085</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span>  fmt::format(<span class="stringliteral">"Couldn't find requested output layer '{}' in graph {}"</span>,</div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span>  requestedOutputName,</div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02089"></a><span class="lineno"> 2089</span>  }</div><div class="line"><a name="l02090"></a><span class="lineno"> 2090</span>  targetLayers.push_back(nodeIt->second);</div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span>  }</div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span> </div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span>  <span class="comment">// Sorts them into a linear ordering such that all inputs of a node are before the node itself.</span></div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span>  std::vector<const caffe::LayerParameter*> sortedNodes;</div><div class="line"><a name="l02095"></a><span class="lineno"> 2095</span>  <span class="keywordflow">if</span> (!armnnUtils::GraphTopologicalSort<const caffe::LayerParameter*>(</div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</span>  targetLayers,</div><div class="line"><a name="l02097"></a><span class="lineno"> 2097</span>  [<span class="keyword">this</span>](<span class="keyword">const</span> caffe::LayerParameter* node)</div><div class="line"><a name="l02098"></a><span class="lineno"> 2098</span>  {</div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2f8fbd66c1a39a06d61fcb6536387d64">GetInputs</a>(*node);</div><div class="line"><a name="l02100"></a><span class="lineno"> 2100</span>  },</div><div class="line"><a name="l02101"></a><span class="lineno"> 2101</span>  sortedNodes))</div><div class="line"><a name="l02102"></a><span class="lineno"> 2102</span>  {</div><div class="line"><a name="l02103"></a><span class="lineno"> 2103</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span>  fmt::format(<span class="stringliteral">"Cycle detected in graph. #nodes: {} {}"</span>,</div><div class="line"><a name="l02105"></a><span class="lineno"> 2105</span>  sortedNodes.size(),</div><div class="line"><a name="l02106"></a><span class="lineno"> 2106</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02107"></a><span class="lineno"> 2107</span>  }</div><div class="line"><a name="l02108"></a><span class="lineno"> 2108</span> </div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span>  <span class="comment">// Parses each node in order, knowing that all inputs of a node will be processed before the node itself.</span></div><div class="line"><a name="l02110"></a><span class="lineno"> 2110</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> caffe::LayerParameter* current : sortedNodes)</div><div class="line"><a name="l02111"></a><span class="lineno"> 2111</span>  {</div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span>  <span class="keyword">auto</span> it = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a6fb0cd80a09cf767309175fb138d203b">ms_CaffeLayerNameToParsingFunctions</a>.find(current->type());</div><div class="line"><a name="l02113"></a><span class="lineno"> 2113</span>  <span class="keywordflow">if</span> (it == <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a6fb0cd80a09cf767309175fb138d203b">ms_CaffeLayerNameToParsingFunctions</a>.end())</div><div class="line"><a name="l02114"></a><span class="lineno"> 2114</span>  {</div><div class="line"><a name="l02115"></a><span class="lineno"> 2115</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02116"></a><span class="lineno"> 2116</span>  fmt::format(<span class="stringliteral">"Unsupported layer type: '{}' for layer {} {}"</span>,</div><div class="line"><a name="l02117"></a><span class="lineno"> 2117</span>  current->type(),</div><div class="line"><a name="l02118"></a><span class="lineno"> 2118</span>  current->name(),</div><div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02120"></a><span class="lineno"> 2120</span>  }</div><div class="line"><a name="l02121"></a><span class="lineno"> 2121</span>  <span class="keyword">auto</span> func = it->second;</div><div class="line"><a name="l02122"></a><span class="lineno"> 2122</span>  (this->*func)(*current);</div><div class="line"><a name="l02123"></a><span class="lineno"> 2123</span>  }</div><div class="line"><a name="l02124"></a><span class="lineno"> 2124</span> </div><div class="line"><a name="l02125"></a><span class="lineno"> 2125</span>  <span class="comment">// Adds ArmNN output layers connected to each requested output.</span></div><div class="line"><a name="l02126"></a><span class="lineno"> 2126</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> std::string& requestedOutput : m_RequestedOutputs)</div><div class="line"><a name="l02127"></a><span class="lineno"> 2127</span>  {</div><div class="line"><a name="l02128"></a><span class="lineno"> 2128</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>& outputSlot = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">GetArmnnOutputSlotForCaffeTop</a>(requestedOutput);</div><div class="line"><a name="l02129"></a><span class="lineno"> 2129</span> </div><div class="line"><a name="l02130"></a><span class="lineno"> 2130</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> outputId = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a>>(</div><div class="line"><a name="l02131"></a><span class="lineno"> 2131</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a62d6d6cba9ed0d3ad63fffb40aec86b5">m_NetworkOutputsBindingInfo</a>.size());</div><div class="line"><a name="l02132"></a><span class="lineno"> 2132</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>->AddOutputLayer(outputId, requestedOutput.c_str());</div><div class="line"><a name="l02133"></a><span class="lineno"> 2133</span>  outputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer->GetInputSlot(0));</div><div class="line"><a name="l02134"></a><span class="lineno"> 2134</span> </div><div class="line"><a name="l02135"></a><span class="lineno"> 2135</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0c98e07875a82c71c65bbb53eb347561">TrackOutputBinding</a>(outputLayer, outputId, outputLayer->GetInputSlot(0).GetConnection()->GetTensorInfo());</div><div class="line"><a name="l02136"></a><span class="lineno"> 2136</span>  }</div><div class="line"><a name="l02137"></a><span class="lineno"> 2137</span> }</div><div class="line"><a name="l02138"></a><span class="lineno"> 2138</span> </div><div class="line"><a name="l02139"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac915fb2df2772be3179e97b1e8287a2d"> 2139</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac915fb2df2772be3179e97b1e8287a2d">ICaffeParser::CaffeParserImpl::CreateNetworkFromTextFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile,</div><div class="line"><a name="l02140"></a><span class="lineno"> 2140</span>  <span class="keyword">const</span> std::map<std::string, armnn::TensorShape>& inputShapes,</div><div class="line"><a name="l02141"></a><span class="lineno"> 2141</span>  <span class="keyword">const</span> std::vector<std::string>& requestedOutputs)</div><div class="line"><a name="l02142"></a><span class="lineno"> 2142</span> {</div><div class="line"><a name="l02143"></a><span class="lineno"> 2143</span>  FILE* fd = fopen(graphFile, <span class="stringliteral">"r"</span>);</div><div class="line"><a name="l02144"></a><span class="lineno"> 2144</span> </div><div class="line"><a name="l02145"></a><span class="lineno"> 2145</span>  <span class="keywordflow">if</span> (fd == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>  {</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_file_not_found_exception.xhtml">FileNotFoundException</a>(</div><div class="line"><a name="l02148"></a><span class="lineno"> 2148</span>  fmt::format(<span class="stringliteral">"Failed to open graph file: {} {}"</span>,</div><div class="line"><a name="l02149"></a><span class="lineno"> 2149</span>  graphFile,</div><div class="line"><a name="l02150"></a><span class="lineno"> 2150</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02151"></a><span class="lineno"> 2151</span>  }</div><div class="line"><a name="l02152"></a><span class="lineno"> 2152</span> </div><div class="line"><a name="l02153"></a><span class="lineno"> 2153</span>  <span class="comment">// Parses the file into a message.</span></div><div class="line"><a name="l02154"></a><span class="lineno"> 2154</span>  NetParameter netParam;</div><div class="line"><a name="l02155"></a><span class="lineno"> 2155</span>  <span class="keyword">auto</span> input = <span class="keyword">new</span> google::protobuf::io::FileInputStream(fileno(fd));</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span>  <span class="keywordtype">bool</span> success = google::protobuf::TextFormat::Parse(input, &netParam);</div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span>  <span class="keyword">delete</span> input;</div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span>  fclose(fd);</div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span> </div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span>  <span class="keywordflow">if</span> (!success)</div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span>  {</div><div class="line"><a name="l02162"></a><span class="lineno"> 2162</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02163"></a><span class="lineno"> 2163</span>  fmt::format(<span class="stringliteral">"Failed to parse graph file: {} {}"</span>,</div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span>  graphFile,</div><div class="line"><a name="l02165"></a><span class="lineno"> 2165</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02166"></a><span class="lineno"> 2166</span>  }</div><div class="line"><a name="l02167"></a><span class="lineno"> 2167</span> </div><div class="line"><a name="l02168"></a><span class="lineno"> 2168</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab0bb4b8a290f1c8acd3c3a0d9a6e9783">CreateNetworkFromNetParameter</a>(netParam, inputShapes, requestedOutputs);</div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span> }</div><div class="line"><a name="l02170"></a><span class="lineno"> 2170</span> </div><div class="line"><a name="l02171"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a7e393f41f2330006fdf00f2840c6dd28"> 2171</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a7e393f41f2330006fdf00f2840c6dd28">ICaffeParser::CaffeParserImpl::CreateNetworkFromString</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* protoText,</div><div class="line"><a name="l02172"></a><span class="lineno"> 2172</span>  <span class="keyword">const</span> std::map<std::string, armnn::TensorShape>& inputShapes,</div><div class="line"><a name="l02173"></a><span class="lineno"> 2173</span>  <span class="keyword">const</span> std::vector<std::string>& requestedOutputs)</div><div class="line"><a name="l02174"></a><span class="lineno"> 2174</span> {</div><div class="line"><a name="l02175"></a><span class="lineno"> 2175</span>  <span class="comment">// Parses the string into a message.</span></div><div class="line"><a name="l02176"></a><span class="lineno"> 2176</span>  NetParameter netParam;</div><div class="line"><a name="l02177"></a><span class="lineno"> 2177</span>  <span class="keywordtype">bool</span> success = google::protobuf::TextFormat::ParseFromString(protoText, &netParam);</div><div class="line"><a name="l02178"></a><span class="lineno"> 2178</span> </div><div class="line"><a name="l02179"></a><span class="lineno"> 2179</span>  <span class="keywordflow">if</span> (!success)</div><div class="line"><a name="l02180"></a><span class="lineno"> 2180</span>  {</div><div class="line"><a name="l02181"></a><span class="lineno"> 2181</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02182"></a><span class="lineno"> 2182</span>  fmt::format(<span class="stringliteral">"Failed to parse graph string {}"</span>,</div><div class="line"><a name="l02183"></a><span class="lineno"> 2183</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02184"></a><span class="lineno"> 2184</span>  }</div><div class="line"><a name="l02185"></a><span class="lineno"> 2185</span> </div><div class="line"><a name="l02186"></a><span class="lineno"> 2186</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab0bb4b8a290f1c8acd3c3a0d9a6e9783">CreateNetworkFromNetParameter</a>(netParam, inputShapes, requestedOutputs);</div><div class="line"><a name="l02187"></a><span class="lineno"> 2187</span> }</div><div class="line"><a name="l02188"></a><span class="lineno"> 2188</span> </div><div class="line"><a name="l02189"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_caffe_parser.xhtml#afb0edadd00c78430efbdc02844ef379a"> 2189</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_caffe_parser_1_1_caffe_parser.xhtml#afb0edadd00c78430efbdc02844ef379a">CaffeParser::CreateNetworkFromBinaryFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile,</div><div class="line"><a name="l02190"></a><span class="lineno"> 2190</span>  <span class="keyword">const</span> std::map<std::string, armnn::TensorShape>& inputShapes,</div><div class="line"><a name="l02191"></a><span class="lineno"> 2191</span>  <span class="keyword">const</span> std::vector<std::string>& requestedOutputs)</div><div class="line"><a name="l02192"></a><span class="lineno"> 2192</span> {</div><div class="line"><a name="l02193"></a><span class="lineno"> 2193</span>  FILE* fd = fopen(graphFile, <span class="stringliteral">"rb"</span>);</div><div class="line"><a name="l02194"></a><span class="lineno"> 2194</span> </div><div class="line"><a name="l02195"></a><span class="lineno"> 2195</span>  <span class="keywordflow">if</span> (fd == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l02196"></a><span class="lineno"> 2196</span>  {</div><div class="line"><a name="l02197"></a><span class="lineno"> 2197</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_file_not_found_exception.xhtml">FileNotFoundException</a>(</div><div class="line"><a name="l02198"></a><span class="lineno"> 2198</span>  fmt::format(<span class="stringliteral">"Failed to open graph file at: {} {}"</span>,</div><div class="line"><a name="l02199"></a><span class="lineno"> 2199</span>  graphFile,</div><div class="line"><a name="l02200"></a><span class="lineno"> 2200</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02201"></a><span class="lineno"> 2201</span>  }</div><div class="line"><a name="l02202"></a><span class="lineno"> 2202</span> </div><div class="line"><a name="l02203"></a><span class="lineno"> 2203</span>  <span class="comment">// Parses the file into a message.</span></div><div class="line"><a name="l02204"></a><span class="lineno"> 2204</span>  NetParameter netParam;</div><div class="line"><a name="l02205"></a><span class="lineno"> 2205</span> </div><div class="line"><a name="l02206"></a><span class="lineno"> 2206</span>  FileInputStream inStream(fileno(fd));</div><div class="line"><a name="l02207"></a><span class="lineno"> 2207</span>  CodedInputStream codedStream(&inStream);</div><div class="line"><a name="l02208"></a><span class="lineno"> 2208</span>  codedStream.SetTotalBytesLimit(INT_MAX);</div><div class="line"><a name="l02209"></a><span class="lineno"> 2209</span>  <span class="keywordtype">bool</span> success = netParam.ParseFromCodedStream(&codedStream);</div><div class="line"><a name="l02210"></a><span class="lineno"> 2210</span>  fclose(fd);</div><div class="line"><a name="l02211"></a><span class="lineno"> 2211</span> </div><div class="line"><a name="l02212"></a><span class="lineno"> 2212</span>  <span class="keywordflow">if</span> (!success)</div><div class="line"><a name="l02213"></a><span class="lineno"> 2213</span>  {</div><div class="line"><a name="l02214"></a><span class="lineno"> 2214</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02215"></a><span class="lineno"> 2215</span>  fmt::format(<span class="stringliteral">"Failed to parse protobuf file: {} {}"</span>,</div><div class="line"><a name="l02216"></a><span class="lineno"> 2216</span>  graphFile,</div><div class="line"><a name="l02217"></a><span class="lineno"> 2217</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02218"></a><span class="lineno"> 2218</span>  }</div><div class="line"><a name="l02219"></a><span class="lineno"> 2219</span> </div><div class="line"><a name="l02220"></a><span class="lineno"> 2220</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab0bb4b8a290f1c8acd3c3a0d9a6e9783">CreateNetworkFromNetParameter</a>(netParam, inputShapes, requestedOutputs);</div><div class="line"><a name="l02221"></a><span class="lineno"> 2221</span> }</div><div class="line"><a name="l02222"></a><span class="lineno"> 2222</span> </div><div class="line"><a name="l02223"></a><span class="lineno"> 2223</span> <span class="comment">// Note: can move to CaffeParser when/if we optimise the text/string format</span></div><div class="line"><a name="l02224"></a><span class="lineno"> 2224</span> <span class="comment">// to load on a layer by layer basis</span></div><div class="line"><a name="l02225"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab0bb4b8a290f1c8acd3c3a0d9a6e9783"> 2225</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab0bb4b8a290f1c8acd3c3a0d9a6e9783">ICaffeParser::CaffeParserImpl::CreateNetworkFromNetParameter</a>(NetParameter& netParam,</div><div class="line"><a name="l02226"></a><span class="lineno"> 2226</span>  <span class="keyword">const</span> std::map<std::string, armnn::TensorShape>& inputShapes,</div><div class="line"><a name="l02227"></a><span class="lineno"> 2227</span>  <span class="keyword">const</span> std::vector<std::string>& requestedOutputs)</div><div class="line"><a name="l02228"></a><span class="lineno"> 2228</span> {</div><div class="line"><a name="l02229"></a><span class="lineno"> 2229</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac62e2558c14e01605f2b4e1e21cdd1e8">m_NetworkInputsBindingInfo</a>.clear();</div><div class="line"><a name="l02230"></a><span class="lineno"> 2230</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a62d6d6cba9ed0d3ad63fffb40aec86b5">m_NetworkOutputsBindingInfo</a>.clear();</div><div class="line"><a name="l02231"></a><span class="lineno"> 2231</span> </div><div class="line"><a name="l02232"></a><span class="lineno"> 2232</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a> = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l02233"></a><span class="lineno"> 2233</span> </div><div class="line"><a name="l02234"></a><span class="lineno"> 2234</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a76ea67f3f7d1d5835c5a92b65dc0854c">m_InputShapes</a> = inputShapes;</div><div class="line"><a name="l02235"></a><span class="lineno"> 2235</span>  <span class="keywordflow">if</span> (requestedOutputs.size() == 0)</div><div class="line"><a name="l02236"></a><span class="lineno"> 2236</span>  {</div><div class="line"><a name="l02237"></a><span class="lineno"> 2237</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"requestedOutputs must have at least one entry"</span>);</div><div class="line"><a name="l02238"></a><span class="lineno"> 2238</span>  }</div><div class="line"><a name="l02239"></a><span class="lineno"> 2239</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a86cb41745deebd9b0ccf157d97d4d9ca">m_RequestedOutputs</a> = requestedOutputs;</div><div class="line"><a name="l02240"></a><span class="lineno"> 2240</span> </div><div class="line"><a name="l02241"></a><span class="lineno"> 2241</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l02242"></a><span class="lineno"> 2242</span>  {</div><div class="line"><a name="l02243"></a><span class="lineno"> 2243</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c5eed5d48d21a8b7e3bcd2cab519217">LoadNetParam</a>(netParam);</div><div class="line"><a name="l02244"></a><span class="lineno"> 2244</span>  }</div><div class="line"><a name="l02245"></a><span class="lineno"> 2245</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>& e)</div><div class="line"><a name="l02246"></a><span class="lineno"> 2246</span>  {</div><div class="line"><a name="l02247"></a><span class="lineno"> 2247</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ae89a123aad1c66a76c398b7af216aae4">Cleanup</a>();</div><div class="line"><a name="l02248"></a><span class="lineno"> 2248</span>  <span class="keywordflow">throw</span> e;</div><div class="line"><a name="l02249"></a><span class="lineno"> 2249</span>  }</div><div class="line"><a name="l02250"></a><span class="lineno"> 2250</span> </div><div class="line"><a name="l02251"></a><span class="lineno"> 2251</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ae89a123aad1c66a76c398b7af216aae4">Cleanup</a>();</div><div class="line"><a name="l02252"></a><span class="lineno"> 2252</span> </div><div class="line"><a name="l02253"></a><span class="lineno"> 2253</span>  <span class="keywordflow">return</span> move(<a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">m_Network</a>);</div><div class="line"><a name="l02254"></a><span class="lineno"> 2254</span> }</div><div class="line"><a name="l02255"></a><span class="lineno"> 2255</span> </div><div class="line"><a name="l02256"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa09a8bb02eed50715082d8b7fccd2f8d"> 2256</a></span> <span class="keyword">const</span> std::string <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa09a8bb02eed50715082d8b7fccd2f8d">ICaffeParser::CaffeParserImpl::GetVersion</a>()</div><div class="line"><a name="l02257"></a><span class="lineno"> 2257</span> {</div><div class="line"><a name="l02258"></a><span class="lineno"> 2258</span>  <span class="keywordflow">return</span> <a class="code" href="include_2armnn_caffe_parser_2_version_8hpp.xhtml#af3d53d50a9ddd493c4b40e44a82e2b44">CAFFE_PARSER_VERSION</a>;</div><div class="line"><a name="l02259"></a><span class="lineno"> 2259</span> }</div><div class="line"><a name="l02260"></a><span class="lineno"> 2260</span> </div><div class="line"><a name="l02261"></a><span class="lineno"><a class="line" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ae89a123aad1c66a76c398b7af216aae4"> 2261</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ae89a123aad1c66a76c398b7af216aae4">ICaffeParser::CaffeParserImpl::Cleanup</a>() {</div><div class="line"><a name="l02262"></a><span class="lineno"> 2262</span>  <span class="comment">// cleanup, in case we reuse this parser</span></div><div class="line"><a name="l02263"></a><span class="lineno"> 2263</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a76ea67f3f7d1d5835c5a92b65dc0854c">m_InputShapes</a>.clear();</div><div class="line"><a name="l02264"></a><span class="lineno"> 2264</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a86cb41745deebd9b0ccf157d97d4d9ca">m_RequestedOutputs</a>.clear();</div><div class="line"><a name="l02265"></a><span class="lineno"> 2265</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c99d40a72e6f0c6e4ad92d21e44edca">m_ArmnnOutputSlotForCaffeTop</a>.clear();</div><div class="line"><a name="l02266"></a><span class="lineno"> 2266</span>  <span class="comment">// NOTE: when we get the text/string format</span></div><div class="line"><a name="l02267"></a><span class="lineno"> 2267</span>  <span class="comment">// optimised for memory then this data structure can</span></div><div class="line"><a name="l02268"></a><span class="lineno"> 2268</span>  <span class="comment">// also move to the CaffeParser class</span></div><div class="line"><a name="l02269"></a><span class="lineno"> 2269</span>  <a class="code" href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a1424f1bfbfc81d317b51053bbb315ef1">m_CaffeLayersByTopName</a>.clear();</div><div class="line"><a name="l02270"></a><span class="lineno"> 2270</span> }</div><div class="line"><a name="l02271"></a><span class="lineno"> 2271</span> </div><div class="line"><a name="l02272"></a><span class="lineno"> 2272</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#l00444">Descriptors.hpp:444</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00454">Descriptors.hpp:454</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_ac2dac3b61c94de52093616be4ab17f8d"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">armnn::IConnectableLayer::GetNumOutputSlots</a></div><div class="ttdeci">virtual unsigned int GetNumOutputSlots() const =0</div><div class="ttdoc">Returns the number of connectable output slots. </div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a3311e9dc3436fe83ef22c5f530fd3234"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a3311e9dc3436fe83ef22c5f530fd3234">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseSplitLayer</a></div><div class="ttdeci">void ParseSplitLayer(const caffe::LayerParameter &layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01881">CaffeParser.cpp:1881</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_adcb87456482d5df17ef09eca1a808091"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#adcb87456482d5df17ef09eca1a808091">armnnCaffeParser::ICaffeParser::CaffeParserImpl::AddConvLayerWithDepthwiseConv</a></div><div class="ttdeci">void AddConvLayerWithDepthwiseConv(const caffe::LayerParameter &layerParam, const armnn::Convolution2dDescriptor &desc, unsigned int kernelW, unsigned int kernelH)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00825">CaffeParser.cpp:825</a></div></div> +<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml">armnn::ViewsDescriptor</a></div><div class="ttdoc">A ViewsDescriptor for the SplitterLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00206">Descriptors.hpp:206</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00062">INetwork.hpp:62</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a77f532a6a82afeb6e79957726a9517a5"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77f532a6a82afeb6e79957726a9517a5">armnnCaffeParser::ICaffeParser::CaffeParserImpl::AddDeconvLayerWithSplits</a></div><div class="ttdeci">void AddDeconvLayerWithSplits(const caffe::LayerParameter &layerParam, const armnn::TransposeConvolution2dDescriptor &desc, unsigned int kernelW, unsigned int kernelH)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00637">CaffeParser.cpp:637</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Pooling2dDescriptor::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#l00371">Descriptors.hpp:371</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a76ea67f3f7d1d5835c5a92b65dc0854c"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a76ea67f3f7d1d5835c5a92b65dc0854c">armnnCaffeParser::ICaffeParser::CaffeParserImpl::m_InputShapes</a></div><div class="ttdeci">std::map< std::string, armnn::TensorShape > m_InputShapes</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00144">CaffeParser.hpp:144</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a8526ea7cf860d8e7f8340e9f9354f9f0"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">armnn::NormalizationDescriptor::m_K</a></div><div class="ttdeci">float m_K</div><div class="ttdoc">Kappa value used for the across channel normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00601">Descriptors.hpp:601</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a7785119cfebd2b02ba3be888965e52ba"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a7785119cfebd2b02ba3be888965e52ba">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseLRNLayer</a></div><div class="ttdeci">void ParseLRNLayer(const caffe::LayerParameter &layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01468">CaffeParser.cpp:1468</a></div></div> +<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml_a214c3636fdf0ea5bac8edb42d0e6c7f0"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml#a214c3636fdf0ea5bac8edb42d0e6c7f0">armnn::SoftmaxDescriptor::m_Axis</a></div><div class="ttdeci">int m_Axis</div><div class="ttdoc">Scalar, defaulted to the last index (-1), specifying the dimension the activation will be performed o...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00154">Descriptors.hpp:154</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">armnn::TransposeConvolution2dDescriptor</a></div><div class="ttdoc">A TransposeConvolution2dDescriptor for the TransposeConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01213">Descriptors.hpp:1213</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00187">Tensor.hpp:187</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a4b1fdcb1985af12dd1848a9ffa5d3271"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a4b1fdcb1985af12dd1848a9ffa5d3271">armnnCaffeParser::ICaffeParser::CaffeParserImpl::GetNetworkOutputBindingInfo</a></div><div class="ttdeci">BindingPointInfo GetNetworkOutputBindingInfo(const std::string &name) const</div><div class="ttdoc">Retrieves binding info (layer id and tensor info) for the network output identified by the given laye...</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00327">CaffeParser.cpp:327</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Pooling2dDescriptor::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#l00365">Descriptors.hpp:365</a></div></div> +<div class="ttc" id="_utils_8hpp_xhtml"><div class="ttname"><a href="_utils_8hpp.xhtml">Utils.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional</a></div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00270">Optional.hpp:270</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a7e393f41f2330006fdf00f2840c6dd28"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a7e393f41f2330006fdf00f2840c6dd28">armnnCaffeParser::ICaffeParser::CaffeParserImpl::CreateNetworkFromString</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromString(const char *protoText, const std::map< std::string, armnn::TensorShape > &inputShapes, const std::vector< std::string > &requestedOutputs)</div><div class="ttdoc">Creates the network directly from protobuf text in a string. Useful for debugging/testing. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l02171">CaffeParser.cpp:2171</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="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_ac915fb2df2772be3179e97b1e8287a2d"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac915fb2df2772be3179e97b1e8287a2d">armnnCaffeParser::ICaffeParser::CaffeParserImpl::CreateNetworkFromTextFile</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromTextFile(const char *graphFile, const std::map< std::string, armnn::TensorShape > &inputShapes, const std::vector< std::string > &requestedOutputs)</div><div class="ttdoc">Create the network from a protobuf text file on disk. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l02139">CaffeParser.cpp:2139</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_a281fcaec86e17c97f7b8402633f6b55a"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">armnn::FullyConnectedDescriptor::m_TransposeWeightMatrix</a></div><div class="ttdeci">bool m_TransposeWeightMatrix</div><div class="ttdoc">Enable/disable transpose weight matrix. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00404">Descriptors.hpp:404</a></div></div> +<div class="ttc" id="namespacegoogle_1_1protobuf_1_1io_xhtml"><div class="ttname"><a href="namespacegoogle_1_1protobuf_1_1io.xhtml">io</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a95799625a4aae0ed73838cbfa3530c1b"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a95799625a4aae0ed73838cbfa3530c1b">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseScaleLayer</a></div><div class="ttdeci">void ParseScaleLayer(const caffe::LayerParameter &layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01831">CaffeParser.cpp:1831</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_afb7e4da478bab76261963479baad5788"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#afb7e4da478bab76261963479baad5788">armnnCaffeParser::ICaffeParser::CaffeParserImpl::GetBindingInfo</a></div><div class="ttdeci">static std::pair< armnn::LayerBindingId, armnn::TensorInfo > GetBindingInfo(const std::string &layerName, const char *bindingPointDesc, const std::unordered_map< std::string, BindingPointInfo > &bindingInfos)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00332">CaffeParser.cpp:332</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a6d8fb685cc1ff224f25aa127fcf62c86"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">armnn::Pooling2dDescriptor::m_PoolWidth</a></div><div class="ttdeci">uint32_t m_PoolWidth</div><div class="ttdoc">Pooling width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00373">Descriptors.hpp:373</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a></div><div class="ttdoc">A Convolution2dDescriptor for the Convolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00408">Descriptors.hpp:408</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a174279be57d7596eeb04c6b7f7510f99"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">armnn::NormalizationDescriptor::m_Alpha</a></div><div class="ttdeci">float m_Alpha</div><div class="ttdoc">Alpha value for the normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00597">Descriptors.hpp:597</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::DepthwiseConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00490">Descriptors.hpp:490</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::TransposeConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01254">Descriptors.hpp:1254</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_aa4c22681675806fa2c5fbf403d49c628"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa4c22681675806fa2c5fbf403d49c628">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseDropoutLayer</a></div><div class="ttdeci">void ParseDropoutLayer(const caffe::LayerParameter &layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01900">CaffeParser.cpp:1900</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_caffe_parser_xhtml_a82fc903eb5648250a6d82371a94772a3"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_caffe_parser.xhtml#a82fc903eb5648250a6d82371a94772a3">armnnCaffeParser::CaffeParser::CaffeParser</a></div><div class="ttdeci">CaffeParser()</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00316">CaffeParser.cpp:316</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a0c98e07875a82c71c65bbb53eb347561"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0c98e07875a82c71c65bbb53eb347561">armnnCaffeParser::ICaffeParser::CaffeParserImpl::TrackOutputBinding</a></div><div class="ttdeci">void TrackOutputBinding(armnn::IConnectableLayer *layer, armnn::LayerBindingId id, const armnn::TensorInfo &tensorInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01923">CaffeParser.cpp:1923</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml_a11c821c7524251004a72ed13c510853c"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">armnn::BatchNormalizationDescriptor::m_Eps</a></div><div class="ttdeci">float m_Eps</div><div class="ttdoc">Value to add to the variance. Used to avoid dividing by zero. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00639">Descriptors.hpp:639</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a8c29d6ea9b4186d69aad5961c910939c"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">armnn::Pooling2dDescriptor::m_PaddingMethod</a></div><div class="ttdeci">PaddingMethod m_PaddingMethod</div><div class="ttdoc">The padding method to be used. (Exclude, IgnoreValue). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00383">Descriptors.hpp:383</a></div></div> +<div class="ttc" id="structarmnn_1_1_arg_min_max_descriptor_xhtml_ab1ae6f520bb1a4da191a0ae907477f23"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_descriptor.xhtml#ab1ae6f520bb1a4da191a0ae907477f23">armnn::ArgMinMaxDescriptor::m_Function</a></div><div class="ttdeci">ArgMinMaxFunction m_Function</div><div class="ttdoc">Specify if the function is to find Min or Max. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00070">Descriptors.hpp:70</a></div></div> +<div class="ttc" id="classarmnn_1_1_file_not_found_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_file_not_found_exception.xhtml">armnn::FileNotFoundException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00086">Exceptions.hpp:86</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a86cb41745deebd9b0ccf157d97d4d9ca"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a86cb41745deebd9b0ccf157d97d4d9ca">armnnCaffeParser::ICaffeParser::CaffeParserImpl::m_RequestedOutputs</a></div><div class="ttdeci">std::vector< std::string > m_RequestedOutputs</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00149">CaffeParser.hpp:149</a></div></div> +<div class="ttc" id="include_2armnn_caffe_parser_2_version_8hpp_xhtml_af3d53d50a9ddd493c4b40e44a82e2b44"><div class="ttname"><a href="include_2armnn_caffe_parser_2_version_8hpp.xhtml#af3d53d50a9ddd493c4b40e44a82e2b44">CAFFE_PARSER_VERSION</a></div><div class="ttdeci">#define CAFFE_PARSER_VERSION</div><div class="ttdoc">CAFFE_PARSER_VERSION: "X.Y.Z" where: X = Major version number Y = Minor version number Z = Patch vers...</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_caffe_parser_2_version_8hpp_source.xhtml#l00025">Version.hpp:25</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Pooling2dDescriptor::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#l00369">Descriptors.hpp:369</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#l00440">Descriptors.hpp:440</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_transpose_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::TransposeConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01248">Descriptors.hpp:1248</a></div></div> +<div class="ttc" id="namespacearmnn_caffe_parser_xhtml"><div class="ttname"><a href="namespacearmnn_caffe_parser.xhtml">armnnCaffeParser</a></div><div class="ttdoc">Caffe networks are loaded from protobuf files (binary or text) using the protobuf library and the gen...</div><div class="ttdef"><b>Definition:</b> <a href="_i_caffe_parser_8hpp_source.xhtml#l00016">ICaffeParser.hpp:16</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_aa09a8bb02eed50715082d8b7fccd2f8d"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa09a8bb02eed50715082d8b7fccd2f8d">armnnCaffeParser::ICaffeParser::CaffeParserImpl::GetVersion</a></div><div class="ttdeci">static const std::string GetVersion()</div><div class="ttdoc">Retrieve version in X.Y.Z form. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l02256">CaffeParser.cpp:2256</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_aa0f0ff1cae05c1a0d7cc11b498714312"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#aa0f0ff1cae05c1a0d7cc11b498714312">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseConcatLayer</a></div><div class="ttdeci">void ParseConcatLayer(const caffe::LayerParameter &layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01708">CaffeParser.cpp:1708</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#l00452">Descriptors.hpp:452</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a9c5eed5d48d21a8b7e3bcd2cab519217"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c5eed5d48d21a8b7e3bcd2cab519217">armnnCaffeParser::ICaffeParser::CaffeParserImpl::LoadNetParam</a></div><div class="ttdeci">void LoadNetParam(caffe::NetParameter &netParameter)</div><div class="ttdoc">does the actual conversion from caffe::NetParameter to armnn::INetwork </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l02043">CaffeParser.cpp:2043</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a5f5e6255b21fdf458d3733bbdcdc4af5"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5f5e6255b21fdf458d3733bbdcdc4af5">armnnCaffeParser::ICaffeParser::CaffeParserImpl::TrackBindingPoint</a></div><div class="ttdeci">static void TrackBindingPoint(armnn::IConnectableLayer *layer, armnn::LayerBindingId id, const armnn::TensorInfo &tensorInfo, const char *bindingPointDesc, std::unordered_map< std::string, BindingPointInfo > &nameToBindingInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01930">CaffeParser.cpp:1930</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Pooling2dDescriptor::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#l00377">Descriptors.hpp:377</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_ab0bb4b8a290f1c8acd3c3a0d9a6e9783"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab0bb4b8a290f1c8acd3c3a0d9a6e9783">armnnCaffeParser::ICaffeParser::CaffeParserImpl::CreateNetworkFromNetParameter</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromNetParameter(caffe::NetParameter &netParam, const std::map< std::string, armnn::TensorShape > &inputShapes, const std::vector< std::string > &requestedOutputs)</div><div class="ttdoc">Parses a NetParameter loaded into memory from one of the other CreateNetwork*. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l02225">CaffeParser.cpp:2225</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ab8cf8f9fb6792e654c2d8d8382f6f01b"><div class="ttname"><a href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a></div><div class="ttdeci">int LayerBindingId</div><div class="ttdoc">Type of identifiers for bindable layers (inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00210">Types.hpp:210</a></div></div> +<div class="ttc" id="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="_numeric_cast_8hpp_xhtml"><div class="ttname"><a href="_numeric_cast_8hpp.xhtml">NumericCast.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a05945f080edf694b631960728b87aadb"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a05945f080edf694b631960728b87aadb">armnn::NormalizationDescriptor::m_NormMethodType</a></div><div class="ttdeci">NormalizationAlgorithmMethod m_NormMethodType</div><div class="ttdoc">Normalization method algorithm to use (LocalBrightness, LocalContrast). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00593">Descriptors.hpp:593</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_xhtml_add49602ee9cd2bd16c1c4ccd25555d8e"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml#add49602ee9cd2bd16c1c4ccd25555d8e">armnnCaffeParser::ICaffeParser::CreateNetworkFromTextFile</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromTextFile(const char *graphFile, const std::map< std::string, armnn::TensorShape > &inputShapes, const std::vector< std::string > &requestedOutputs)</div><div class="ttdoc">Create the network from a protobuf text file on the disk. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00085">CaffeParser.cpp:85</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_abe8889e8150beef5fd204b2d87b49298"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">armnn::TensorInfo::SetShape</a></div><div class="ttdeci">void SetShape(const TensorShape &newShape)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00189">Tensor.hpp:189</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a5ce905b7412e68d588e08f4afc333aac"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5ce905b7412e68d588e08f4afc333aac">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseArgmaxLayer</a></div><div class="ttdeci">void ParseArgmaxLayer(const caffe::LayerParameter &layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01382">CaffeParser.cpp:1382</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a62d6d6cba9ed0d3ad63fffb40aec86b5"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a62d6d6cba9ed0d3ad63fffb40aec86b5">armnnCaffeParser::ICaffeParser::CaffeParserImpl::m_NetworkOutputsBindingInfo</a></div><div class="ttdeci">std::unordered_map< std::string, BindingPointInfo > m_NetworkOutputsBindingInfo</div><div class="ttdoc">maps output layer names to their corresponding ids and tensor infos </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00140">CaffeParser.hpp:140</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a9fea304829fe514d664de515ca5c3918"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9fea304829fe514d664de515ca5c3918">armnnCaffeParser::ICaffeParser::CaffeParserImpl::AddConvLayerWithSplits</a></div><div class="ttdeci">void AddConvLayerWithSplits(const caffe::LayerParameter &layerParam, const armnn::Convolution2dDescriptor &desc, unsigned int kernelW, unsigned int kernelH)</div><div class="ttdoc">ParseConv may use these helpers depending on the group parameter. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00447">CaffeParser.cpp:447</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_xhtml"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser.xhtml">armnnCaffeParser::ICaffeParser</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_caffe_parser_8hpp_source.xhtml#l00024">ICaffeParser.hpp:24</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a5699e8606c37d18c03910b242cd1b010"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">armnn::Pooling2dDescriptor::m_PoolHeight</a></div><div class="ttdeci">uint32_t m_PoolHeight</div><div class="ttdoc">Pooling height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00375">Descriptors.hpp:375</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a8b053a6c449d0814cc831c916c126668"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a8b053a6c449d0814cc831c916c126668">armnnCaffeParser::ICaffeParser::CaffeParserImpl::GetNetworkInputBindingInfo</a></div><div class="ttdeci">BindingPointInfo GetNetworkInputBindingInfo(const std::string &name) const</div><div class="ttdoc">Retrieves binding info (layer id and tensor info) for the network input identified by the given layer...</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00322">CaffeParser.cpp:322</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#l00442">Descriptors.hpp:442</a></div></div> +<div class="ttc" id="namespacestd_xhtml"><div class="ttname"><a href="namespacestd.xhtml">std</a></div><div class="ttdef"><b>Definition:</b> <a href="_backend_id_8hpp_source.xhtml#l00147">BackendId.hpp:147</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#l00446">Descriptors.hpp:446</a></div></div> +<div class="ttc" id="_verification_helpers_8hpp_xhtml"><div class="ttname"><a href="_verification_helpers_8hpp.xhtml">VerificationHelpers.hpp</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a9650b8810d4e6734b255ca25d495fe06"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9650b8810d4e6734b255ca25d495fe06">armnnCaffeParser::ICaffeParser::CaffeParserImpl::GetArmnnOutputSlotForCaffeTop</a></div><div class="ttdeci">armnn::IOutputSlot & GetArmnnOutputSlotForCaffeTop(const std::string &caffeTopName) const</div><div class="ttdoc">Retrieves the Armnn IOutputSlot representing the given Caffe top. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01952">CaffeParser.cpp:1952</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Pooling2dDescriptor::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#l00367">Descriptors.hpp:367</a></div></div> +<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml_aae0893695f5803a3517985c7cb1ccb2e"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">armnn::ViewsDescriptor::SetViewSize</a></div><div class="ttdeci">Status SetViewSize(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the size of the views. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00315">Descriptors.cpp:315</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233"><div class="ttname"><a href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">armnn::ArgMinMaxFunction::Max</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a></div><div class="ttdoc">An output connection slot for a layer. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00038">INetwork.hpp:38</a></div></div> +<div class="ttc" id="structarmnn_1_1_arg_min_max_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_descriptor.xhtml">armnn::ArgMinMaxDescriptor</a></div><div class="ttdoc">An ArgMinMaxDescriptor for ArgMinMaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00056">Descriptors.hpp:56</a></div></div> +<div class="ttc" id="_caffe_parser_8hpp_xhtml"><div class="ttname"><a href="_caffe_parser_8hpp.xhtml">CaffeParser.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a></div><div class="ttdoc">An OriginsDescriptor for the ConcatLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00163">Descriptors.hpp:163</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a></div><div class="ttdoc">A FullyConnectedDescriptor for the FullyConnectedLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00389">Descriptors.hpp:389</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a6fb0cd80a09cf767309175fb138d203b"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a6fb0cd80a09cf767309175fb138d203b">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ms_CaffeLayerNameToParsingFunctions</a></div><div class="ttdeci">static const std::map< std::string, OperationParsingFunction > ms_CaffeLayerNameToParsingFunctions</div><div class="ttdoc">Maps Caffe layer names to parsing member functions. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00134">CaffeParser.hpp:134</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::FullyConnectedDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00402">Descriptors.hpp:402</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_ac62e2558c14e01605f2b4e1e21cdd1e8"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac62e2558c14e01605f2b4e1e21cdd1e8">armnnCaffeParser::ICaffeParser::CaffeParserImpl::m_NetworkInputsBindingInfo</a></div><div class="ttdeci">std::unordered_map< std::string, BindingPointInfo > m_NetworkInputsBindingInfo</div><div class="ttdoc">maps input layer names to their corresponding ids and tensor infos </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00137">CaffeParser.hpp:137</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="_record_by_record_caffe_parser_8hpp_xhtml"><div class="ttname"><a href="_record_by_record_caffe_parser_8hpp.xhtml">RecordByRecordCaffeParser.hpp</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_caffe_parser_xhtml_afb0edadd00c78430efbdc02844ef379a"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_caffe_parser.xhtml#afb0edadd00c78430efbdc02844ef379a">armnnCaffeParser::CaffeParser::CreateNetworkFromBinaryFile</a></div><div class="ttdeci">virtual armnn::INetworkPtr CreateNetworkFromBinaryFile(const char *graphFile, const std::map< std::string, armnn::TensorShape > &inputShapes, const std::vector< std::string > &requestedOutputs) override</div><div class="ttdoc">Create the network from a protobuf binary file on disk. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l02189">CaffeParser.cpp:2189</a></div></div> +<div class="ttc" id="_assert_8hpp_xhtml_a5698be69cbd5dfe6c28fcd9867e8cbed"><div class="ttname"><a href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a></div><div class="ttdeci">#define ARMNN_ASSERT(COND)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00014">Assert.hpp:14</a></div></div> +<div class="ttc" id="_graph_topological_sort_8hpp_xhtml"><div class="ttname"><a href="_graph_topological_sort_8hpp.xhtml">GraphTopologicalSort.hpp</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a8449e66d395c0525561e3c67b100bafe"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a8449e66d395c0525561e3c67b100bafe">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseReluLayer</a></div><div class="ttdeci">void ParseReluLayer(const caffe::LayerParameter &layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01442">CaffeParser.cpp:1442</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a1c0594bf03dfbb44029465d3466127b3"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a1c0594bf03dfbb44029465d3466127b3">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseSoftmaxLayer</a></div><div class="ttdeci">void ParseSoftmaxLayer(const caffe::LayerParameter &layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01641">CaffeParser.cpp:1641</a></div></div> +<div class="ttc" id="_caffe_parser_8cpp_xhtml_a69f4a692d0095f6b19b0cd99cd75e465"><div class="ttname"><a href="_caffe_parser_8cpp.xhtml#a69f4a692d0095f6b19b0cd99cd75e465">GET_OPTIONAL_WITH_FALLBACK</a></div><div class="ttdeci">#define GET_OPTIONAL_WITH_FALLBACK(PARAM, PARAM_TYPE, OPTIONAL_VALUE, FALLBACK_VALUE, VALUE_TYPE, DEFAULT_VALUE)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00257">CaffeParser.cpp:257</a></div></div> +<div class="ttc" id="namespacearmnn_caffe_parser_xhtml_a33c76910f1980ffaa41c22e0151cce2a"><div class="ttname"><a href="namespacearmnn_caffe_parser.xhtml#a33c76910f1980ffaa41c22e0151cce2a">armnnCaffeParser::ICaffeParserPtr</a></div><div class="ttdeci">std::unique_ptr< ICaffeParser, void(*)(ICaffeParser *parser)> ICaffeParserPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_caffe_parser_8hpp_source.xhtml#l00022">ICaffeParser.hpp:22</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a></div><div class="ttdoc">An ActivationDescriptor for the ActivationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00025">Descriptors.hpp:25</a></div></div> +<div class="ttc" id="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="_exceptions_8hpp_xhtml_aa3be76aec4ce713822a5ea1ecbb7bc61"><div class="ttname"><a href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a></div><div class="ttdeci">#define CHECK_LOCATION()</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00197">Exceptions.hpp:197</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a940483591995bb812cfcd1595dba83c3"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a940483591995bb812cfcd1595dba83c3">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseBatchNormLayer</a></div><div class="ttdeci">void ParseBatchNormLayer(const caffe::LayerParameter &layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01767">CaffeParser.cpp:1767</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a34f6df4b84de1e269bcf02efeecc3892"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a34f6df4b84de1e269bcf02efeecc3892">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseInnerProductLayer</a></div><div class="ttdeci">void ParseInnerProductLayer(const caffe::LayerParameter &layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01567">CaffeParser.cpp:1567</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#l00448">Descriptors.hpp:448</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a89631aa06b5c628c46674c202b40dbc5"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a89631aa06b5c628c46674c202b40dbc5">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ResolveInPlaceLayers</a></div><div class="ttdeci">void ResolveInPlaceLayers(caffe::NetParameter &netParameter)</div><div class="ttdoc">Modifies the Caffe network to replace "in-place" layers (whose top() and bottom() are both the same) ...</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01987">CaffeParser.cpp:1987</a></div></div> +<div class="ttc" id="namespacecaffe_xhtml"><div class="ttname"><a href="namespacecaffe.xhtml">caffe</a></div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00016">CaffeParser.hpp:16</a></div></div> +<div class="ttc" id="_i_network_8hpp_xhtml"><div class="ttname"><a href="_i_network_8hpp.xhtml">INetwork.hpp</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a0767b1c7ee9cbd014fd97c701a954caa"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a0767b1c7ee9cbd014fd97c701a954caa">armnnCaffeParser::ICaffeParser::CaffeParserImpl::SetArmnnOutputSlotForCaffeTop</a></div><div class="ttdeci">void SetArmnnOutputSlotForCaffeTop(const std::string &caffeTopName, armnn::IOutputSlot &armnnOutputSlot)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01968">CaffeParser.cpp:1968</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_afe1f0f09d49ad2befc01f8789187b7dd"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">armnn::NormalizationDescriptor::m_NormChannelType</a></div><div class="ttdeci">NormalizationAlgorithmChannel m_NormChannelType</div><div class="ttdoc">Normalization channel algorithm to use (Across, Within). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00591">Descriptors.hpp:591</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml_a017b2990003a014234f13e999dc7c689"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">armnn::ActivationDescriptor::m_A</a></div><div class="ttdeci">float m_A</div><div class="ttdoc">Alpha upper bound value used by the activation functions. (BoundedReLu, Linear, TanH, Elu). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00050">Descriptors.hpp:50</a></div></div> +<div class="ttc" id="structarmnn_1_1_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#l00450">Descriptors.hpp:450</a></div></div> +<div class="ttc" id="_assert_8hpp_xhtml"><div class="ttname"><a href="_assert_8hpp.xhtml">Assert.hpp</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_ab3329e4bcd8e42cd314f84c8260b06ad"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ab3329e4bcd8e42cd314f84c8260b06ad">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParsePoolingLayer</a></div><div class="ttdeci">void ParsePoolingLayer(const caffe::LayerParameter &layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01270">CaffeParser.cpp:1270</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::TransposeConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01242">Descriptors.hpp:1242</a></div></div> +<div class="ttc" id="structarmnn_1_1_empty_optional_xhtml"><div class="ttname"><a href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a></div><div class="ttdoc">EmptyOptional is used to initialize the Optional class in case we want to have default value for an O...</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00032">Optional.hpp:32</a></div></div> +<div class="ttc" id="classarmnn_1_1_parse_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_parse_exception.xhtml">armnn::ParseException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00092">Exceptions.hpp:92</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a280670a263dc4fd40491f6d0a2737f44"><div class="ttname"><a href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">armnn::BindingPointInfo</a></div><div class="ttdeci">std::pair< armnn::LayerBindingId, armnn::TensorInfo > BindingPointInfo</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00261">Tensor.hpp:261</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::TransposeConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01250">Descriptors.hpp:1250</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a0031997bf43bd2747656c31e4977793a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">armnn::Pooling2dDescriptor::m_PoolType</a></div><div class="ttdeci">PoolingAlgorithm m_PoolType</div><div class="ttdoc">The pooling algorithm to use (Max. Average, L2). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00363">Descriptors.hpp:363</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::TransposeConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01252">Descriptors.hpp:1252</a></div></div> +<div class="ttc" id="_descriptors_8hpp_xhtml"><div class="ttname"><a href="_descriptors_8hpp.xhtml">Descriptors.hpp</a></div></div> +<div class="ttc" id="_exceptions_8hpp_xhtml"><div class="ttname"><a href="_exceptions_8hpp.xhtml">Exceptions.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_affb5b68b3eba3ed45a06c7cde7781962"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#affb5b68b3eba3ed45a06c7cde7781962">armnn::Pooling2dDescriptor::m_OutputShapeRounding</a></div><div class="ttdeci">OutputShapeRounding m_OutputShapeRounding</div><div class="ttdoc">The rounding method for the output shape. (Floor, Ceiling). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00381">Descriptors.hpp:381</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_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a4ddfa6cb51f928114a8151b8f455f115"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a4ddfa6cb51f928114a8151b8f455f115">armnnCaffeParser::ICaffeParser::CaffeParserImpl::CaffeParserImpl</a></div><div class="ttdeci">CaffeParserImpl()</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00310">CaffeParser.cpp:310</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a375ca3cff9f1b005d1412dc5f3cf5b6e"><div class="ttname"><a href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t< std::is_unsigned< Source >::value &&std::is_unsigned< Dest >::value, Dest > numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00035">NumericCast.hpp:35</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_acee63cd08da47910fc166a1990988fa8"><div class="ttname"><a href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo GetTensorInfo(unsigned int numberOfBatches, unsigned int numberOfChannels, unsigned int height, unsigned int width, const armnn::DataLayout dataLayout, const armnn::DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.xhtml#l00038">TensorUtils.cpp:38</a></div></div> +<div class="ttc" id="include_2armnn_caffe_parser_2_version_8hpp_xhtml"><div class="ttname"><a href="include_2armnn_caffe_parser_2_version_8hpp.xhtml">Version.hpp</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a99a846a21b3a6ec97cc1d4344b91df36"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a99a846a21b3a6ec97cc1d4344b91df36">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseEltwiseLayer</a></div><div class="ttdeci">void ParseEltwiseLayer(const caffe::LayerParameter &layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01663">CaffeParser.cpp:1663</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::TransposeConvolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01244">Descriptors.hpp:1244</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233">armnn::PoolingAlgorithm::Max</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="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a2db3ae8d422f17d455e0ba0cb6291d2a"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2db3ae8d422f17d455e0ba0cb6291d2a">armnnCaffeParser::ICaffeParser::CaffeParserImpl::m_Network</a></div><div class="ttdeci">armnn::INetworkPtr m_Network</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00142">CaffeParser.hpp:142</a></div></div> +<div class="ttc" id="structarmnn_1_1_arg_min_max_descriptor_xhtml_a214c3636fdf0ea5bac8edb42d0e6c7f0"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_descriptor.xhtml#a214c3636fdf0ea5bac8edb42d0e6c7f0">armnn::ArgMinMaxDescriptor::m_Axis</a></div><div class="ttdeci">int m_Axis</div><div class="ttdoc">Axis to reduce across the input tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00072">Descriptors.hpp:72</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a9c99d40a72e6f0c6e4ad92d21e44edca"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a9c99d40a72e6f0c6e4ad92d21e44edca">armnnCaffeParser::ICaffeParser::CaffeParserImpl::m_ArmnnOutputSlotForCaffeTop</a></div><div class="ttdeci">std::unordered_map< std::string, armnn::IOutputSlot * > m_ArmnnOutputSlotForCaffeTop</div><div class="ttdoc">As we add armnn layers we store the armnn IOutputSlot which corresponds to the Caffe tops...</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00147">CaffeParser.hpp:147</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_afcc1c3a20bd2860e0ddd21674389246f"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">armnn::IConnectableLayer::GetName</a></div><div class="ttdeci">virtual const char * GetName() const =0</div><div class="ttdoc">Returns the name of the layer. </div></div> +<div class="ttc" id="_caffe_parser_8cpp_xhtml_a7553d91772274ed9b103824bbf7f75a5"><div class="ttname"><a href="_caffe_parser_8cpp.xhtml#a7553d91772274ed9b103824bbf7f75a5">GET_OPTIONAL_WITH_VECTOR_FALLBACK</a></div><div class="ttdeci">#define GET_OPTIONAL_WITH_VECTOR_FALLBACK(PARAM, PARAM_TYPE, OPTIONAL_VALUE, FALLBACK_VECTOR, VALUE_TYPE, DEFAULT_VALUE)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00225">CaffeParser.cpp:225</a></div></div> +<div class="ttc" id="_test_utils_8cpp_xhtml_a0b295acb179f15eb3fb862b32008f782"><div class="ttname"><a href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a></div><div class="ttdeci">void Connect(armnn::IConnectableLayer *from, armnn::IConnectableLayer *to, const armnn::TensorInfo &tensorInfo, unsigned int fromIndex, unsigned int toIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8cpp_source.xhtml#l00012">TestUtils.cpp:12</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_output_slot_xhtml_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &destination)=0</div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a2a1112c66d08e3760ecccf39c7854a90"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2a1112c66d08e3760ecccf39c7854a90">armnnCaffeParser::ICaffeParser::CaffeParserImpl::TrackInputBinding</a></div><div class="ttdeci">void TrackInputBinding(armnn::IConnectableLayer *layer, armnn::LayerBindingId id, const armnn::TensorInfo &tensorInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01916">CaffeParser.cpp:1916</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a></div><div class="ttdoc">A Pooling2dDescriptor for the Pooling2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00329">Descriptors.hpp:329</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_ae89a123aad1c66a76c398b7af216aae4"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ae89a123aad1c66a76c398b7af216aae4">armnnCaffeParser::ICaffeParser::CaffeParserImpl::Cleanup</a></div><div class="ttdeci">void Cleanup()</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l02261">CaffeParser.cpp:2261</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a></div><div class="ttdoc">A NormalizationDescriptor for the NormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00567">Descriptors.hpp:567</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a77e6c08b48c99fafa560805270503856"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a77e6c08b48c99fafa560805270503856">armnnCaffeParser::ICaffeParser::CaffeParserImpl::BlobShapeToTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo BlobShapeToTensorInfo(const caffe::BlobShape &blobShape) const</div><div class="ttdoc">Converts Caffe&#39;s protobuf tensor shape format to ArmNN&#39;s. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00349">CaffeParser.cpp:349</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a2f8fbd66c1a39a06d61fcb6536387d64"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a2f8fbd66c1a39a06d61fcb6536387d64">armnnCaffeParser::ICaffeParser::CaffeParserImpl::GetInputs</a></div><div class="ttdeci">std::vector< const caffe::LayerParameter * > GetInputs(const caffe::LayerParameter &layerParam)</div><div class="ttdoc">Find the Caffe layers listed as inputs (bottoms) for a given layer. </div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00374">CaffeParser.cpp:374</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a5cddc80538d5de7d36192e0fd2d09c63"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a5cddc80538d5de7d36192e0fd2d09c63">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseConvLayer</a></div><div class="ttdeci">void ParseConvLayer(const caffe::LayerParameter &layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00915">CaffeParser.cpp:915</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a3a2636dd8414f2bb62c5fa097bdc9791"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a3a2636dd8414f2bb62c5fa097bdc9791">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseInputLayer</a></div><div class="ttdeci">void ParseInputLayer(const caffe::LayerParameter &layerParam)</div><div class="ttdoc">Adds an armnn layer to m_Network given a Caffe LayerParameter of the correct type and is responsible ...</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00397">CaffeParser.cpp:397</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_tensor_info_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorInfo::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00191">Tensor.hpp:191</a></div></div> +<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::SoftmaxDescriptor</a></div><div class="ttdoc">A SoftmaxDescriptor for the SoftmaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00139">Descriptors.hpp:139</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a8275d51ef9a584feb95726ea0522f6e5"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">armnn::NormalizationDescriptor::m_Beta</a></div><div class="ttdeci">float m_Beta</div><div class="ttdoc">Beta value for the normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00599">Descriptors.hpp:599</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_aa70c05f1aad12fbd9d9ec43ea4557b03"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">armnn::NormalizationDescriptor::m_NormSize</a></div><div class="ttdeci">uint32_t m_NormSize</div><div class="ttdoc">Depth radius value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00595">Descriptors.hpp:595</a></div></div> +<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml_a2b125117aa61f9baf3a9cb8658aa61a2"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">armnn::ViewsDescriptor::SetViewOriginCoord</a></div><div class="ttdeci">Status SetViewOriginCoord(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the view origin coordinates. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00310">Descriptors.cpp:310</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml_af10fa7883e3579950f477bee92a64844"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">armnn::ActivationDescriptor::m_Function</a></div><div class="ttdeci">ActivationFunction m_Function</div><div class="ttdoc">The activation function to use (Sigmoid, TanH, Linear, ReLu, BoundedReLu, SoftReLu, LeakyReLu, Abs, Sqrt, Square, Elu). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00048">Descriptors.hpp:48</a></div></div> +<div class="ttc" id="structarmnn_1_1_arg_min_max_descriptor_xhtml_abce784834696eb928c620f1fafe71a8d"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_descriptor.xhtml#abce784834696eb928c620f1fafe71a8d">armnn::ArgMinMaxDescriptor::m_Output_Type</a></div><div class="ttdeci">armnn::DataType m_Output_Type</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00074">Descriptors.hpp:74</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Pooling2dDescriptor::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#l00379">Descriptors.hpp:379</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_ac3f705f208b5ee9f540577524b2ad513"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#ac3f705f208b5ee9f540577524b2ad513">armnnCaffeParser::ICaffeParser::CaffeParserImpl::ParseDeconvLayer</a></div><div class="ttdeci">void ParseDeconvLayer(const caffe::LayerParameter &layerParam)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l01094">CaffeParser.cpp:1094</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a></div><div class="ttdoc">A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00460">Descriptors.hpp:460</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml">armnn::BatchNormalizationDescriptor</a></div><div class="ttdoc">A BatchNormalizationDescriptor for the BatchNormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00626">Descriptors.hpp:626</a></div></div> +<div class="ttc" id="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#l00438">Descriptors.hpp:438</a></div></div> +<div class="ttc" id="namespacearmnn_caffe_parser_xhtml_af3fde7630e7c9df35cae9ed2b435dbed"><div class="ttname"><a href="namespacearmnn_caffe_parser.xhtml#af3fde7630e7c9df35cae9ed2b435dbed">armnnCaffeParser::TensorDescToBlobShape</a></div><div class="ttdeci">BlobShape TensorDescToBlobShape(const TensorInfo &desc)</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8cpp_source.xhtml#l00360">CaffeParser.cpp:360</a></div></div> +<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml_a2b125117aa61f9baf3a9cb8658aa61a2"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">armnn::OriginsDescriptor::SetViewOriginCoord</a></div><div class="ttdeci">Status SetViewOriginCoord(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the view origin coordinates. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00167">Descriptors.cpp:167</a></div></div> +<div class="ttc" id="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl_xhtml_a1424f1bfbfc81d317b51053bbb315ef1"><div class="ttname"><a href="classarmnn_caffe_parser_1_1_i_caffe_parser_1_1_caffe_parser_impl.xhtml#a1424f1bfbfc81d317b51053bbb315ef1">armnnCaffeParser::ICaffeParser::CaffeParserImpl::m_CaffeLayersByTopName</a></div><div class="ttdeci">std::map< std::string, const caffe::LayerParameter * > m_CaffeLayersByTopName</div><div class="ttdef"><b>Definition:</b> <a href="_caffe_parser_8hpp_source.xhtml#l00155">CaffeParser.hpp:155</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_04760fd1fb320650b591e359d321263b.xhtml">armnnCaffeParser</a></li><li class="navelem"><a class="el" href="_caffe_parser_8cpp.xhtml">CaffeParser.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> |