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| author | Nikhil Raj <nikhil.raj@arm.com> | 2023-02-24 10:28:19 +0000 |
|---|---|---|
| committer | Nikhil Raj <nikhil.raj@arm.com> | 2023-02-24 10:28:19 +0000 |
| commit | 8d2ca734165a068478df7cffa46185680b05cd20 (patch) | |
| tree | 0433a7e6b007fe4639334c4438e58e9872a34b20 /23.02/_tf_lite_parser_8cpp_source.xhtml | |
| parent | cb0630959aeae05bc2ae9f6d80cf5f5983a8fb77 (diff) | |
| download | armnn-8d2ca734165a068478df7cffa46185680b05cd20.tar.gz | |
Update Doxygen docu for 23.02
Signed-off-by: Nikhil Raj <nikhil.raj@arm.com>
Change-Id: Ie6c19a27d50fefab2796b2b5875374e81f5bf971
Diffstat (limited to '23.02/_tf_lite_parser_8cpp_source.xhtml')
| -rw-r--r-- | 23.02/_tf_lite_parser_8cpp_source.xhtml | 458 |
1 files changed, 458 insertions, 0 deletions
diff --git a/23.02/_tf_lite_parser_8cpp_source.xhtml b/23.02/_tf_lite_parser_8cpp_source.xhtml new file mode 100644 index 0000000000..549be93140 --- /dev/null +++ b/23.02/_tf_lite_parser_8cpp_source.xhtml @@ -0,0 +1,458 @@ +<!-- 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/armnnTfLiteParser/TfLiteParser.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"> + $(document).ready(initResizable); +</script> +<link href="search/search.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="search/searchdata.js"></script> +<script type="text/javascript" src="search/search.js"></script> +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + extensions: ["tex2jax.js"], + jax: ["input/TeX","output/HTML-CSS"], +}); +</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> +<link href="doxygen.css" rel="stylesheet" type="text/css" /> +<link href="stylesheet.css" rel="stylesheet" type="text/css"/> +</head> +<body> +<div id="top"><!-- do not remove this div, it is closed by doxygen! --> +<div id="titlearea"> +<table cellspacing="0" cellpadding="0"> + <tbody> + <tr style="height: 56px;"> + <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/> + <td style="padding-left: 0.5em;"> + <div id="projectname"> +  <span id="projectnumber">23.02</span> + </div> + </td> + </tr> + </tbody> +</table> +</div> +<!-- end header part --> +<!-- Generated by Doxygen 1.8.13 --> +<script type="text/javascript"> +var searchBox = new SearchBox("searchBox", "search",false,'Search'); +</script> +<script type="text/javascript" src="menudata.js"></script> +<script type="text/javascript" src="menu.js"></script> +<script type="text/javascript"> +$(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('_tf_lite_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">TfLiteParser.cpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_tf_lite_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 © 2017-2023 Arm Ltd and Contributors. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_tf_lite_parser_8hpp.xhtml">TfLiteParser.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_tf_lite_parser_2_version_8hpp.xhtml">armnnTfLiteParser/Version.hpp</a>"</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include "<a class="code" href="_lstm_params_8hpp.xhtml">armnn/LstmParams.hpp</a>"</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_backend_options_8hpp.xhtml">armnn/BackendOptions.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_descriptors_8hpp.xhtml">armnn/Descriptors.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> <span class="preprocessor">#include <<a class="code" href="_logging_8hpp.xhtml">armnn/Logging.hpp</a>></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <<a class="code" href="_tensor_8hpp.xhtml">armnn/Tensor.hpp</a>></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <<a class="code" href="_tensor_utils_8hpp.xhtml">armnnUtils/TensorUtils.hpp</a>></span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <<a class="code" href="_types_utils_8hpp.xhtml">armnn/TypesUtils.hpp</a>></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="_ignore_unused_8hpp.xhtml">armnn/utility/IgnoreUnused.hpp</a>></span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</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="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include <<a class="code" href="_layer_support_8hpp.xhtml">armnn/LayerSupport.hpp</a>></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">// armnnUtils:</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include <<a class="code" href="_permute_8hpp.xhtml">armnnUtils/Permute.hpp</a>></span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#include <<a class="code" href="_filesystem_8hpp.xhtml">armnnUtils/Filesystem.hpp</a>></span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include <<a class="code" href="_parser_helper_8hpp.xhtml">ParserHelper.hpp</a>></span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include <<a class="code" href="_verification_helpers_8hpp.xhtml">VerificationHelpers.hpp</a>></span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="comment">// The generated code based on the Tf Lite schema:</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include <schema_generated.h></span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include <flatbuffers/flexbuffers.h></span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include <fmt/format.h></span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#include <algorithm></span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="preprocessor">#include <iostream></span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="preprocessor">#include <limits></span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="preprocessor">#include <numeric></span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> </div><div class="line"><a name="l00042"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9"> 42</a></span> <span class="preprocessor">#define ARMNN_THROW_PARSE_EXCEPTION(msg) \</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="preprocessor"> throw armnn::ParseException( static_cast<const std::stringstream&>( std::stringstream() << msg \</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="preprocessor"> << ": " \</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="preprocessor"> << CHECK_LOCATION().AsString()).str()); \</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="preprocessor"> }</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="keyword">using</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">armnn::CheckLocation</a>;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn_tf_lite_parser.xhtml">armnnTfLiteParser</a></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> {</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> </div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> ITfLiteParser::ITfLiteParser(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<TfLiteParserOptions></a>& options) :</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  pTfLiteParserImpl(<span class="keyword">new</span> TfLiteParserImpl(options)) {}</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> ITfLiteParser::~ITfLiteParser() = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00059"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#acf8cc929eadbabf197b36f7364d3d2cb"> 59</a></span> <a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml">ITfLiteParser</a>* ITfLiteParser::CreateRaw(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<TfLiteParserOptions></a>& options)</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="keywordflow">return</span> <span class="keyword">new</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml">ITfLiteParser</a>(options);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> }</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a9932449a89a62cfcfd72a4fedbee1ab7"> 64</a></span> <a class="code" href="namespacearmnn_tf_lite_parser.xhtml#af69bedce3c37be895f75146016ba8a17">ITfLiteParserPtr</a> ITfLiteParser::Create(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<TfLiteParserOptions></a>& options)</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>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_tf_lite_parser.xhtml#af69bedce3c37be895f75146016ba8a17">ITfLiteParserPtr</a>(CreateRaw(options), &ITfLiteParser::Destroy);</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> }</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div><div class="line"><a name="l00069"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a29a2a153962a701843d5c8ae953cb032"> 69</a></span> <span class="keywordtype">void</span> ITfLiteParser::Destroy(<a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml">ITfLiteParser</a>* parser)</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> {</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keyword">delete</span> parser;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> }</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a6bf5861864c8828e59df24a7868c5439"> 74</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> ITfLiteParser::CreateNetworkFromBinaryFile(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> {</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">return</span> pTfLiteParserImpl-><a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a6bf5861864c8828e59df24a7868c5439">CreateNetworkFromBinaryFile</a>(graphFile);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> }</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#aaa88c7afbe8e8f777d05f99a2a540a99"> 79</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> ITfLiteParser::CreateNetworkFromBinary(<span class="keyword">const</span> std::vector<uint8_t>& binaryContent)</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> {</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keywordflow">return</span> pTfLiteParserImpl->CreateNetworkFromBinary(binaryContent);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> }</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div><div class="line"><a name="l00084"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a913d452e7766210b59a69d3c658b2862"> 84</a></span> <a class="code" href="namespacearmnn_tf_lite_parser.xhtml#a9084adbf804022c874039ad40d1939e9">BindingPointInfo</a> ITfLiteParser::GetNetworkInputBindingInfo(<span class="keywordtype">size_t</span> subgraphId,</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> <span class="keyword"></span>{</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordflow">return</span> pTfLiteParserImpl->GetNetworkInputBindingInfo(subgraphId, name);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> }</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> </div><div class="line"><a name="l00090"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a59139faede3a07a43c4c8f86bb03dc43"> 90</a></span> <a class="code" href="namespacearmnn_tf_lite_parser.xhtml#a9084adbf804022c874039ad40d1939e9">BindingPointInfo</a> ITfLiteParser::GetNetworkOutputBindingInfo(<span class="keywordtype">size_t</span> subgraphId,</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> <span class="keyword"></span>{</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordflow">return</span> pTfLiteParserImpl->GetNetworkOutputBindingInfo(subgraphId, name);</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> }</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> </div><div class="line"><a name="l00096"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a1b7aed8466cc5792966d271a65285d0e"> 96</a></span> <span class="keywordtype">size_t</span> ITfLiteParser::GetSubgraphCount()<span class="keyword"> const</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> <span class="keyword"></span>{</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keywordflow">return</span> pTfLiteParserImpl->GetSubgraphCount();</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_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#afe1ea028d8fcb9942c204d4415ff3319"> 101</a></span> std::vector<std::string> ITfLiteParser::GetSubgraphInputTensorNames(<span class="keywordtype">size_t</span> subgraphId)<span class="keyword"> const</span></div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> <span class="keyword"></span>{</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordflow">return</span> pTfLiteParserImpl->GetSubgraphInputTensorNames(subgraphId);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> }</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a949070abe70fabe18cc1eb29d5853f47"> 106</a></span> std::vector<std::string> ITfLiteParser::GetSubgraphOutputTensorNames(<span class="keywordtype">size_t</span> subgraphId)<span class="keyword"> const</span></div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> <span class="keyword"></span>{</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="keywordflow">return</span> pTfLiteParserImpl->GetSubgraphOutputTensorNames(subgraphId);</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> }</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> </div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> <span class="keyword">namespace</span></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"> 114</span> <span class="keyword">const</span> uint32_t VIRTUAL_OPERATOR_ID = std::numeric_limits<uint32_t>::max();</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> <span class="keywordtype">void</span> CheckSubgraph(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abfc86471394295357a23b3addd0b5b1c">TfLiteParserImpl::ModelPtr</a>& model,</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a>& location)</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> {</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordflow">if</span> (model.get() == <span class="keyword">nullptr</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="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  fmt::format(<span class="stringliteral">"{} was called with invalid (null) model. "</span></div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="stringliteral">"Possible reason is that the model is not yet loaded and Unpack(ed). "</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="stringliteral">"subgraph:{} at {}"</span>,</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a>,</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  subgraphIndex,</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  }</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (subgraphIndex >= model->subgraphs.size())</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  {</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  fmt::format(<span class="stringliteral">"{} was called with an invalid subgraph index. "</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="stringliteral">"subgraph:{} at {}"</span>,</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a>,</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  subgraphIndex,</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  }</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> }</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a"> 141</a></span> <span class="preprocessor">#define CHECK_SUBGRAPH(MODEL, SUBGRAPH_INDEX) \</span></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> <span class="preprocessor"> CheckSubgraph(MODEL, SUBGRAPH_INDEX, CHECK_LOCATION())</span></div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> </div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> <span class="keywordtype">void</span> CheckModel(<span class="keyword">const</span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">TfLiteParserImpl::ModelPtr</a>& model,</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a>& location)</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> {</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="keywordflow">if</span> (model.get() == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  {</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  fmt::format(<span class="stringliteral">"{} was called with invalid (null) model. "</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="stringliteral">"Possible reason is that the model is not yet loaded and Unpack(ed). "</span></div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="stringliteral">"subgraph:{} operator:{} at {}"</span>,</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a>,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  subgraphIndex,</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  operatorIndex,</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  }</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (subgraphIndex >= model->subgraphs.size())</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  {</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  fmt::format(<span class="stringliteral">"{} was called with an invalid subgraph index. "</span></div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="stringliteral">"subgraph:{} operator:{} at {}"</span>,</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a>,</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  subgraphIndex,</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  operatorIndex,</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  }</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (operatorIndex >= model->subgraphs[subgraphIndex]->operators.size() &&</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  operatorIndex != VIRTUAL_OPERATOR_ID)</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  fmt::format(<span class="stringliteral">"{} was called with an invalid operator index. "</span></div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="stringliteral">"subgraph:{} operator:{} at {}"</span>,</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a>,</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  subgraphIndex,</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  operatorIndex,</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</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> }</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> </div><div class="line"><a name="l00183"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb"> 183</a></span> <span class="preprocessor">#define CHECK_MODEL(MODEL, SUBGRAPH_INDEX, OPERATOR_INDEX) \</span></div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> <span class="preprocessor"> CheckModel(MODEL, SUBGRAPH_INDEX, OPERATOR_INDEX, CHECK_LOCATION())</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> <span class="keywordtype">void</span> CheckTensor(<span class="keyword">const</span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">TfLiteParserImpl::ModelPtr</a>& model,</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordtype">size_t</span> tensorIndex,</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a>& location)</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> {</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="comment">// not checking model, because I assume CHECK_MODEL already run</span></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="comment">// and checked that. An assert would do.</span></div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(model.get() != <span class="keyword">nullptr</span>, <span class="stringliteral">"Expecting a valid model in this function"</span>);</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> </div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="comment">// also subgraph index should be checked by CHECK_MODEL so</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="comment">// I only add an assert here</span></div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(subgraphIndex < model->subgraphs.size(), <span class="stringliteral">"Expecting a valid subgraph index"</span>);</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> </div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="comment">// the tensor index is the only one to check here</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keywordflow">if</span> (tensorIndex >= model->subgraphs[subgraphIndex]->tensors.size())</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  {</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  fmt::format(<span class="stringliteral">"{} was called with an invalid tensor index. "</span></div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="stringliteral">"subgraph:{} tensor:{} at {}"</span>,</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a>,</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  subgraphIndex,</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  tensorIndex,</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</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> }</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> </div><div class="line"><a name="l00212"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#aa1664dc13adbc85ac12fb584b76bfdae"> 212</a></span> <span class="preprocessor">#define CHECK_TENSOR(MODEL, SUBGRAPH_INDEX, TENSOR_INDEX) \</span></div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span> <span class="preprocessor"> CheckTensor(MODEL, SUBGRAPH_INDEX, TENSOR_INDEX, CHECK_LOCATION())</span></div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> </div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> <span class="keywordtype">void</span> CheckTensorPtr(<a class="code" href="namespacearmnn_deserializer.xhtml#a80888061963ddd18e87105807a035d9a">TfLiteParserImpl::TensorRawPtr</a> rawPtr,</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a>& location)</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> {</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keywordflow">if</span> (rawPtr == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  {</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  fmt::format(<span class="stringliteral">"{} was called with a null tensor pointer at {}"</span>, location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a>, location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  }</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> }</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> </div><div class="line"><a name="l00225"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#ae38d96fe05581ea025713b3e781c5a43"> 225</a></span> <span class="preprocessor">#define CHECK_TENSOR_PTR(TENSOR_PTR) \</span></div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> <span class="preprocessor"> CheckTensorPtr(TENSOR_PTR, CHECK_LOCATION())</span></div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> </div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span> <span class="keywordtype">void</span> CheckBuffer(<span class="keyword">const</span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">TfLiteParserImpl::ModelPtr</a>& model,</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keywordtype">size_t</span> bufferIndex,</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a>& location)</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> {</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keywordflow">if</span> (model.get() == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  {</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  fmt::format(<span class="stringliteral">"{} was called with invalid (null) model. "</span></div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="stringliteral">"Possible reason is that the model is not yet loaded and Unpack(ed). "</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="stringliteral">"buffer:{} at {}"</span>,</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a>,</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  bufferIndex,</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  }</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (bufferIndex >= model->buffers.size())</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  {</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  fmt::format(<span class="stringliteral">"{} was called with an invalid buffer index. "</span></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="stringliteral">"buffer index:{} at {}"</span>,</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a>,</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  bufferIndex,</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  }</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (model->buffers[bufferIndex].get() == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  {</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  fmt::format(<span class="stringliteral">"The buffer #{} is null. {}"</span>,</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  bufferIndex,</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a5e3562cda960da001597e7dd5679b140">AsString</a>()));</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  }</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> }</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> </div><div class="line"><a name="l00260"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#a7c88d54e3f895030c70330a4c9d76a7a"> 260</a></span> <span class="preprocessor">#define CHECK_BUFFER(MODEL, BUFFER_INDEX) \</span></div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> <span class="preprocessor"> CheckBuffer(MODEL, BUFFER_INDEX, CHECK_LOCATION())</span></div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> </div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> <span class="keywordtype">void</span> CheckBufferSize(TfLiteParserImpl::BufferRawPtr bufferPtr,</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</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="l00265"></a><span class="lineno"> 265</span>  uint32_t bufferId,</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a>& location)</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> {</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keywordflow">if</span> (bufferPtr == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  {</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  fmt::format(<span class="stringliteral">"BufferPtr is null for buffer:{}. {}"</span>,</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  bufferId,</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a5e3562cda960da001597e7dd5679b140">AsString</a>()));</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  }</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() > bufferPtr->data.size() ||</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>() > bufferPtr->data.size())</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  {</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  std::stringstream ss;</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  ss << <span class="stringliteral">"Buffer #"</span> << bufferId << <span class="stringliteral">" has "</span> << bufferPtr->data.size() << <span class="stringliteral">" bytes. "</span></div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  << <span class="stringliteral">"For tensor: "</span> << tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  << <span class="stringliteral">" expecting: "</span> << tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>() << <span class="stringliteral">" bytes and "</span></div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  << tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() << <span class="stringliteral">" elements. "</span> << location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a5e3562cda960da001597e7dd5679b140">AsString</a>();</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(ss.str());</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  }</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> }</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span> </div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> </div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span> tflite::BuiltinOperator GetOpCode(<span class="keyword">const</span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">TfLiteParserImpl::ModelPtr</a>& model, <span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> {</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="keyword">auto</span> opcodeIndex = operatorPtr->opcode_index;</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span> </div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span> <span class="comment">// work around the introduction of the deprecated_builtin_code introduced in 2.4 in a backwards compatible manner</span></div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span> <span class="preprocessor">#if defined(ARMNN_POST_TFLITE_2_3)</span></div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="keyword">auto</span> opcode = std::max(model->operator_codes[opcodeIndex]->builtin_code,</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  static_cast<tflite::BuiltinOperator>(model->operator_codes[opcodeIndex]->deprecated_builtin_code));</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span> <span class="preprocessor">#else</span></div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="keyword">auto</span> opcode = model->operator_codes[opcodeIndex]->builtin_code;</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keywordflow">return</span> opcode;</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> }</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span> </div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> std::vector<unsigned int> GetUIntBuffer(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>,</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">TfLiteParserImpl::ModelPtr</a>& model,</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keywordtype">size_t</span> bufferIndex)</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span> {</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  TfLiteParserImpl::BufferRawPtr bufferPtr = TfLiteParserImpl::GetBuffer(model, bufferIndex);</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  std::vector<unsigned int> buffer(info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span> </div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keywordflow">if</span> (info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>)</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  {</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  ::memcpy(buffer.data(), bufferPtr->data.data(), bufferPtr->data.size());</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">DataType::Signed64</a>)</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  {</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  std::vector<uint64_t> uint64Buffer(info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  ::memcpy(uint64Buffer.data(), bufferPtr->data.data(), bufferPtr->data.size());</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  buffer.assign(std::begin(uint64Buffer), std::end(uint64Buffer));</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>  <span class="keywordflow">else</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"> 322</span>  <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a> location = <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>();</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  fmt::format(<span class="stringliteral">"Unsupported data type for uint buffer {}, only Signed 32 or Signed 64 are supported. {}"</span>,</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()),</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a5e3562cda960da001597e7dd5679b140">AsString</a>()));</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  }</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="keywordflow">return</span> buffer;</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span> }</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> </div><div class="line"><a name="l00331"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#ae6440b8ea95cf981cd7bbffa52c22fe1"> 331</a></span> <span class="preprocessor">#define CHECK_BUFFER_SIZE(BUFFER_PTR, TENSOR_INFO, BUFFER_ID) \</span></div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span> <span class="preprocessor"> CheckBufferSize(BUFFER_PTR, TENSOR_INFO, BUFFER_ID, CHECK_LOCATION())</span></div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span> </div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span> <span class="keywordtype">bool</span> <a class="code" href="namespacearmnn.xhtml#a6e7dc8639c4b2f0a93031afff5182735">IsActivationSupported</a>(tflite::ActivationFunctionType activationType)</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span> {</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="keywordflow">switch</span>(activationType)</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  {</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_NONE:</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_RELU:</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_RELU6:</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_TANH:</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  {</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  }</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  {</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <span class="keywordflow">return</span> <span class="keyword">false</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"> 349</span>  }</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span> }</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span> </div><div class="line"><a name="l00352"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf"> 352</a></span> <span class="preprocessor">#define CHECK_SUPPORTED_FUSED_ACTIVATION(OPTION, SUBGRAPH_INDEX, OPERATOR_INDEX) \</span></div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span> <span class="preprocessor"> do { \</span></div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> <span class="preprocessor"> if (IsActivationSupported(OPTION->fused_activation_function) == false) \</span></div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span> <span class="preprocessor"> throw ParseException( \</span></div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> <span class="preprocessor"> fmt::format("TfLite parser doesn't support fused activation: " \</span></div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span> <span class="preprocessor"> "{}/{} in {} subgraph:{} operator:{} at {}", \</span></div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span> <span class="preprocessor"> OPTION->fused_activation_function, \</span></div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span> <span class="preprocessor"> tflite::EnumNameActivationFunctionType(\</span></div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span> <span class="preprocessor"> OPTION->fused_activation_function), \</span></div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span> <span class="preprocessor"> __func__, \</span></div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span> <span class="preprocessor"> SUBGRAPH_INDEX, \</span></div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> <span class="preprocessor"> OPERATOR_INDEX, \</span></div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span> <span class="preprocessor"> CHECK_LOCATION().FileLine())); \</span></div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span> <span class="preprocessor"> } \</span></div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span> <span class="preprocessor"> } while(false)</span></div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span> </div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span> </div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span> std::vector<unsigned int> AsUnsignedVector(<span class="keyword">const</span> std::vector<int32_t>& in)</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>  std::vector<unsigned int> result;</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  result.reserve(in.size());</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>& i : in)</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>  <span class="comment">// If the location of the input data is -1 then the input should be ignored.</span></div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <span class="keywordflow">if</span> (i == -1)</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  {</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <span class="keywordflow">continue</span>;</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  }</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  result.push_back(<a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(i));</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  }</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> }</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span> </div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span> <span class="keywordtype">bool</span> IsOptionalOperandPresent(<span class="keywordtype">int</span> input)</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> {</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="keywordflow">return</span> (input >= 0);</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> }</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> </div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span> <span class="keywordtype">void</span> CalcPadding(uint32_t inputSize,</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  uint32_t filterSize,</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  uint32_t stride,</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  uint32_t dilation,</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  uint32_t& paddingFront,</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  uint32_t& paddingBack,</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  tflite::Padding padding)</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>  paddingFront = 0;</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  paddingBack = 0;</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="keywordflow">if</span> (padding == tflite::Padding_SAME)</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  {</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  uint32_t outputSize = (inputSize + stride - 1) / stride;</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  uint32_t dilatedSize = filterSize + (dilation - 1) * (filterSize - 1);</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  uint32_t temp = (outputSize - 1) * stride + dilatedSize;</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keywordflow">if</span> (temp > inputSize)</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>  paddingFront = (temp - inputSize) / 2;</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  paddingBack = (temp - inputSize) - paddingFront;</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  }</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> }</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span> </div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(<a class="code" href="namespacearmnn_deserializer.xhtml#a80888061963ddd18e87105807a035d9a">TfLiteParserImpl::TensorRawPtr</a> tensorPtr,</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <span class="keyword">const</span> std::vector<unsigned int>& shape,</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> outputTensor = <span class="keyword">false</span>)</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>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> type;</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ae38d96fe05581ea025713b3e781c5a43">CHECK_TENSOR_PTR</a>(tensorPtr);</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span> </div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keywordflow">switch</span> (tensorPtr->type)</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  {</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="keywordflow">case</span> tflite::TensorType_UINT8:</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>;</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keywordflow">case</span> tflite::TensorType_FLOAT32:</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>;</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="keywordflow">case</span> tflite::TensorType_FLOAT16:</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a>;</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="keywordflow">case</span> tflite::TensorType_INT8:</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keywordflow">if</span> (tensorPtr->quantization->zero_point.size() == 1)</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="comment">// Per-tensor</span></div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>;</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  }</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  {</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <span class="comment">// Per-channel</span></div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>;</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  }</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <span class="keywordflow">case</span> tflite::TensorType_INT16:</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>;</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <span class="keywordflow">case</span> tflite::TensorType_INT32:</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>;</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  <span class="keywordflow">case</span> tflite::TensorType_INT64:</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::DataType::Signed64</a>;</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <span class="keywordflow">case</span> tflite::TensorType_BOOL:</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>;</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  {</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a> location = <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>();</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  fmt::format(<span class="stringliteral">"Unsupported data type {} = {} for tensor: {}. {}"</span>,</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  tensorPtr->type,</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  tflite::EnumNameTensorType(tensorPtr->type),</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  tensorPtr->name,</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a5e3562cda960da001597e7dd5679b140">AsString</a>()));</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>  }</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> tensorShape;</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span> </div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  std::vector<unsigned int> safeShape = shape;</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <span class="keywordflow">if</span> (shape.size() == 0)</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>  safeShape.push_back(1);</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  }</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span> </div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <span class="keywordflow">if</span> (!outputTensor)</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  {</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  tensorShape = <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(armnn::numeric_cast<unsigned int>(safeShape.size()), safeShape.data());</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  }</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="keywordflow">else</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>  <span class="keywordtype">size_t</span> shapeSignatureSize = tensorPtr->shape_signature.size();</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span> </div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <span class="comment">// If a shape signature exists we will use that to infer dynamic tensors</span></div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="keywordflow">if</span> (shapeSignatureSize != 0)</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  {</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <span class="comment">// If the shape is incompatible with the shape signature override the shape</span></div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="keywordflow">if</span> (shapeSignatureSize != shape.size())</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  {</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  safeShape = {};</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span> </div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < shapeSignatureSize; ++i)</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  {</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dim = tensorPtr->shape_signature[i] > -1 ?</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(tensorPtr->shape_signature[i]) : 0;</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  safeShape.push_back(dim);</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>  }</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span> </div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  std::unique_ptr<bool[]> dimMask = std::make_unique<bool[]>(tensorPtr->shape_signature.size());</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <span class="keywordtype">bool</span> batchOnly = <span class="keyword">true</span>;</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < tensorPtr->shape_signature.size(); ++i)</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>  dimMask[i] = tensorPtr->shape_signature[i] != -1;</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span> </div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  <span class="keywordflow">if</span> (i > 0 && !dimMask[i])</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>  batchOnly = <span class="keyword">false</span>;</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  }</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>  <span class="keywordflow">if</span> (batchOnly)</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>  dimMask[0] = <span class="keyword">true</span>;</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  }</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  tensorShape = <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(static_cast<unsigned int>(safeShape.size()), safeShape.data(), dimMask.get());</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  }</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <span class="comment">// If there is no shape signature treat the tensor as dynamic if the shape has a size of zero</span></div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (shape.size() == 0)</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  {</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  tensorShape = <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(1, <span class="keyword">false</span>);</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  }</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  {</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  tensorShape = <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(armnn::numeric_cast<unsigned int>(shape.size()), shape.data());</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  }</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  }</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span> </div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <span class="keywordtype">float</span> quantizationScale = 0.0f;</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  int32_t quantizationOffset = 0;</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span> </div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <span class="keywordflow">if</span> (tensorPtr->quantization.get())</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  {</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <span class="keywordflow">if</span> (tensorPtr->quantization->scale.size() <= 1)</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  {</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(tensorPtr->quantization->zero_point.size(), 0, 1);</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(tensorPtr->quantization->zero_point.size(), 0, 1);</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span> </div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="keywordflow">if</span> (tensorPtr->quantization->scale.size() == 1)</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  {</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  quantizationScale = tensorPtr->quantization->scale[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="keywordflow">if</span> (tensorPtr->quantization->zero_point.size() == 1)</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  {</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="comment">// NOTE: we lose precision here when converting from 64 bit to 32</span></div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <span class="comment">// but this is what we support at the moment in ArmNN</span></div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  quantizationOffset = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><int32_t>(tensorPtr->quantization->zero_point[0]);</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> </div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> result(tensorShape,</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  type,</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  quantizationScale,</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  quantizationOffset);</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  }</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  {</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  std::vector<float> quantizationScales;</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  std::vector<int32_t> quantizationOffsets;</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="comment">// Scale</span></div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  std::copy(tensorPtr->quantization->scale.begin(),</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  tensorPtr->quantization->scale.end(),</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  std::back_inserter(quantizationScales));</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">// QSymmS8 Per-axis</span></div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> result(tensorShape,</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  type,</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  quantizationScales,</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  armnn::numeric_cast<unsigned int>(tensorPtr->quantization->quantized_dimension));</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  <span class="keywordflow">return</span> result;</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>  }</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  {</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> result(tensorShape,</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  type,</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  quantizationScale,</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  quantizationOffset);</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  }</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span> }</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span> </div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(<a class="code" href="namespacearmnn_deserializer.xhtml#a80888061963ddd18e87105807a035d9a">TfLiteParserImpl::TensorRawPtr</a> tensorPtr,</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> outputTensor = <span class="keyword">false</span>)</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="keyword">auto</span> <span class="keyword">const</span>& dimensions = AsUnsignedVector(tensorPtr->shape);</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(tensorPtr, dimensions, outputTensor);</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span> }</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span> </div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span> <span class="keyword">template</span><<span class="keyword">typename</span> T></div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span> std::pair<armnn::ConstTensor, std::unique_ptr<T[]>></div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#ae89f792279f0d06b6c164a6f1c7529e1">CreateConstTensorImpl</a>(TfLiteParserImpl::BufferRawPtr bufferPtr,</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  <a class="code" href="namespacearmnn_deserializer.xhtml#a80888061963ddd18e87105807a035d9a">TfLiteParserImpl::TensorRawPtr</a> tensorPtr,</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& tensorInfo,</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::PermutationVector&></a> permutationVector)</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span> {</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(tensorPtr);</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(tensorPtr != <span class="keyword">nullptr</span>, <span class="stringliteral">"tensorPtr is null"</span>);</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(bufferPtr != <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  fmt::format(<span class="stringliteral">"Buffer for buffer:{} is null"</span>, tensorPtr->buffer).c_str());</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>  std::unique_ptr<T[]> data(<span class="keyword">new</span> T[tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>()]);</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span> </div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  <span class="keywordflow">if</span> (permutationVector.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() && permutationVector.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>().<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>() > 0)</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  {</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  tensorInfo = <a class="code" href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(tensorInfo, permutationVector.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>());</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">armnnUtils::Permute</a>(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), permutationVector.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>(),</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <span class="keyword">reinterpret_cast<</span><span class="keyword">const </span>T*<span class="keyword">></span>(bufferPtr->data.data()), data.get(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  }</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  <span class="keywordflow">else</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>  ::memcpy(data.get(), bufferPtr->data.data(), tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  }</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span> </div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  <span class="comment">// Make sure isConstant flag is set.</span></div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>();</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span> </div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <span class="keywordflow">return</span> std::make_pair(<a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(tensorInfo, data.get()), std::move(data));</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span> }</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span> </div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> GenerateLayerBindingId(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> tensorIndex)</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span> {</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  <span class="comment">// generate the binding id by shifting the tensor id by 8 bit</span></div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  <span class="comment">// and add the subgraph id, which allows 256 subgraphs</span></div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  <span class="keywordflow">return</span> <span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a><span class="keyword">></span>((tensorIndex<<8)+subgraphIndex);</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span> }</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span> </div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span> <span class="keywordtype">bool</span> <a class="code" href="namespacearmnn_deserializer.xhtml#a97b50f22cd99f0e09e6e48d20a35f6b2">CheckShape</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& actual, <span class="keyword">const</span> std::vector<int32_t>& expected)</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="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> actualSize = actual.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  <span class="keywordflow">if</span> (actualSize != expected.size())</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  {</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  }</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span> </div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0u; i < actualSize; i++)</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  {</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  <span class="keywordflow">if</span> (expected[i] < 0 ||</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  actual[i] != static_cast<unsigned int>(expected[i]))</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  {</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  }</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  }</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span> </div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span> }</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span> </div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span> <span class="keywordtype">bool</span> <a class="code" href="namespacearmnn_deserializer.xhtml#a97b50f22cd99f0e09e6e48d20a35f6b2">CheckShape</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& actual, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& expected)</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>  std::vector<int32_t> expectedVec;</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  <span class="keywordflow">for</span> (uint32_t i = 0; i < expected.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); i++)</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  {</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  expectedVec.push_back(expected[i]);</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  }</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_deserializer.xhtml#a97b50f22cd99f0e09e6e48d20a35f6b2">CheckShape</a>(actual, expectedVec);</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> </div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span> <span class="keywordtype">void</span> CheckMatchingQuantization(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& first,</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& second,</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <span class="keyword">const</span> std::string& descName,</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  std::string <span class="keyword">const</span>& firstName,</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  std::string <span class="keyword">const</span>& secondName)</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span> {</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <span class="keywordflow">if</span> (!first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a7c00efeb540198b33b8558c76e5cc2dd">IsQuantized</a>() ||</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  !second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a7c00efeb540198b33b8558c76e5cc2dd">IsQuantized</a>())</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  {</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <span class="comment">// Not a quantized type, ignore the validation</span></div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  }</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span> </div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> firstDataType = first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> secondDataType = second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span> </div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <span class="keywordflow">if</span> (firstDataType != secondDataType)</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  {</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">": "</span> + firstName + <span class="stringliteral">" and "</span> + secondName +</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  <span class="stringliteral">" must be of the same quantized type, "</span> +</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  firstName + <span class="stringliteral">" is "</span> + <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(firstDataType) + <span class="stringliteral">", "</span> +</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  secondName + <span class="stringliteral">" is "</span> + <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(secondDataType));</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> </div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  <span class="keywordflow">if</span> (!first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a22f377fc4e10dc1773a3f979061e85f1">IsTypeSpaceMatch</a>(second))</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  {</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">": "</span> + firstName + <span class="stringliteral">" and "</span> + secondName +</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  <span class="stringliteral">" must have the same quantization space, "</span> +</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  firstName + <span class="stringliteral">" has offset "</span> + std::to_string(first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>()) +</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  <span class="stringliteral">" and scale "</span> + std::to_string(first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>()) + <span class="stringliteral">", "</span> +</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  secondName + <span class="stringliteral">" has offset "</span> + std::to_string(second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>()) +</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  <span class="stringliteral">" and scale "</span> + std::to_string(second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>()));</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> }</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> <span class="keywordtype">bool</span> IsDynamic(<a class="code" href="namespacearmnn_deserializer.xhtml#a80888061963ddd18e87105807a035d9a">TfLiteParserImpl::TensorRawPtr</a> tensorPtr)</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">auto</span> shape = tensorPtr->shape;</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span> </div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  <span class="keywordflow">if</span> (shape.empty())</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  {</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</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>  <span class="keyword">auto</span> shapeSig = tensorPtr->shape_signature;</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>  <span class="keywordflow">if</span> (shapeSig.empty())</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  {</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  }</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span> </div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < shapeSig.size() ; ++i)</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  {</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  <span class="keywordflow">if</span> (shapeSig[i] == -1)</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  {</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  }</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  }</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span> }</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span> </div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span> } <span class="comment">// <anonymous></span></div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span> </div><div class="line"><a name="l00719"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#ad9199a42f5fae611db182fb4c31a11df"> 719</a></span> TfLiteParserImpl::TfLiteParserImpl(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ITfLiteParser::TfLiteParserOptions></a>& options)</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span> : m_Options(options)</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span> , m_Network(nullptr, nullptr)</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span> , m_ParserFunctions(<a class="code" href="namespacetflite.xhtml">tflite</a>::BuiltinOperator_MAX+1, &<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml">TfLiteParserImpl</a>::ParseUnsupportedOperator)</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span> {</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  <span class="comment">// register supported operators</span></div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  m_ParserFunctions[tflite::BuiltinOperator_ABS] = &TfLiteParserImpl::ParseAbs;</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  m_ParserFunctions[tflite::BuiltinOperator_ADD] = &TfLiteParserImpl::ParseAdd;</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  m_ParserFunctions[tflite::BuiltinOperator_ARG_MIN] = &TfLiteParserImpl::ParseArgMin;</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  m_ParserFunctions[tflite::BuiltinOperator_ARG_MAX] = &TfLiteParserImpl::ParseArgMax;</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  m_ParserFunctions[tflite::BuiltinOperator_AVERAGE_POOL_2D] = &TfLiteParserImpl::ParseAveragePool2D;</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  m_ParserFunctions[tflite::BuiltinOperator_BATCH_TO_SPACE_ND] = &TfLiteParserImpl::ParseBatchToSpaceND;</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  m_ParserFunctions[tflite::BuiltinOperator_BATCH_MATMUL] = &TfLiteParserImpl::ParseBatchMatMul;</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  m_ParserFunctions[tflite::BuiltinOperator_CAST] = &TfLiteParserImpl::ParseCast;</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  m_ParserFunctions[tflite::BuiltinOperator_CONCATENATION] = &TfLiteParserImpl::ParseConcatenation;</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  m_ParserFunctions[tflite::BuiltinOperator_CONV_2D] = &TfLiteParserImpl::ParseConv2D;</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  <span class="comment">// Conv3D support was added in TF 2.5, so for backwards compatibility a hash define is needed.</span></div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span> <span class="preprocessor"> #if defined(ARMNN_POST_TFLITE_2_4)</span></div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  m_ParserFunctions[tflite::BuiltinOperator_CONV_3D] = &TfLiteParserImpl::ParseConv3D;</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span> <span class="preprocessor"> #endif</span></div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  m_ParserFunctions[tflite::BuiltinOperator_CUSTOM] = &TfLiteParserImpl::ParseCustomOperator;</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  m_ParserFunctions[tflite::BuiltinOperator_DEPTH_TO_SPACE] = &TfLiteParserImpl::ParseDepthToSpace;</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  m_ParserFunctions[tflite::BuiltinOperator_DEPTHWISE_CONV_2D] = &TfLiteParserImpl::ParseDepthwiseConv2D;</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  m_ParserFunctions[tflite::BuiltinOperator_DEQUANTIZE] = &TfLiteParserImpl::ParseDequantize;</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  m_ParserFunctions[tflite::BuiltinOperator_DIV] = &TfLiteParserImpl::ParseDiv;</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  m_ParserFunctions[tflite::BuiltinOperator_ELU] = &TfLiteParserImpl::ParseElu;</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  m_ParserFunctions[tflite::BuiltinOperator_EQUAL] = &TfLiteParserImpl::ParseEqual;</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  m_ParserFunctions[tflite::BuiltinOperator_EXP] = &TfLiteParserImpl::ParseExp;</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  m_ParserFunctions[tflite::BuiltinOperator_EXPAND_DIMS] = &TfLiteParserImpl::ParseExpandDims;</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  m_ParserFunctions[tflite::BuiltinOperator_FLOOR_DIV] = &TfLiteParserImpl::ParseFloorDiv;</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  m_ParserFunctions[tflite::BuiltinOperator_FULLY_CONNECTED] = &TfLiteParserImpl::ParseFullyConnected;</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  m_ParserFunctions[tflite::BuiltinOperator_GATHER] = &TfLiteParserImpl::ParseGather;</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  m_ParserFunctions[tflite::BuiltinOperator_GATHER_ND] = &TfLiteParserImpl::ParseGatherNd;</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  m_ParserFunctions[tflite::BuiltinOperator_GREATER] = &TfLiteParserImpl::ParseGreater;</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  m_ParserFunctions[tflite::BuiltinOperator_GREATER_EQUAL] = &TfLiteParserImpl::ParseGreaterOrEqual;</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  m_ParserFunctions[tflite::BuiltinOperator_HARD_SWISH] = &TfLiteParserImpl::ParseHardSwish;</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  m_ParserFunctions[tflite::BuiltinOperator_LEAKY_RELU] = &TfLiteParserImpl::ParseLeakyRelu;</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  m_ParserFunctions[tflite::BuiltinOperator_LESS] = &TfLiteParserImpl::ParseLess;</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  m_ParserFunctions[tflite::BuiltinOperator_LESS_EQUAL] = &TfLiteParserImpl::ParseLessOrEqual;</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  m_ParserFunctions[tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION]</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  = &TfLiteParserImpl::ParseLocalResponseNormalization;</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  m_ParserFunctions[tflite::BuiltinOperator_LOG] = &TfLiteParserImpl::ParseLog;</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  m_ParserFunctions[tflite::BuiltinOperator_LOGICAL_NOT] = &TfLiteParserImpl::ParseLogicalNot;</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  m_ParserFunctions[tflite::BuiltinOperator_LOGISTIC] = &TfLiteParserImpl::ParseLogistic;</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  m_ParserFunctions[tflite::BuiltinOperator_LOG_SOFTMAX] = &TfLiteParserImpl::ParseLogSoftmax;</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  m_ParserFunctions[tflite::BuiltinOperator_L2_NORMALIZATION] = &TfLiteParserImpl::ParseL2Normalization;</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  m_ParserFunctions[tflite::BuiltinOperator_MAX_POOL_2D] = &TfLiteParserImpl::ParseMaxPool2D;</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  m_ParserFunctions[tflite::BuiltinOperator_MAXIMUM] = &TfLiteParserImpl::ParseMaximum;</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  m_ParserFunctions[tflite::BuiltinOperator_MEAN] = &TfLiteParserImpl::ParseMean;</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  m_ParserFunctions[tflite::BuiltinOperator_MINIMUM] = &TfLiteParserImpl::ParseMinimum;</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  m_ParserFunctions[tflite::BuiltinOperator_MIRROR_PAD] = &TfLiteParserImpl::ParseMirrorPad;</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  m_ParserFunctions[tflite::BuiltinOperator_MUL] = &TfLiteParserImpl::ParseMul;</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  m_ParserFunctions[tflite::BuiltinOperator_NEG] = &TfLiteParserImpl::ParseNeg;</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  m_ParserFunctions[tflite::BuiltinOperator_NOT_EQUAL] = &TfLiteParserImpl::ParseNotEqual;</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  m_ParserFunctions[tflite::BuiltinOperator_PACK] = &TfLiteParserImpl::ParsePack;</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  m_ParserFunctions[tflite::BuiltinOperator_PAD] = &TfLiteParserImpl::ParsePad;</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  m_ParserFunctions[tflite::BuiltinOperator_PADV2] = &TfLiteParserImpl::ParsePad;</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  m_ParserFunctions[tflite::BuiltinOperator_PRELU] = &TfLiteParserImpl::ParsePrelu;</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  m_ParserFunctions[tflite::BuiltinOperator_QUANTIZE] = &TfLiteParserImpl::ParseQuantize;</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  m_ParserFunctions[tflite::BuiltinOperator_RELU] = &TfLiteParserImpl::ParseRelu;</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  m_ParserFunctions[tflite::BuiltinOperator_RELU6] = &TfLiteParserImpl::ParseRelu6;</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  m_ParserFunctions[tflite::BuiltinOperator_REDUCE_MAX] = &TfLiteParserImpl::ParseReduceMax;</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  m_ParserFunctions[tflite::BuiltinOperator_REDUCE_MIN] = &TfLiteParserImpl::ParseReduceMin;</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  m_ParserFunctions[tflite::BuiltinOperator_REDUCE_PROD] = &TfLiteParserImpl::ParseReduceProd;</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  m_ParserFunctions[tflite::BuiltinOperator_RESHAPE] = &TfLiteParserImpl::ParseReshape;</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  m_ParserFunctions[tflite::BuiltinOperator_RESIZE_BILINEAR] = &TfLiteParserImpl::ParseResizeBilinear;</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  m_ParserFunctions[tflite::BuiltinOperator_RESIZE_NEAREST_NEIGHBOR] = &TfLiteParserImpl::ParseResizeNearestNeighbor;</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  m_ParserFunctions[tflite::BuiltinOperator_RSQRT] = &TfLiteParserImpl::ParseRsqrt;</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  m_ParserFunctions[tflite::BuiltinOperator_SQRT] = &TfLiteParserImpl::ParseSqrt;</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  m_ParserFunctions[tflite::BuiltinOperator_SHAPE] = &TfLiteParserImpl::ParseShape;</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  m_ParserFunctions[tflite::BuiltinOperator_SIN] = &TfLiteParserImpl::ParseSin;</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  m_ParserFunctions[tflite::BuiltinOperator_SLICE] = &TfLiteParserImpl::ParseSlice;</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  m_ParserFunctions[tflite::BuiltinOperator_SOFTMAX] = &TfLiteParserImpl::ParseSoftmax;</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  m_ParserFunctions[tflite::BuiltinOperator_SPACE_TO_BATCH_ND] = &TfLiteParserImpl::ParseSpaceToBatchND;</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  m_ParserFunctions[tflite::BuiltinOperator_SPLIT] = &TfLiteParserImpl::ParseSplit;</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  m_ParserFunctions[tflite::BuiltinOperator_SPLIT_V] = &TfLiteParserImpl::ParseSplitV;</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  m_ParserFunctions[tflite::BuiltinOperator_SQUEEZE] = &TfLiteParserImpl::ParseSqueeze;</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  m_ParserFunctions[tflite::BuiltinOperator_STRIDED_SLICE] = &TfLiteParserImpl::ParseStridedSlice;</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  m_ParserFunctions[tflite::BuiltinOperator_SUB] = &TfLiteParserImpl::ParseSub;</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  m_ParserFunctions[tflite::BuiltinOperator_SUM] = &TfLiteParserImpl::ParseSum;</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  m_ParserFunctions[tflite::BuiltinOperator_TANH] = &TfLiteParserImpl::ParseTanH;</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  m_ParserFunctions[tflite::BuiltinOperator_TRANSPOSE] = &TfLiteParserImpl::ParseTranspose;</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  m_ParserFunctions[tflite::BuiltinOperator_TRANSPOSE_CONV] = &TfLiteParserImpl::ParseTransposeConv;</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  m_ParserFunctions[tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM]</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  = &TfLiteParserImpl::ParseUnidirectionalSequenceLSTM;</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  m_ParserFunctions[tflite::BuiltinOperator_UNPACK] = &TfLiteParserImpl::ParseUnpack;</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="comment">// register supported custom operators</span></div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  m_CustomParserFunctions[<span class="stringliteral">"TFLite_Detection_PostProcess"</span>] = &TfLiteParserImpl::ParseDetectionPostProcess;</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span> }</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span> </div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> TfLiteParserImpl::InputTensorInfo(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  <span class="keywordtype">int</span> input)</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span> {</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& subgraphPtr = m_Model->subgraphs[subgraphIndex];</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</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>  uint32_t inputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(operatorPtr->inputs[input]);</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  <span class="keyword">auto</span> search = armnnTfLiteParser::TfLiteParserImpl::m_TensorInfos.find(inputId);</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span> </div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  <span class="keywordflow">if</span> (search != m_TensorInfos.end())</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  {</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  <span class="keywordflow">return</span> m_TensorInfos[inputId];</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  {</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  <span class="keyword">auto</span> tensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">::armnnTfLiteParser::ToTensorInfo</a>(subgraphPtr->tensors[inputId].get());</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  m_TensorInfos.insert({ inputId, tensorInfo });</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  <span class="keywordflow">return</span> tensorInfo;</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> }</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span> </div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> TfLiteParserImpl::OutputTensorInfoFromInputs(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  <span class="keywordtype">int</span> output,</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  std::vector<int> inputs)</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span> {</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& subgraphPtr = m_Model->subgraphs[subgraphIndex];</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span> </div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  uint32_t outputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(operatorPtr->outputs[output]);</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span> </div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  <span class="keyword">auto</span> outputSearch = armnnTfLiteParser::TfLiteParserImpl::m_TensorInfos.find(outputId);</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span> </div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  <span class="keywordflow">if</span> (outputSearch != m_TensorInfos.end())</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="keywordflow">return</span> m_TensorInfos[outputId];</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> </div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& outputTensorPtr = subgraphPtr->tensors[outputId].get();</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> tensor = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">::armnnTfLiteParser::ToTensorInfo</a>(outputTensorPtr, <span class="keyword">true</span>);</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span> </div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  <span class="keywordflow">if</span> (IsDynamic(outputTensorPtr))</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  {</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  <span class="keywordflow">if</span> (inputs.empty())</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  {</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>(); ++i)</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  {</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  inputs.emplace_back(i);</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  }</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  }</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  <span class="keyword">auto</span> inputTensorIds = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  std::vector<armnn::TensorShape> inputShapes;</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="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputs.size(); ++i)</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  {</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  uint32_t inputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(operatorPtr->inputs[inputs[i]]);</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  <span class="keyword">auto</span> search = armnnTfLiteParser::TfLiteParserImpl::m_TensorInfos.find(inputId);</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span> </div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  <span class="keywordflow">if</span> (search != m_TensorInfos.end())</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  {</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  <span class="keyword">auto</span> &inputTensorInfo = m_TensorInfos[inputId];</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  inputShapes.push_back(inputTensorInfo.GetShape());</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  }</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  {</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  <span class="keyword">auto</span> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">::armnnTfLiteParser::ToTensorInfo</a>(subgraphPtr->tensors[inputId].get());</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  m_TensorInfos.insert({ inputId, inputTensorInfo});</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  inputShapes.push_back(inputTensorInfo.GetShape());</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  }</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  }</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  <span class="keyword">const</span> <span class="keyword">auto</span> outputShape = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#aa6e3c075c888e7c16942a468a3aae33c">InferOutputShapes</a>(inputShapes)[output];</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(outputShape);</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>  m_TensorInfos.insert({ outputId, tensor});</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  <span class="keywordflow">return</span> tensor;</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> </div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> TfLiteParserImpl::OutputTensorInfoFromShapes(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  <span class="keywordtype">int</span> output,</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  std::vector<armnn::TensorShape> inputShapes)</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span> {</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& subgraphPtr = m_Model->subgraphs[subgraphIndex];</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span> </div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  uint32_t outputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(operatorPtr->outputs[output]);</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& outputTensorPtr = subgraphPtr->tensors[outputId].get();</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> tensor = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">::armnnTfLiteParser::ToTensorInfo</a>(outputTensorPtr, <span class="keyword">true</span>);</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span> </div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  <span class="keywordflow">if</span> (IsDynamic(outputTensorPtr))</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  {</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  <span class="keyword">const</span> <span class="keyword">auto</span> outputShape = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#aa6e3c075c888e7c16942a468a3aae33c">InferOutputShapes</a>(inputShapes)[output];</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(outputShape);</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  }</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  m_TensorInfos.insert({ outputId, tensor});</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  <span class="keywordflow">return</span> tensor;</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span> }</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span> </div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span> <span class="keywordtype">void</span> TfLiteParserImpl::ResetParser()</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>  m_Network = <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a>(<span class="keyword">nullptr</span>, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span>  m_Model = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span>  m_SubgraphConnections.clear();</div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  m_OverriddenOutputShapes.clear();</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  m_ConstantsToDequantize.clear();</div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  m_ConstantsToBeCreated.clear();</div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  m_TensorInfos.clear();</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span> }</div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span> </div><div class="line"><a name="l00923"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aed935c554e4f6a4e7b9dcde057d00877"> 923</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aed935c554e4f6a4e7b9dcde057d00877">TfLiteParserImpl::CreateNetworkFromBinaryFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile)</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span> {</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  ResetParser();</div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  m_Model = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#af9590c2ae8d68b5da999d3d6ab21dd3b">LoadModelFromFile</a>(graphFile);</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  <span class="keywordflow">return</span> CreateNetworkFromModel();</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span> }</div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span> </div><div class="line"><a name="l00930"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a32a96909bc8a8ee9076bd4d5c1028301"> 930</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a32a96909bc8a8ee9076bd4d5c1028301">TfLiteParserImpl::CreateNetworkFromBinary</a>(<span class="keyword">const</span> std::vector<uint8_t>& binaryContent)</div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span> {</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  ResetParser();</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  m_Model = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a7f3fc56b7306b444f06f45cdc4032bbf">LoadModelFromBinary</a>(binaryContent.data(), binaryContent.size());</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  <span class="keywordflow">return</span> CreateNetworkFromModel();</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span> }</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> </div><div class="line"><a name="l00938"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a0da1e3b0281fa84f8b6f5ce3cd15b7c0"> 938</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a0da1e3b0281fa84f8b6f5ce3cd15b7c0">TfLiteParserImpl::LoadModel</a>(std::unique_ptr<tflite::ModelT> model)</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span> {</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  ResetParser();</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  m_Model = std::move(model);</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span> </div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  <span class="keywordflow">return</span> CreateNetworkFromModel();</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span> }</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span> </div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> TfLiteParserImpl::CreateNetworkFromModel()</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span> {</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span> </div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  <span class="keyword">using</span> <a class="code" href="namespacearmnn.xhtml#a4de71c3661093e5c4ae7775114f43413">NetworkOptions</a> = std::vector<BackendOptions>;</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  <a class="code" href="namespacearmnn.xhtml#a4de71c3661093e5c4ae7775114f43413">NetworkOptions</a> networkOptions = {};</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  <span class="keywordflow">if</span> (m_Options)</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  {</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  <span class="keywordflow">if</span> (m_Options.value().m_InferAndValidate)</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  {</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  <a class="code" href="structarmnn_1_1_backend_options.xhtml">BackendOptions</a> shapeInferenceMethodOption(<span class="stringliteral">"ShapeInferenceMethod"</span>,</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  {</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  { <span class="stringliteral">"InferAndValidate"</span>, <span class="keyword">true</span> }</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  });</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span> </div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  networkOptions.push_back(shapeInferenceMethodOption);</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  }</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  <span class="keywordflow">if</span> (m_Options.value().m_AllowExpandedDims)</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  {</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  <a class="code" href="structarmnn_1_1_backend_options.xhtml">BackendOptions</a> shapeInferenceMethodOption(<span class="stringliteral">"AllowExpandedDims"</span>,</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  {</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  { <span class="stringliteral">"AllowExpandedDims"</span>, <span class="keyword">true</span> }</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  });</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>  networkOptions.push_back(shapeInferenceMethodOption);</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  }</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  }</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  m_Network = INetwork::Create(networkOptions);</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(m_Model.get() != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span> </div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  <span class="keywordflow">if</span> (m_Model->subgraphs.size() != 1)</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  {</div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  fmt::format(<span class="stringliteral">"Current TfLite parser only supports 1 subgraph. Current one has: {} {}"</span>,</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  m_Model->subgraphs.size(),</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  }</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span> </div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  <span class="keywordtype">size_t</span> subgraphIndex = 0;</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  <span class="keywordtype">size_t</span> operatorIndex = 0;</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  {</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  <span class="keywordflow">for</span> (<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a988cb5e216eb87d854414c6a0282eeb4">SubgraphPtr</a> <span class="keyword">const</span>& subgraph : m_Model->subgraphs)</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>  SetupInputLayerTensorInfos(subgraphIndex);</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  SetupConstantLayerTensorInfos(subgraphIndex);</div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span> </div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  m_SubgraphConnections.emplace_back(subgraph->tensors.size());</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  <span class="keywordflow">for</span> (<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aadad81a95152fe5aad839db352d4012c">OperatorPtr</a> <span class="keyword">const</span>& op : subgraph->operators)</div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span>  {</div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span>  <span class="keyword">auto</span> <span class="keyword">const</span>& opCodePtr = m_Model->operator_codes[op->opcode_index];</div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span> </div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span> <span class="comment">// work around the introduction of the deprecated_builtin_code introduced in 2.4 in a backwards compatible manner</span></div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span> <span class="preprocessor">#if defined(ARMNN_POST_TFLITE_2_3)</span></div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  <span class="keyword">auto</span> builtinCode = std::max(opCodePtr->builtin_code,</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  static_cast<tflite::BuiltinOperator>(opCodePtr->deprecated_builtin_code));</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span> <span class="preprocessor">#else</span></div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  <span class="keyword">auto</span> builtinCode = opCodePtr->builtin_code;</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span> </div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  <span class="keywordflow">if</span> (builtinCode > tflite::BuiltinOperator_MAX)</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  {</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(fmt::format(<span class="stringliteral">"Operator code {} is out of range 0-{}. "</span></div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  <span class="stringliteral">"subgraph:{} operator idx:{}. {}"</span>,</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  builtinCode, tflite::BuiltinOperator_MAX, subgraphIndex,</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  operatorIndex, <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  }</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span> </div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  <span class="comment">// lookup and call the parser function</span></div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  <span class="keyword">auto</span>& parserFunction = m_ParserFunctions[builtinCode];</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  (this->*parserFunction)(subgraphIndex, operatorIndex);</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  ++operatorIndex;</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> </div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>  SetupInputLayers(subgraphIndex);</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  SetupOutputLayers(subgraphIndex);</div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>  SetupConstantLayers(subgraphIndex);</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span> </div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  ++subgraphIndex;</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  operatorIndex = 0;</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>  }</div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  }</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</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="l01028"></a><span class="lineno"> 1028</span>  {</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  std::stringstream errorString;</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  errorString << <span class="stringliteral">"Failed to parse operator #"</span> << operatorIndex << <span class="stringliteral">" within subgraph #"</span></div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  << subgraphIndex << <span class="stringliteral">" error: "</span> << e.<a class="code" href="classarmnn_1_1_exception.xhtml#abf843cbb29dec939d0731e491bab6f70">what</a>();</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>) << errorString.str();</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>  std::stringstream errors;</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>  errors << errorString.str() << <span class="stringliteral">"\n"</span>;</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(errors.str());</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  }</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">// establish the connections from the layer outputs to the inputs of the subsequent layers</span></div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>  <span class="keywordflow">for</span> (subgraphIndex = 0; subgraphIndex < m_SubgraphConnections.size(); ++subgraphIndex)</div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>  {</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> tensorIndex = 0; tensorIndex < m_SubgraphConnections[subgraphIndex].size(); ++tensorIndex)</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  {</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  <span class="keywordflow">if</span> (m_SubgraphConnections[subgraphIndex][tensorIndex].outputSlot != <span class="keyword">nullptr</span>)</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="keywordflow">for</span> (<span class="keywordtype">size_t</span> inputSlotIdx = 0;</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>  inputSlotIdx < m_SubgraphConnections[subgraphIndex][tensorIndex].inputSlots.size();</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  ++inputSlotIdx)</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>  {</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>  m_SubgraphConnections[subgraphIndex][tensorIndex].outputSlot->Connect(</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>  *(m_SubgraphConnections[subgraphIndex][tensorIndex].inputSlots[inputSlotIdx]));</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</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>  }</div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  }</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  <span class="keywordflow">return</span> std::move(m_Network);</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span> }</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span> </div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span> <span class="keywordtype">bool</span> TfLiteParserImpl::ShouldConstantTensorBeConverted(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TfLiteParserImpl::TensorRawPtr</a> tensorPtr,</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> inputDataType,</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> tensorDataType)</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span> {</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>  <span class="keywordflow">return</span> (TfLiteParserImpl::IsConstTensor(tensorPtr) && inputDataType == DataType::Float32 &&</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>  (tensorDataType == DataType::QAsymmU8 ||</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  tensorDataType == DataType::QAsymmS8 ||</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  tensorDataType == DataType::QSymmS8 ||</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>  tensorDataType == DataType::Signed32 ||</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>  tensorDataType == DataType::Signed64));</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span> }</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span> </div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span> <span class="keywordtype">void</span> TfLiteParserImpl::RegisterProducerOfTensor(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  <span class="keywordtype">size_t</span> tensorIndex,</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>* slot)</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span> {</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#aa1664dc13adbc85ac12fb584b76bfdae">CHECK_TENSOR</a>(m_Model, subgraphIndex, tensorIndex);</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(m_SubgraphConnections.size() > subgraphIndex);</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(m_SubgraphConnections[subgraphIndex].size() > tensorIndex);</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span> </div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>  TensorSlots & tensorSlots = m_SubgraphConnections[subgraphIndex][tensorIndex];</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> (slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a22b3f11f56aa924c0c1b6ee4d6cc90e1">GetOwningIConnectableLayer</a>().<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">GetType</a>() != LayerType::Constant)</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> </div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  <span class="comment">// assuming there is only one producer for that tensor</span></div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>  <span class="keywordflow">if</span> (tensorSlots.outputSlot != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>  {</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(fmt::format(<span class="stringliteral">"Another layer has already registered itself as the producer of "</span></div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  <span class="stringliteral">"subgraph:{} tensor:{} {}"</span>,</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  subgraphIndex,</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  tensorIndex,</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  }</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  }</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>  tensorSlots.outputSlot = slot;</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span> }</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="keywordtype">void</span> TfLiteParserImpl::RegisterConsumerOfTensor(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  <span class="keywordtype">size_t</span> tensorIndex,</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>  <a class="code" href="classarmnn_1_1_i_input_slot.xhtml">armnn::IInputSlot</a>* slot)</div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span> {</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#aa1664dc13adbc85ac12fb584b76bfdae">CHECK_TENSOR</a>(m_Model, subgraphIndex, tensorIndex);</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(m_SubgraphConnections.size() > subgraphIndex);</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(m_SubgraphConnections[subgraphIndex].size() > tensorIndex);</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span> </div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  TensorSlots& tensorSlots = m_SubgraphConnections[subgraphIndex][tensorIndex];</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>  tensorSlots.inputSlots.push_back(slot);</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span> }</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span> </div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseCustomOperator(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span> {</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span> </div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  <span class="comment">// NOTE: By default we presume the custom operator is not supported</span></div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>  <span class="keyword">auto</span> customParserFunction = &TfLiteParserImpl::ParseUnsupportedOperator;</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span> </div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>  <span class="comment">// Identify custom code defined for custom operator</span></div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& customCode = m_Model->operator_codes[operatorPtr->opcode_index]->custom_code;</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span> </div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>  <span class="comment">// Find parser function that corresponds to custom code (if any)</span></div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>  <span class="keyword">auto</span> iterator = m_CustomParserFunctions.find(customCode);</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  <span class="keywordflow">if</span> (iterator != m_CustomParserFunctions.end())</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>  {</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>  customParserFunction = iterator->second;</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  }</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="comment">// Run parser function</span></div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  (this->*customParserFunction)(subgraphIndex, operatorIndex);</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span> }</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span> </div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseUnsupportedOperator(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span> {</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span> </div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span> </div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  <span class="keyword">auto</span> opcodeIndex = operatorPtr->opcode_index;</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span> </div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span> <span class="comment">// work around the introduction of the deprecated_builtin_code introduced in 2.4 in a backwards compatible manner</span></div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span> <span class="preprocessor">#if defined(ARMNN_POST_TFLITE_2_3)</span></div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  <span class="keyword">auto</span> opcode = std::max(m_Model->operator_codes[opcodeIndex]->builtin_code,</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  static_cast<tflite::BuiltinOperator>(m_Model->operator_codes[opcodeIndex]->deprecated_builtin_code));</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span> <span class="preprocessor">#else</span></div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  <span class="keyword">auto</span> opcode = m_Model->operator_codes[opcodeIndex]->builtin_code;</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span> </div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  <span class="keywordflow">if</span> (!m_Options || !m_Options.value().m_StandInLayerForUnsupported)</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  {</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  <span class="comment">// Do not add StandInLayer, throw ParseException instead</span></div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>  fmt::format(<span class="stringliteral">"Operator not supported. "</span></div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>  <span class="stringliteral">"subgraph:{} operator:{} "</span></div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  <span class="stringliteral">"opcode_index:{} opcode:{} / {} {}"</span>,</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  subgraphIndex,</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  operatorIndex,</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  opcodeIndex,</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  opcode,</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  tflite::EnumNameBuiltinOperator(opcode),</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  }</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span> </div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</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="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numInputs = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputs.size());</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numOutputs = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputs.size());</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span> </div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  <a class="code" href="structarmnn_1_1_stand_in_descriptor.xhtml">StandInDescriptor</a> descriptor(numInputs, numOutputs);</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"StandIn:{}:{}:{}"</span>, subgraphIndex, operatorIndex, opcode);</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span> </div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  <span class="comment">// Add a non-executable StandInLayer as a placeholder for any unsupported operator</span></div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddStandInLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</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">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0u; i < numOutputs; ++i)</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>  layer-><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="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(outputs[0], <span class="keyword">true</span>));</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  }</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span> </div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  <span class="keyword">auto</span> inputTensorIds = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  <span class="keyword">auto</span> outputTensorIds = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span> </div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, inputTensorIds);</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIds);</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span> }</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span> </div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseCast(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span> {</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span> </div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</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>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Cast:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span> </div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddCastLayer(layerName.c_str());</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span> </div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span> </div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span> </div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span> }</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span> </div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseConv2D(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span> {</div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</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="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsConv2DOptions();</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span> </div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a>(options, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span> </div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span> </div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> desc;</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  inputs.size() == 3 ?</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = true : desc.m_BiasEnabled = <span class="keyword">false</span>;</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  desc.m_StrideX = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->stride_w);</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>  desc.m_StrideY = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->stride_h);</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>  desc.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>  desc.m_DilationX = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->dilation_w_factor);</div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>  desc.m_DilationY = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->dilation_h_factor);</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span> </div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> filterTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</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>  <span class="comment">// assuming input is NHWC</span></div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span> </div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>  <span class="comment">// assuming the filter is OHWI : Output, H, W, Input</span></div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  <span class="comment">// which is essentially the same as NHWC</span></div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> filterHeight = filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> filterWidth = filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span> </div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  CalcPadding(inputHeight, filterHeight, desc.m_StrideY,</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>  desc.m_DilationY, desc.m_PadTop, desc.m_PadBottom, options->padding);</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>  CalcPadding(inputWidth, filterWidth, desc.m_StrideX,</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>  desc.m_DilationX, desc.m_PadLeft, desc.m_PadRight, options->padding);</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span> </div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>  <span class="comment">// Add the first input and weights tensor to the registration list.</span></div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  <span class="comment">// The constant weights will be added by SetupConstantLayers.</span></div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>  std::vector<unsigned int> tensorIndexesToRegister = { inputTensorIndexes[0], inputTensorIndexes[1] };</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span> </div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Conv2D:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer = m_Network->AddConvolution2dLayer(desc, layerName.c_str());</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span> </div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  <span class="keywordflow">if</span> (ShouldConstantTensorBeConverted(inputs[1], inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>(), filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()))</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>  {</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>  m_ConstantsToDequantize.emplace_back(inputs[1]->buffer);</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>  }</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span> </div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>  <span class="keywordflow">if</span> (desc.m_BiasEnabled)</div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  {</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 2);</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>  <span class="comment">// Add the biases input to the registration list, a constant layer will be added by SetupConstantLayers.</span></div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  tensorIndexesToRegister.emplace_back(inputTensorIndexes[2]);</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>  <span class="keywordflow">if</span> (ShouldConstantTensorBeConverted(inputs[2], inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>(), biasTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()))</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>  {</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>  m_ConstantsToDequantize.emplace_back(inputs[2]->buffer);</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>  }</div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  }</div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span> </div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span> </div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});</div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</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="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, tensorIndexesToRegister);</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span> </div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>  layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function);</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, { outputTensorIndexes[0] });</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> </div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span> <span class="comment">// Conv3D support was added in TF 2.5, so for backwards compatibility a hash define is needed.</span></div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span> <span class="preprocessor">#if defined(ARMNN_POST_TFLITE_2_4)</span></div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseConv3D(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span> {</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span> </div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsConv3DOptions();</div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span> </div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a>(options, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span> </div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>  <a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml">Convolution3dDescriptor</a> desc;</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>  desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>  desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a4dd0194b114cbf51da5b3a72569863ef">armnn::DataLayout::NDHWC</a>;</div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>  desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->stride_w);</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>  desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->stride_h);</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>  desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a5164336f6a1b15be0d434a6bbf7289da">m_StrideZ</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->stride_d);</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>  desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->dilation_w_factor);</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>  desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->dilation_h_factor);</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>  desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a16543bce17aa2e4d6e81c88f74227192">m_DilationZ</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->dilation_d_factor);</div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span> </div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2, 3);</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span> </div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span> </div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> filterTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span> </div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>  <span class="comment">// Assuming input is NDHWC</span></div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputDepth = inputTensorInfo.GetShape()[1];</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = inputTensorInfo.GetShape()[2];</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = inputTensorInfo.GetShape()[3];</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span> </div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>  <span class="comment">// Assuming the filter is DHWIO : Depth, Height, Width, OutputChannels, InputChannels</span></div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> filterDepth = filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0];</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> filterHeight = filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> filterWidth = filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span> </div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>  CalcPadding(inputDepth, filterDepth, desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a5164336f6a1b15be0d434a6bbf7289da">m_StrideZ</a>,</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>  desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a16543bce17aa2e4d6e81c88f74227192">m_DilationZ</a>, desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a83ca447892f460dabaa2f87d3dc3db61">m_PadFront</a>, desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a11d5c25face9b54e90f79ee8bdc1d0fb">m_PadBack</a>, options->padding);</div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>  CalcPadding(inputHeight, filterHeight, desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>,</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>  desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a>, desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>, desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>, options->padding);</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>  CalcPadding(inputWidth, filterWidth, desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>,</div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>  desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a>, desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>, desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>, options->padding);</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span> </div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>  <span class="keyword">auto</span> filterTensorAndData = CreateConstTensorNonPermuted(inputs[1], filterTensorInfo, inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>());</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span> </div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Conv3D:{}:{}"</span>, subgraphIndex, operatorIndex);</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>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span>  <span class="comment">// Add the first input and weights tensor to the registration list.</span></div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>  <span class="comment">// The constant weights will be added by SetupConstantLayers.</span></div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>  std::vector<unsigned int> tensorIndexesToRegister = {inputTensorIndexes[0], inputTensorIndexes[1]};</div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span> </div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span>  <span class="keywordflow">if</span> (inputs.size() == 3)</div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>  {</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>  desc.<a class="code" href="structarmnn_1_1_convolution3d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span> </div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>  <span class="comment">// Add the biases input to the registration list, a constant layer will be added by SetupConstantLayers.</span></div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>  tensorIndexesToRegister.emplace_back(inputTensorIndexes[2]);</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>  }</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span> </div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer = m_Network->AddConvolution3dLayer(desc, layerName.c_str());</div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span> </div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span> </div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>  <span class="comment">// Register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, tensorIndexesToRegister);</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>  layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function);</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>  <span class="comment">// Register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span> }</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span> </div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseDepthwiseConv2D(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span> {</div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span> </div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsDepthwiseConv2DOptions();</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="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a>(options, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span> </div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> desc;</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->stride_w);</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->stride_h);</div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->depth_multiplier);</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span> </div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2, 3);</div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>  <span class="keywordflow">if</span> (inputs.size() == 3)</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>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>  }</div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span> </div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->dilation_w_factor);</div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->dilation_h_factor);</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> filterTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span> </div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>  <span class="comment">// Assuming input is NHWC</span></div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</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>  <span class="comment">// TensorflowLite weights come in the format [1, H, W, I * M]</span></div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> filterHeight = filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> filterWidth = filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span> </div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  CalcPadding(inputHeight, filterHeight, desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>,</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a>, desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>, desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>, options->padding);</div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span>  CalcPadding(inputWidth, filterWidth, desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>,</div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a>, desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>, desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>, options->padding);</div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span> </div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span>  <span class="comment">// ArmNN uses the same filter tensor layout at TfLite [1, H, W, O] no need for any permutation</span></div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"DepthwiseConv2D:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span> </div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>  <span class="comment">// Add the first input and weights tensor to the registration list.</span></div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>  <span class="comment">// The constant weights will be added by SetupConstantLayers.</span></div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>  std::vector<unsigned int> tensorIndexesToRegister = {inputTensorIndexes[0], inputTensorIndexes[1]};</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span> </div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer = m_Network->AddDepthwiseConvolution2dLayer(desc, layerName.c_str());</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span> </div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>  <span class="keywordflow">if</span> (desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>  {</div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 2);</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span> </div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>  <span class="comment">// Add the biases input to the registration list, a constant layer will be added by SetupConstantLayers.</span></div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>  tensorIndexesToRegister.emplace_back(inputTensorIndexes[2]);</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>  }</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span> </div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});</div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span> </div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, tensorIndexesToRegister);</div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span> </div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>  layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function);</div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span> }</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="keywordtype">void</span> TfLiteParserImpl::ParseDequantize(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span> {</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span> </div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</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>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span> </div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Dequantize:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span> </div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddDequantizeLayer(layerName.c_str());</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span> </div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[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>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</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> </div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseExpandDims(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span> {</div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span> </div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span> </div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</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>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"ExpandDims:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span> </div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(outputs[0], <span class="keyword">true</span>);</div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span> </div><div class="line"><a name="l01486"></a><span class="lineno"> 1486</span>  CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, <span class="stringliteral">"Input 0"</span>, <span class="stringliteral">"Output 0"</span>);</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span> </div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">ReshapeDescriptor</a> reshapeDesc;</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span> </div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>  <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>().<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#acccb75bd1d68a81f6ddd61687f51c5a1">AreAllDimensionsSpecified</a>())</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>  reshapeDesc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span>  {</div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>  int32_t axis = inputs[1]->shape[0];</div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span> </div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>  int32_t inputDimSize = <span class="keyword">static_cast<</span>int32_t<span class="keyword">></span>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>().<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>());</div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span> </div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span>  <span class="keywordflow">if</span> (axis > inputDimSize || axis < 0 - (inputDimSize + 1))</div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>  {</div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"axis must be in range [0 - (inputDimSize + 1), inputDimSize] inclusive"</span>);</div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span>  }</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>  <span class="keywordflow">if</span>(axis < 0)</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>  axis = inputDimSize + axis + 1;</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> </div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>  std::vector<unsigned int> shape(static_cast<unsigned int>(inputDimSize) + 1);</div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShapeIndex = 0;</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < static_cast<unsigned int>(inputDimSize + 1); ++i)</div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span>  {</div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span>  <span class="keywordflow">if</span> (i == static_cast<unsigned int>(axis))</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>  shape[i] = 1;</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>  shape[i] = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[inputShapeIndex];</div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span>  ++inputShapeIndex;</div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span>  }</div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>  }</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>  reshapeDesc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(static_cast<unsigned int>(inputDimSize + 1), shape.data());</div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span>  }</div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span> </div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str());</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</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>  reshapeDesc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0}).GetShape();</div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(reshapeDesc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a>);</div><div class="line"><a name="l01533"></a><span class="lineno"> 1533</span> </div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>  layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span> </div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span> </div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</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> </div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseTranspose(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span> {</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span> </div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1, 2);</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="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span> </div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Transpose:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span>  <a class="code" href="structarmnn_1_1_transpose_descriptor.xhtml">TransposeDescriptor</a> desc;</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>  <span class="keywordflow">if</span> (inputs.size() == 2)</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> permuteTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> permuteBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>  <span class="keyword">auto</span> numPermVecElements = permuteTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>();</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>  std::vector<unsigned int> permuteShape(numPermVecElements);</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>  ::memcpy(permuteShape.data(), permuteBufferPtr->data.data(), permuteTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>  <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> permutationVector(permuteShape.data(), permuteTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span> </div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>  desc = <a class="code" href="structarmnn_1_1_transpose_descriptor.xhtml">TransposeDescriptor</a>(permutationVector);</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>  }</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddTransposeLayer(desc, layerName.c_str());</div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span> </div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>  CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, <span class="stringliteral">"Input 0"</span>, <span class="stringliteral">"Output 0"</span>);</div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span> </div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span> </div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span> }</div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</span> </div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseTransposeConv(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span> {</div><div class="line"><a name="l01585"></a><span class="lineno"> 1585</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span> </div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsTransposeConvOptions();</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>  <a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">TransposeConvolution2dDescriptor</a> desc;</div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->stride_w);</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->stride_h);</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span> </div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>  <span class="keywordflow">if</span> (inputs.size() == 4)</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>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>  }</div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span>  <span class="keywordflow">else</span></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>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 3);</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">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span> </div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span>  <span class="keywordflow">if</span> (inputs[0])</div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>  {</div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l01612"></a><span class="lineno"> 1612</span>  std::vector<int> output_shape(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l01613"></a><span class="lineno"> 1613</span> </div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>  <span class="keywordflow">if</span> (tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == DataType::Signed32)</div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>  {</div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span>  ::memcpy(output_shape.data(), <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[0]->buffer)->data.data(), tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</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="keywordflow">if</span> (tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == DataType::QAsymmU8)</div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span>  {</div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i=0; i < tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(); i++)</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>  output_shape[i] = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[0]->buffer)->data.data()[i];</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>  }</div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span>  }</div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>  <span class="comment">// Change from signed to unsigned int to store in TransposeConvolution2dDescriptor.</span></div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> dimension : output_shape)</div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>  {</div><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a78f5b213a603b55d0fabea389e89f203">m_OutputShape</a>.push_back(static_cast<unsigned int>(dimension));</div><div class="line"><a name="l01629"></a><span class="lineno"> 1629</span>  }</div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a9bc46a2ad56319786bad30be9ab2dc6a">m_OutputShapeEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>  }</div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 2);</div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> filterTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</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>  <span class="comment">// TfLite uses NHWC tensors</span></div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span> </div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> filterHeight = filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> filterWidth = filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</span> </div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span>  CalcPadding(inputHeight,</div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span>  filterHeight,</div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>,</div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span>  1, <span class="comment">// DilationY</span></div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>,</div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>,</div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>  options->padding);</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span> </div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>  CalcPadding(inputWidth,</div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>  filterWidth,</div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>,</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span>  1, <span class="comment">// DilationX</span></div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>,</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>,</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span>  options->padding);</div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span> </div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>  <span class="keyword">auto</span> filterTensorAndData = CreateConstTensorNonPermuted(inputs[1], filterTensorInfo, inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>());</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span> </div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"TransposeConv:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span> </div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</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="l01664"></a><span class="lineno"> 1664</span>  {</div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>  <span class="keyword">auto</span> biasTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 3);</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span>  <span class="keyword">auto</span> biasConstTensor = CreateConstTensorNonPermuted(inputs[3], biasTensorInfo, inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>());</div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span>  layer = m_Network->AddTransposeConvolution2dLayer(desc,</div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span>  filterTensorAndData.first,</div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span>  biasConstTensor.first,</div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span>  layerName.c_str());</div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span>  }</div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span>  <span class="keywordflow">else</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>  layer = m_Network->AddTransposeConvolution2dLayer(desc,</div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>  filterTensorAndData.first,</div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span>  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>(),</div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>  layerName.c_str());</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> </div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0 , { 2, 1 });</div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>  layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span> </div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const (filter) tensor</span></div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[2]});</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>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</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> </div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseAveragePool2D(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span> {</div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span>  ParsePool(subgraphIndex, operatorIndex, PoolingAlgorithm::Average);</div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span> }</div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span> </div><div class="line"><a name="l01698"></a><span class="lineno"> 1698</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseBatchMatMul(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</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>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</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>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span> </div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span> </div><div class="line"><a name="l01708"></a><span class="lineno"> 1708</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"BatchMatMul:{}:{}"</span>, subgraphIndex, operatorIndex);</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputXTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l01711"></a><span class="lineno"> 1711</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputYTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span> </div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsBatchMatMulOptions();</div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span> </div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>  <span class="comment">// Adjoint in tensorflow lite performs transpose operation</span></div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>  <a class="code" href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml">BatchMatMulDescriptor</a> descriptor(options->adj_x,</div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span>  options->adj_y,</div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>  <span class="keyword">false</span>,</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>  <span class="keyword">false</span>);</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>  <span class="comment">// Arbitrary DataLayout</span></div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span> </div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddBatchMatMulLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span> </div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span> </div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span> </div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</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> <span class="keywordtype">void</span> TfLiteParserImpl::ParseBatchToSpaceND(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span> {</div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span> </div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 3);</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>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> blockShapeTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> blockShapeBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[1]->buffer);</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cropsTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 2);</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> cropsBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[2]->buffer);</div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span> </div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span>  std::vector<unsigned int> blockShape(blockShapeTensorInfo.GetNumElements());</div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span>  ::memcpy(blockShape.data(), blockShapeBufferPtr->data.data(), blockShapeTensorInfo.GetNumBytes());</div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span> </div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>  std::vector<unsigned int> cropsVector(cropsTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span>  ::memcpy(cropsVector.data(), cropsBufferPtr->data.data(), cropsTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span> </div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span>  <span class="keywordtype">size_t</span> step = 2;</div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>  std::vector<std::pair<unsigned int, unsigned int>> crops;</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < cropsTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / step; ++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>  crops.emplace_back(cropsVector[i * step], cropsVector[i * step + 1]);</div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>  }</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span> </div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>  <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">armnn::BatchToSpaceNdDescriptor</a> desc;</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span>  desc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">m_BlockShape</a> = blockShape;</div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span>  desc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a3941f674c071c9503e00d2b59e92e454">m_Crops</a> = crops;</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>  desc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span> </div><div class="line"><a name="l01770"></a><span class="lineno"> 1770</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"BatchToSpaceND:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span> </div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span> </div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddBatchToSpaceNdLayer(desc, layerName.c_str());</div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span> </div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>  CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, <span class="stringliteral">"Input 0"</span>, <span class="stringliteral">"Output 0"</span>);</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span> </div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span> </div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span> }</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span> </div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseL2Normalization(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span> {</div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span> </div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span> </div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span> </div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span>  <a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml">L2NormalizationDescriptor</a> desc;</div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span>  desc.<a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l01800"></a><span class="lineno"> 1800</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"L2Normalization:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddL2NormalizationLayer(desc, layerName.c_str());</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>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01804"></a><span class="lineno"> 1804</span> </div><div class="line"><a name="l01805"></a><span class="lineno"> 1805</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</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">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span> </div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</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="keywordtype">void</span> TfLiteParserImpl::ParseMaxPool2D(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span> {</div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span>  ParsePool(subgraphIndex, operatorIndex, PoolingAlgorithm::Max);</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> </div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseMaximum(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span> {</div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</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>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span> </div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01828"></a><span class="lineno"> 1828</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</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>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Maximum:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01831"></a><span class="lineno"> 1831</span> </div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input1TensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>  CheckMatchingQuantization(inputTensorInfo, input1TensorInfo, layerName, <span class="stringliteral">"Input 0"</span>, <span class="stringliteral">"Input 1"</span>);</div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span> </div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddMaximumLayer(layerName.c_str());</div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01838"></a><span class="lineno"> 1838</span> </div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});</div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>  CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, <span class="stringliteral">"Input 0"</span>, <span class="stringliteral">"Output 0"</span>);</div><div class="line"><a name="l01841"></a><span class="lineno"> 1841</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</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="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01844"></a><span class="lineno"> 1844</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l01845"></a><span class="lineno"> 1845</span> </div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01848"></a><span class="lineno"> 1848</span> }</div><div class="line"><a name="l01849"></a><span class="lineno"> 1849</span> </div><div class="line"><a name="l01850"></a><span class="lineno"> 1850</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseMinimum(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</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>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span> </div><div class="line"><a name="l01854"></a><span class="lineno"> 1854</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01855"></a><span class="lineno"> 1855</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span> </div><div class="line"><a name="l01857"></a><span class="lineno"> 1857</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</span> </div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Minimum:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span> </div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input1TensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span>  CheckMatchingQuantization(inputTensorInfo, input1TensorInfo, layerName, <span class="stringliteral">"Input 0"</span>, <span class="stringliteral">"Input 1"</span>);</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddMinimumLayer(layerName.c_str());</div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</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_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});</div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>  CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, <span class="stringliteral">"Input 0"</span>, <span class="stringliteral">"Output 0"</span>);</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span> </div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l01875"></a><span class="lineno"> 1875</span> </div><div class="line"><a name="l01876"></a><span class="lineno"> 1876</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01877"></a><span class="lineno"> 1877</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01878"></a><span class="lineno"> 1878</span> }</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> <span class="keywordtype">void</span> TfLiteParserImpl::ParsePool(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l01881"></a><span class="lineno"> 1881</span>  <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l01882"></a><span class="lineno"> 1882</span>  <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718">PoolingAlgorithm</a> algorithm)</div><div class="line"><a name="l01883"></a><span class="lineno"> 1883</span> {</div><div class="line"><a name="l01884"></a><span class="lineno"> 1884</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</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="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsPool2DOptions();</div><div class="line"><a name="l01888"></a><span class="lineno"> 1888</span> </div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a>(options, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span> </div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span>  std::string layerName;</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>  <span class="keywordflow">switch</span> (algorithm)</div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>  {</div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>  <span class="keywordflow">case</span> PoolingAlgorithm::Average:</div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span>  layerName =</div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span>  fmt::format(<span class="stringliteral">"AveragePool2D:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01898"></a><span class="lineno"> 1898</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span>  <span class="keywordflow">case</span> PoolingAlgorithm::Max:</div><div class="line"><a name="l01900"></a><span class="lineno"> 1900</span>  layerName =</div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>  fmt::format(<span class="stringliteral">"MaxPool2D:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(<span class="keyword">false</span>, <span class="stringliteral">"Unsupported Pooling Algorithm"</span>);</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>  }</div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span> </div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> desc;</div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span> </div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = algorithm;</div><div class="line"><a name="l01910"></a><span class="lineno"> 1910</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->stride_w);</div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->stride_h);</div><div class="line"><a name="l01912"></a><span class="lineno"> 1912</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->filter_width);</div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->filter_height);</div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = PaddingMethod::Exclude;</div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#affb5b68b3eba3ed45a06c7cde7781962">m_OutputShapeRounding</a> = OutputShapeRounding::Floor;</div><div class="line"><a name="l01916"></a><span class="lineno"> 1916</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l01917"></a><span class="lineno"> 1917</span> </div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</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>  <span class="comment">// assuming input is NHWC</span></div><div class="line"><a name="l01923"></a><span class="lineno"> 1923</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = inputTensorInfo.GetShape()[1];</div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = inputTensorInfo.GetShape()[2];</div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span> </div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span>  CalcPadding(inputHeight, desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a>, desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>, 1u,</div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>, desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>, options->padding);</div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span>  CalcPadding(inputWidth, desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a>, desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>, 1u,</div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>, desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>, options->padding);</div><div class="line"><a name="l01930"></a><span class="lineno"> 1930</span> </div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01933"></a><span class="lineno"> 1933</span> </div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddPooling2dLayer(desc, layerName.c_str());</div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span> </div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>  CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, <span class="stringliteral">"Input 0"</span>, <span class="stringliteral">"Output 0"</span>);</div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>  layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);</div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span> </div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l01943"></a><span class="lineno"> 1943</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span> </div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>  layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function);</div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</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"> 1952</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseSlice(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span> {</div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span> </div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01957"></a><span class="lineno"> 1957</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 3);</div><div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</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>  <a class="code" href="structarmnn_1_1_slice_descriptor.xhtml">SliceDescriptor</a> desc;</div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span> </div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>  <span class="comment">// set begin tensor info for slice descriptor</span></div><div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> beginTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> beginBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[1]->buffer);</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>  std::vector<unsigned int> begin(beginTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l01968"></a><span class="lineno"> 1968</span>  ::memcpy(begin.data(), beginBufferPtr->data.data(), beginTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span> </div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>  <span class="comment">// set size tensor info for slice descriptor</span></div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> sizeTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 2);</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> sizeBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[2]->buffer);</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>  std::vector<int> signedSize(sizeTensorInfo.GetNumElements(), 1);</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="comment">// if size buffer data is not specified, all contents of size vector remain as values of 1</span></div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>  <span class="keywordflow">if</span> (sizeBufferPtr->data.data())</div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>  {</div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>  ::memcpy(signedSize.data(), sizeBufferPtr->data.data(), sizeTensorInfo.GetNumBytes());</div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>  }</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span> </div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>  std::vector<unsigned int> size(sizeTensorInfo.GetNumElements());</div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</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="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < signedSize.size(); ++i)</div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span>  {</div><div class="line"><a name="l01987"></a><span class="lineno"> 1987</span>  <span class="keywordtype">int</span> signedValue = signedSize[i];</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="keywordflow">if</span> (signedValue < -1 || signedValue > static_cast<int>(inputTensorInfo.GetShape()[i] - begin[i]))</div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>  {</div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(fmt::format(<span class="stringliteral">"Invalid value for size {} size must be in range "</span></div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>  <span class="stringliteral">"[-1, inputDimSize - begin] [-1, {}] inclusive {}"</span>,</div><div class="line"><a name="l01993"></a><span class="lineno"> 1993</span>  signedValue,</div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>  inputTensorInfo.GetShape()[i] - begin[i],</div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>  <span class="keywordflow">if</span> (signedValue == -1)</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>  size[i] = inputTensorInfo.GetShape()[i] - begin[i];</div><div class="line"><a name="l02001"></a><span class="lineno"> 2001</span>  }</div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l02003"></a><span class="lineno"> 2003</span>  {</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>  size[i] = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(signedValue);</div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span>  }</div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>  }</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>  desc = <a class="code" href="structarmnn_1_1_slice_descriptor.xhtml">SliceDescriptor</a>(begin, size);</div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span> </div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Slice:{}:{}"</span>, subgraphIndex, operatorIndex);</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network->AddSliceLayer(desc, layerName.c_str());</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span> </div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>  CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, <span class="stringliteral">"Input 0"</span>, <span class="stringliteral">"Output 0"</span>);</div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span> </div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</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>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</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> </div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseSoftmax(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span> {</div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsSoftmaxOptions();</div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span> </div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span>  <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">SoftmaxDescriptor</a> desc;</div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>  desc.<a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = options->beta;</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span> </div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span> </div><div class="line"><a name="l02042"></a><span class="lineno"> 2042</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Softmax:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02043"></a><span class="lineno"> 2043</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network->AddSoftmaxLayer(desc, layerName.c_str());</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span> </div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span> </div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l02054"></a><span class="lineno"> 2054</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span> }</div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span> </div><div class="line"><a name="l02058"></a><span class="lineno"> 2058</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseLogSoftmax(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02059"></a><span class="lineno"> 2059</span> {</div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02061"></a><span class="lineno"> 2061</span> </div><div class="line"><a name="l02062"></a><span class="lineno"> 2062</span>  <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">LogSoftmaxDescriptor</a> desc;</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>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02065"></a><span class="lineno"> 2065</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l02066"></a><span class="lineno"> 2066</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02067"></a><span class="lineno"> 2067</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span> </div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"LogSoftmax:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network->AddLogSoftmaxLayer(desc, layerName.c_str());</div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span> </div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span> </div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l02076"></a><span class="lineno"> 2076</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l02077"></a><span class="lineno"> 2077</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span> </div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span> }</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="keywordtype">void</span> TfLiteParserImpl::ParseSpaceToBatchND(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span> {</div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span> </div><div class="line"><a name="l02089"></a><span class="lineno"> 2089</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02090"></a><span class="lineno"> 2090</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 3);</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>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span> </div><div class="line"><a name="l02095"></a><span class="lineno"> 2095</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> blockShapeTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> blockShapeBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l02097"></a><span class="lineno"> 2097</span> </div><div class="line"><a name="l02098"></a><span class="lineno"> 2098</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> padListTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 2);</div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> padListBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[2]->buffer);</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>  std::vector<unsigned int> blockShape(blockShapeTensorInfo.GetNumElements());</div><div class="line"><a name="l02102"></a><span class="lineno"> 2102</span>  ::memcpy(blockShape.data(), blockShapeBufferPtr->data.data(), blockShapeTensorInfo.GetNumBytes());</div><div class="line"><a name="l02103"></a><span class="lineno"> 2103</span> </div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span>  std::vector<unsigned int> padListVector(padListTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l02105"></a><span class="lineno"> 2105</span>  ::memcpy(padListVector.data(), padListBufferPtr->data.data(), padListTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l02106"></a><span class="lineno"> 2106</span> </div><div class="line"><a name="l02107"></a><span class="lineno"> 2107</span>  <span class="keywordtype">size_t</span> step = 2;</div><div class="line"><a name="l02108"></a><span class="lineno"> 2108</span>  std::vector<std::pair<unsigned int, unsigned int>> padList;</div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < padListTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / step; ++i)</div><div class="line"><a name="l02110"></a><span class="lineno"> 2110</span>  {</div><div class="line"><a name="l02111"></a><span class="lineno"> 2111</span>  padList.emplace_back(padListVector[i * step], padListVector[i * step + 1]);</div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span>  }</div><div class="line"><a name="l02113"></a><span class="lineno"> 2113</span> </div><div class="line"><a name="l02114"></a><span class="lineno"> 2114</span>  <a class="code" href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml">armnn::SpaceToBatchNdDescriptor</a> desc;</div><div class="line"><a name="l02115"></a><span class="lineno"> 2115</span>  desc.<a class="code" href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">m_BlockShape</a> = blockShape;</div><div class="line"><a name="l02116"></a><span class="lineno"> 2116</span>  desc.<a class="code" href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml#a85f98c94e11f65a6b73f831735c040f3">m_PadList</a> = padList;</div><div class="line"><a name="l02117"></a><span class="lineno"> 2117</span>  desc.<a class="code" href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l02118"></a><span class="lineno"> 2118</span> </div><div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"SpaceToBatchND:{}:{}"</span>, subgraphIndex, operatorIndex);</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l02122"></a><span class="lineno"> 2122</span> </div><div class="line"><a name="l02123"></a><span class="lineno"> 2123</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddSpaceToBatchNdLayer(desc, layerName.c_str());</div><div class="line"><a name="l02124"></a><span class="lineno"> 2124</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02125"></a><span class="lineno"> 2125</span> </div><div class="line"><a name="l02126"></a><span class="lineno"> 2126</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l02127"></a><span class="lineno"> 2127</span>  CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, <span class="stringliteral">"Input 0"</span>, <span class="stringliteral">"Output 0"</span>);</div><div class="line"><a name="l02128"></a><span class="lineno"> 2128</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</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">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02131"></a><span class="lineno"> 2131</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02132"></a><span class="lineno"> 2132</span> </div><div class="line"><a name="l02133"></a><span class="lineno"> 2133</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02134"></a><span class="lineno"> 2134</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02135"></a><span class="lineno"> 2135</span> }</div><div class="line"><a name="l02136"></a><span class="lineno"> 2136</span> </div><div class="line"><a name="l02137"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a7d28f3a07c7ab30f66e7c70ded86eb49"> 2137</a></span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a7d28f3a07c7ab30f66e7c70ded86eb49">TfLiteParserImpl::OutputShapeOfSqueeze</a>(std::vector<uint32_t> squeezeDims,</div><div class="line"><a name="l02138"></a><span class="lineno"> 2138</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& inputTensorInfo)</div><div class="line"><a name="l02139"></a><span class="lineno"> 2139</span> {</div><div class="line"><a name="l02140"></a><span class="lineno"> 2140</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(squeezeDims.size(), 0, 1, 2, 3, 4);</div><div class="line"><a name="l02141"></a><span class="lineno"> 2141</span>  <span class="keyword">static</span> <span class="keyword">const</span> uint32_t dimensionSequence[] = { 0, 1, 2, 3 };</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>  <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() > 4)</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>  std::stringstream ss;</div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>  ss << <span class="stringliteral">"Input tensor has unexpected number of dimensions:"</span> << inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>  << <span class="stringliteral">" shape:"</span> << inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() << <span class="stringliteral">" "</span></div><div class="line"><a name="l02148"></a><span class="lineno"> 2148</span>  << <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString();</div><div class="line"><a name="l02149"></a><span class="lineno"> 2149</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(ss.str());</div><div class="line"><a name="l02150"></a><span class="lineno"> 2150</span>  }</div><div class="line"><a name="l02151"></a><span class="lineno"> 2151</span> </div><div class="line"><a name="l02152"></a><span class="lineno"> 2152</span>  <span class="keywordflow">if</span> (squeezeDims.empty())</div><div class="line"><a name="l02153"></a><span class="lineno"> 2153</span>  {</div><div class="line"><a name="l02154"></a><span class="lineno"> 2154</span>  squeezeDims.assign(dimensionSequence,</div><div class="line"><a name="l02155"></a><span class="lineno"> 2155</span>  dimensionSequence+inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>());</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span>  }</div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span> </div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span>  std::vector<uint32_t> outputDims;</div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); i++)</div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span>  {</div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span>  <span class="keywordtype">bool</span> skipSqueeze = (std::find(squeezeDims.begin(), squeezeDims.end(), i) == squeezeDims.end());</div><div class="line"><a name="l02162"></a><span class="lineno"> 2162</span>  <span class="keyword">auto</span> currentDimension = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i];</div><div class="line"><a name="l02163"></a><span class="lineno"> 2163</span>  <span class="keywordflow">if</span> (skipSqueeze || currentDimension != 1)</div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span>  {</div><div class="line"><a name="l02165"></a><span class="lineno"> 2165</span>  outputDims.push_back(currentDimension);</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> </div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span>  <span class="keywordflow">if</span> (outputDims.size() > 4)</div><div class="line"><a name="l02170"></a><span class="lineno"> 2170</span>  {</div><div class="line"><a name="l02171"></a><span class="lineno"> 2171</span>  std::stringstream ss;</div><div class="line"><a name="l02172"></a><span class="lineno"> 2172</span>  ss << <span class="stringliteral">"Output tensor has unexpected number of dimensions:"</span> << inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()</div><div class="line"><a name="l02173"></a><span class="lineno"> 2173</span>  << <span class="stringliteral">" shape:"</span> << inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() << <span class="stringliteral">" "</span></div><div class="line"><a name="l02174"></a><span class="lineno"> 2174</span>  << <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString();</div><div class="line"><a name="l02175"></a><span class="lineno"> 2175</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(ss.str());</div><div class="line"><a name="l02176"></a><span class="lineno"> 2176</span>  }</div><div class="line"><a name="l02177"></a><span class="lineno"> 2177</span> </div><div class="line"><a name="l02178"></a><span class="lineno"> 2178</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outShape = <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(static_cast<unsigned int>(outputDims.size()),</div><div class="line"><a name="l02179"></a><span class="lineno"> 2179</span>  outputDims.data());</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="comment">// we need to preserve the tensor type and the quantization data as well</span></div><div class="line"><a name="l02182"></a><span class="lineno"> 2182</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outTensorInfo = inputTensorInfo;</div><div class="line"><a name="l02183"></a><span class="lineno"> 2183</span>  outTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(outShape);</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>  <span class="keywordflow">return</span> outTensorInfo;</div><div class="line"><a name="l02186"></a><span class="lineno"> 2186</span> }</div><div class="line"><a name="l02187"></a><span class="lineno"> 2187</span> </div><div class="line"><a name="l02188"></a><span class="lineno"> 2188</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseShape(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02189"></a><span class="lineno"> 2189</span> {</div><div class="line"><a name="l02190"></a><span class="lineno"> 2190</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02191"></a><span class="lineno"> 2191</span> </div><div class="line"><a name="l02192"></a><span class="lineno"> 2192</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02193"></a><span class="lineno"> 2193</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l02194"></a><span class="lineno"> 2194</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02195"></a><span class="lineno"> 2195</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</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="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Shape:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02198"></a><span class="lineno"> 2198</span> </div><div class="line"><a name="l02199"></a><span class="lineno"> 2199</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddShapeLayer(layerName.c_str());</div><div class="line"><a name="l02200"></a><span class="lineno"> 2200</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l02203"></a><span class="lineno"> 2203</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02204"></a><span class="lineno"> 2204</span> </div><div class="line"><a name="l02205"></a><span class="lineno"> 2205</span>  <span class="comment">// Check if output tensor type is Signed32 or Signed64</span></div><div class="line"><a name="l02206"></a><span class="lineno"> 2206</span>  <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a> &&</div><div class="line"><a name="l02207"></a><span class="lineno"> 2207</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::DataType::Signed64</a>)</div><div class="line"><a name="l02208"></a><span class="lineno"> 2208</span>  {</div><div class="line"><a name="l02209"></a><span class="lineno"> 2209</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02210"></a><span class="lineno"> 2210</span>  fmt::format(</div><div class="line"><a name="l02211"></a><span class="lineno"> 2211</span>  <span class="stringliteral">"Output tensor data type is not supported. (Supported types: Signed32 & Signed64) {}"</span>,</div><div class="line"><a name="l02212"></a><span class="lineno"> 2212</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l02215"></a><span class="lineno"> 2215</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02216"></a><span class="lineno"> 2216</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02217"></a><span class="lineno"> 2217</span> </div><div class="line"><a name="l02218"></a><span class="lineno"> 2218</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02219"></a><span class="lineno"> 2219</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</div><div class="line"><a name="l02220"></a><span class="lineno"> 2220</span> }</div><div class="line"><a name="l02221"></a><span class="lineno"> 2221</span> </div><div class="line"><a name="l02222"></a><span class="lineno"> 2222</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseSqueeze(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02223"></a><span class="lineno"> 2223</span> {</div><div class="line"><a name="l02224"></a><span class="lineno"> 2224</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02225"></a><span class="lineno"> 2225</span> </div><div class="line"><a name="l02226"></a><span class="lineno"> 2226</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02227"></a><span class="lineno"> 2227</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</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>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02230"></a><span class="lineno"> 2230</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</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>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l02233"></a><span class="lineno"> 2233</span>  <span class="keyword">const</span> <span class="keyword">auto</span> * options = operatorPtr->builtin_options.AsSqueezeOptions();</div><div class="line"><a name="l02234"></a><span class="lineno"> 2234</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Squeeze:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02235"></a><span class="lineno"> 2235</span> </div><div class="line"><a name="l02236"></a><span class="lineno"> 2236</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l02237"></a><span class="lineno"> 2237</span> </div><div class="line"><a name="l02238"></a><span class="lineno"> 2238</span>  std::vector<uint32_t> squeezeDim;</div><div class="line"><a name="l02239"></a><span class="lineno"> 2239</span>  <span class="comment">// A single negative dim index is interpreted as a negative index in python</span></div><div class="line"><a name="l02240"></a><span class="lineno"> 2240</span>  <span class="comment">// Meaning the index will be the shape size plus the negative index value</span></div><div class="line"><a name="l02241"></a><span class="lineno"> 2241</span>  <span class="keywordflow">if</span> (options->squeeze_dims.size() == 1 && options->squeeze_dims[0] < 0)</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>  int32_t dim = <span class="keyword">static_cast<</span>int32_t<span class="keyword">></span>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>().<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()) + options->squeeze_dims[0];</div><div class="line"><a name="l02244"></a><span class="lineno"> 2244</span>  squeezeDim.push_back(static_cast<uint32_t>(dim));</div><div class="line"><a name="l02245"></a><span class="lineno"> 2245</span>  }</div><div class="line"><a name="l02246"></a><span class="lineno"> 2246</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l02247"></a><span class="lineno"> 2247</span>  {</div><div class="line"><a name="l02248"></a><span class="lineno"> 2248</span>  squeezeDim = AsUnsignedVector(options->squeeze_dims);</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_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a7d28f3a07c7ab30f66e7c70ded86eb49">TfLiteParserImpl::OutputShapeOfSqueeze</a>(squeezeDim, inputTensorInfo);</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>  CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, <span class="stringliteral">"Input 0"</span>, <span class="stringliteral">"Output 0"</span>);</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>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">ReshapeDescriptor</a> reshapeDesc;</div><div class="line"><a name="l02256"></a><span class="lineno"> 2256</span>  reshapeDesc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l02257"></a><span class="lineno"> 2257</span> </div><div class="line"><a name="l02258"></a><span class="lineno"> 2258</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str());</div><div class="line"><a name="l02259"></a><span class="lineno"> 2259</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02260"></a><span class="lineno"> 2260</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02261"></a><span class="lineno"> 2261</span> </div><div class="line"><a name="l02262"></a><span class="lineno"> 2262</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02263"></a><span class="lineno"> 2263</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02264"></a><span class="lineno"> 2264</span> </div><div class="line"><a name="l02265"></a><span class="lineno"> 2265</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02266"></a><span class="lineno"> 2266</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02267"></a><span class="lineno"> 2267</span> }</div><div class="line"><a name="l02268"></a><span class="lineno"> 2268</span> </div><div class="line"><a name="l02269"></a><span class="lineno"> 2269</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseStridedSlice(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</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>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02272"></a><span class="lineno"> 2272</span> </div><div class="line"><a name="l02273"></a><span class="lineno"> 2273</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02274"></a><span class="lineno"> 2274</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 4);</div><div class="line"><a name="l02275"></a><span class="lineno"> 2275</span> </div><div class="line"><a name="l02276"></a><span class="lineno"> 2276</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02277"></a><span class="lineno"> 2277</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02278"></a><span class="lineno"> 2278</span> </div><div class="line"><a name="l02279"></a><span class="lineno"> 2279</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l02280"></a><span class="lineno"> 2280</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsStridedSliceOptions();</div><div class="line"><a name="l02281"></a><span class="lineno"> 2281</span> </div><div class="line"><a name="l02282"></a><span class="lineno"> 2282</span>  <a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml">StridedSliceDescriptor</a> desc;</div><div class="line"><a name="l02283"></a><span class="lineno"> 2283</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a61081be1483984e33db452c75d569f51">m_BeginMask</a> = options->begin_mask;</div><div class="line"><a name="l02284"></a><span class="lineno"> 2284</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#af996d82c47e43a16f4c8faa6c6b3e030">m_EllipsisMask</a> = options->ellipsis_mask;</div><div class="line"><a name="l02285"></a><span class="lineno"> 2285</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#ac37e49c0d6e6e54f9d2015d0f11f8ee7">m_EndMask</a> = options->end_mask;</div><div class="line"><a name="l02286"></a><span class="lineno"> 2286</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a7c91eda2b331d607bae92cd8ebf50bb9">m_NewAxisMask</a> = options->new_axis_mask;</div><div class="line"><a name="l02287"></a><span class="lineno"> 2287</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a6d0384878432cfc9652b7ae8bc59506f">m_ShrinkAxisMask</a> = options->shrink_axis_mask;</div><div class="line"><a name="l02288"></a><span class="lineno"> 2288</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l02289"></a><span class="lineno"> 2289</span> </div><div class="line"><a name="l02290"></a><span class="lineno"> 2290</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> beginTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l02291"></a><span class="lineno"> 2291</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> beginBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l02292"></a><span class="lineno"> 2292</span> </div><div class="line"><a name="l02293"></a><span class="lineno"> 2293</span>  std::vector<int> begin(beginTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l02294"></a><span class="lineno"> 2294</span>  ::memcpy(begin.data(), beginBufferPtr->data.data(), beginTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l02295"></a><span class="lineno"> 2295</span> </div><div class="line"><a name="l02296"></a><span class="lineno"> 2296</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> endTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 2);</div><div class="line"><a name="l02297"></a><span class="lineno"> 2297</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> endBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[2]->buffer);</div><div class="line"><a name="l02298"></a><span class="lineno"> 2298</span> </div><div class="line"><a name="l02299"></a><span class="lineno"> 2299</span>  std::vector<int> end(endTensorInfo.GetNumElements());</div><div class="line"><a name="l02300"></a><span class="lineno"> 2300</span>  ::memcpy(end.data(), endBufferPtr->data.data(), endTensorInfo.GetNumBytes());</div><div class="line"><a name="l02301"></a><span class="lineno"> 2301</span> </div><div class="line"><a name="l02302"></a><span class="lineno"> 2302</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> strideTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 3);</div><div class="line"><a name="l02303"></a><span class="lineno"> 2303</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> strideBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[3]->buffer);</div><div class="line"><a name="l02304"></a><span class="lineno"> 2304</span> </div><div class="line"><a name="l02305"></a><span class="lineno"> 2305</span>  std::vector<int> stride(strideTensorInfo.GetNumElements());</div><div class="line"><a name="l02306"></a><span class="lineno"> 2306</span>  ::memcpy(stride.data(), strideBufferPtr->data.data(), strideTensorInfo.GetNumBytes());</div><div class="line"><a name="l02307"></a><span class="lineno"> 2307</span> </div><div class="line"><a name="l02308"></a><span class="lineno"> 2308</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a118fe06b7c2599da60398ee311ede923">m_Begin</a> = begin;</div><div class="line"><a name="l02309"></a><span class="lineno"> 2309</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#aa68194dd6258ab5b04123005a066ea25">m_End</a> = end;</div><div class="line"><a name="l02310"></a><span class="lineno"> 2310</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a0d53caff836b84204adbd1c28752a201">m_Stride</a> = stride;</div><div class="line"><a name="l02311"></a><span class="lineno"> 2311</span> </div><div class="line"><a name="l02312"></a><span class="lineno"> 2312</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"StridedSlice:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02313"></a><span class="lineno"> 2313</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddStridedSliceLayer(desc, layerName.c_str());</div><div class="line"><a name="l02314"></a><span class="lineno"> 2314</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02315"></a><span class="lineno"> 2315</span> </div><div class="line"><a name="l02316"></a><span class="lineno"> 2316</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l02317"></a><span class="lineno"> 2317</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02318"></a><span class="lineno"> 2318</span> </div><div class="line"><a name="l02319"></a><span class="lineno"> 2319</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02320"></a><span class="lineno"> 2320</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02321"></a><span class="lineno"> 2321</span> </div><div class="line"><a name="l02322"></a><span class="lineno"> 2322</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02323"></a><span class="lineno"> 2323</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02324"></a><span class="lineno"> 2324</span> }</div><div class="line"><a name="l02325"></a><span class="lineno"> 2325</span> </div><div class="line"><a name="l02326"></a><span class="lineno"> 2326</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseSub(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02327"></a><span class="lineno"> 2327</span> {</div><div class="line"><a name="l02328"></a><span class="lineno"> 2328</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02329"></a><span class="lineno"> 2329</span> </div><div class="line"><a name="l02330"></a><span class="lineno"> 2330</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l02331"></a><span class="lineno"> 2331</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsSubOptions();</div><div class="line"><a name="l02332"></a><span class="lineno"> 2332</span> </div><div class="line"><a name="l02333"></a><span class="lineno"> 2333</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02334"></a><span class="lineno"> 2334</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l02335"></a><span class="lineno"> 2335</span> </div><div class="line"><a name="l02336"></a><span class="lineno"> 2336</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02337"></a><span class="lineno"> 2337</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02338"></a><span class="lineno"> 2338</span> </div><div class="line"><a name="l02339"></a><span class="lineno"> 2339</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l02340"></a><span class="lineno"> 2340</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l02341"></a><span class="lineno"> 2341</span> </div><div class="line"><a name="l02342"></a><span class="lineno"> 2342</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Sub:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02343"></a><span class="lineno"> 2343</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddSubtractionLayer(layerName.c_str());</div><div class="line"><a name="l02344"></a><span class="lineno"> 2344</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02345"></a><span class="lineno"> 2345</span> </div><div class="line"><a name="l02346"></a><span class="lineno"> 2346</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});</div><div class="line"><a name="l02347"></a><span class="lineno"> 2347</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02348"></a><span class="lineno"> 2348</span> </div><div class="line"><a name="l02349"></a><span class="lineno"> 2349</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02350"></a><span class="lineno"> 2350</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l02351"></a><span class="lineno"> 2351</span> </div><div class="line"><a name="l02352"></a><span class="lineno"> 2352</span>  layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function);</div><div class="line"><a name="l02353"></a><span class="lineno"> 2353</span> </div><div class="line"><a name="l02354"></a><span class="lineno"> 2354</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02355"></a><span class="lineno"> 2355</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02356"></a><span class="lineno"> 2356</span> }</div><div class="line"><a name="l02357"></a><span class="lineno"> 2357</span> </div><div class="line"><a name="l02358"></a><span class="lineno"> 2358</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseDiv(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02359"></a><span class="lineno"> 2359</span> {</div><div class="line"><a name="l02360"></a><span class="lineno"> 2360</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02361"></a><span class="lineno"> 2361</span> </div><div class="line"><a name="l02362"></a><span class="lineno"> 2362</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l02363"></a><span class="lineno"> 2363</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsDivOptions();</div><div class="line"><a name="l02364"></a><span class="lineno"> 2364</span> </div><div class="line"><a name="l02365"></a><span class="lineno"> 2365</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02366"></a><span class="lineno"> 2366</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l02367"></a><span class="lineno"> 2367</span> </div><div class="line"><a name="l02368"></a><span class="lineno"> 2368</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02369"></a><span class="lineno"> 2369</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02370"></a><span class="lineno"> 2370</span> </div><div class="line"><a name="l02371"></a><span class="lineno"> 2371</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l02372"></a><span class="lineno"> 2372</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l02373"></a><span class="lineno"> 2373</span> </div><div class="line"><a name="l02374"></a><span class="lineno"> 2374</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Div:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02375"></a><span class="lineno"> 2375</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddDivisionLayer(layerName.c_str());</div><div class="line"><a name="l02376"></a><span class="lineno"> 2376</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02377"></a><span class="lineno"> 2377</span> </div><div class="line"><a name="l02378"></a><span class="lineno"> 2378</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});</div><div class="line"><a name="l02379"></a><span class="lineno"> 2379</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02380"></a><span class="lineno"> 2380</span> </div><div class="line"><a name="l02381"></a><span class="lineno"> 2381</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02382"></a><span class="lineno"> 2382</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l02383"></a><span class="lineno"> 2383</span>  layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function);</div><div class="line"><a name="l02384"></a><span class="lineno"> 2384</span> </div><div class="line"><a name="l02385"></a><span class="lineno"> 2385</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02386"></a><span class="lineno"> 2386</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02387"></a><span class="lineno"> 2387</span> }</div><div class="line"><a name="l02388"></a><span class="lineno"> 2388</span> </div><div class="line"><a name="l02389"></a><span class="lineno"> 2389</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseFloorDiv(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02390"></a><span class="lineno"> 2390</span> {</div><div class="line"><a name="l02391"></a><span class="lineno"> 2391</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02392"></a><span class="lineno"> 2392</span> </div><div class="line"><a name="l02393"></a><span class="lineno"> 2393</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02394"></a><span class="lineno"> 2394</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l02395"></a><span class="lineno"> 2395</span> </div><div class="line"><a name="l02396"></a><span class="lineno"> 2396</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02397"></a><span class="lineno"> 2397</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02398"></a><span class="lineno"> 2398</span> </div><div class="line"><a name="l02399"></a><span class="lineno"> 2399</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l02400"></a><span class="lineno"> 2400</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l02401"></a><span class="lineno"> 2401</span> </div><div class="line"><a name="l02402"></a><span class="lineno"> 2402</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Div:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02403"></a><span class="lineno"> 2403</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddDivisionLayer(layerName.c_str());</div><div class="line"><a name="l02404"></a><span class="lineno"> 2404</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02405"></a><span class="lineno"> 2405</span> </div><div class="line"><a name="l02406"></a><span class="lineno"> 2406</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});</div><div class="line"><a name="l02407"></a><span class="lineno"> 2407</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02408"></a><span class="lineno"> 2408</span> </div><div class="line"><a name="l02409"></a><span class="lineno"> 2409</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02410"></a><span class="lineno"> 2410</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l02411"></a><span class="lineno"> 2411</span>  layer = AddFusedFloorLayer(layer, 0);</div><div class="line"><a name="l02412"></a><span class="lineno"> 2412</span> </div><div class="line"><a name="l02413"></a><span class="lineno"> 2413</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02414"></a><span class="lineno"> 2414</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02415"></a><span class="lineno"> 2415</span> }</div><div class="line"><a name="l02416"></a><span class="lineno"> 2416</span> </div><div class="line"><a name="l02417"></a><span class="lineno"> 2417</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseAdd(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02418"></a><span class="lineno"> 2418</span> {</div><div class="line"><a name="l02419"></a><span class="lineno"> 2419</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02420"></a><span class="lineno"> 2420</span> </div><div class="line"><a name="l02421"></a><span class="lineno"> 2421</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l02422"></a><span class="lineno"> 2422</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsAddOptions();</div><div class="line"><a name="l02423"></a><span class="lineno"> 2423</span> </div><div class="line"><a name="l02424"></a><span class="lineno"> 2424</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02425"></a><span class="lineno"> 2425</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l02426"></a><span class="lineno"> 2426</span> </div><div class="line"><a name="l02427"></a><span class="lineno"> 2427</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02428"></a><span class="lineno"> 2428</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02429"></a><span class="lineno"> 2429</span> </div><div class="line"><a name="l02430"></a><span class="lineno"> 2430</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l02431"></a><span class="lineno"> 2431</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l02432"></a><span class="lineno"> 2432</span> </div><div class="line"><a name="l02433"></a><span class="lineno"> 2433</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Add:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02434"></a><span class="lineno"> 2434</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddAdditionLayer(layerName.c_str());</div><div class="line"><a name="l02435"></a><span class="lineno"> 2435</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02436"></a><span class="lineno"> 2436</span> </div><div class="line"><a name="l02437"></a><span class="lineno"> 2437</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});</div><div class="line"><a name="l02438"></a><span class="lineno"> 2438</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02439"></a><span class="lineno"> 2439</span> </div><div class="line"><a name="l02440"></a><span class="lineno"> 2440</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02441"></a><span class="lineno"> 2441</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l02442"></a><span class="lineno"> 2442</span>  layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function);</div><div class="line"><a name="l02443"></a><span class="lineno"> 2443</span> </div><div class="line"><a name="l02444"></a><span class="lineno"> 2444</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02445"></a><span class="lineno"> 2445</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02446"></a><span class="lineno"> 2446</span> }</div><div class="line"><a name="l02447"></a><span class="lineno"> 2447</span> </div><div class="line"><a name="l02448"></a><span class="lineno"> 2448</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseMul(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02449"></a><span class="lineno"> 2449</span> {</div><div class="line"><a name="l02450"></a><span class="lineno"> 2450</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02451"></a><span class="lineno"> 2451</span> </div><div class="line"><a name="l02452"></a><span class="lineno"> 2452</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l02453"></a><span class="lineno"> 2453</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsMulOptions();</div><div class="line"><a name="l02454"></a><span class="lineno"> 2454</span> </div><div class="line"><a name="l02455"></a><span class="lineno"> 2455</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02456"></a><span class="lineno"> 2456</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l02457"></a><span class="lineno"> 2457</span> </div><div class="line"><a name="l02458"></a><span class="lineno"> 2458</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02459"></a><span class="lineno"> 2459</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02460"></a><span class="lineno"> 2460</span> </div><div class="line"><a name="l02461"></a><span class="lineno"> 2461</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l02462"></a><span class="lineno"> 2462</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l02463"></a><span class="lineno"> 2463</span> </div><div class="line"><a name="l02464"></a><span class="lineno"> 2464</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Mul:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02465"></a><span class="lineno"> 2465</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddMultiplicationLayer(layerName.c_str());</div><div class="line"><a name="l02466"></a><span class="lineno"> 2466</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02467"></a><span class="lineno"> 2467</span> </div><div class="line"><a name="l02468"></a><span class="lineno"> 2468</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});</div><div class="line"><a name="l02469"></a><span class="lineno"> 2469</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02470"></a><span class="lineno"> 2470</span> </div><div class="line"><a name="l02471"></a><span class="lineno"> 2471</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02472"></a><span class="lineno"> 2472</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l02473"></a><span class="lineno"> 2473</span>  layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function);</div><div class="line"><a name="l02474"></a><span class="lineno"> 2474</span> </div><div class="line"><a name="l02475"></a><span class="lineno"> 2475</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02476"></a><span class="lineno"> 2476</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02477"></a><span class="lineno"> 2477</span> }</div><div class="line"><a name="l02478"></a><span class="lineno"> 2478</span> </div><div class="line"><a name="l02479"></a><span class="lineno"> 2479</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseMean(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02480"></a><span class="lineno"> 2480</span> {</div><div class="line"><a name="l02481"></a><span class="lineno"> 2481</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02482"></a><span class="lineno"> 2482</span> </div><div class="line"><a name="l02483"></a><span class="lineno"> 2483</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02484"></a><span class="lineno"> 2484</span> </div><div class="line"><a name="l02485"></a><span class="lineno"> 2485</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02486"></a><span class="lineno"> 2486</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02487"></a><span class="lineno"> 2487</span> </div><div class="line"><a name="l02488"></a><span class="lineno"> 2488</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> dimTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l02489"></a><span class="lineno"> 2489</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> bufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l02490"></a><span class="lineno"> 2490</span> </div><div class="line"><a name="l02491"></a><span class="lineno"> 2491</span>  <a class="code" href="structarmnn_1_1_mean_descriptor.xhtml">armnn::MeanDescriptor</a> desc;</div><div class="line"><a name="l02492"></a><span class="lineno"> 2492</span>  std::vector<unsigned int> axis(dimTensorInfo.GetNumElements());</div><div class="line"><a name="l02493"></a><span class="lineno"> 2493</span>  ::memcpy(axis.data(), bufferPtr->data.data(), dimTensorInfo.GetNumBytes());</div><div class="line"><a name="l02494"></a><span class="lineno"> 2494</span>  desc.<a class="code" href="structarmnn_1_1_mean_descriptor.xhtml#a1f0d67b087c491248bd1cde3ff995a95">m_Axis</a> = axis;</div><div class="line"><a name="l02495"></a><span class="lineno"> 2495</span> </div><div class="line"><a name="l02496"></a><span class="lineno"> 2496</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l02497"></a><span class="lineno"> 2497</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(outputs[0], <span class="keyword">true</span>);</div><div class="line"><a name="l02498"></a><span class="lineno"> 2498</span> </div><div class="line"><a name="l02499"></a><span class="lineno"> 2499</span>  desc.<a class="code" href="structarmnn_1_1_mean_descriptor.xhtml#a28e0548abfc4e79c48f29a3d11a062e9">m_KeepDims</a> =</div><div class="line"><a name="l02500"></a><span class="lineno"> 2500</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() == outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() ?</div><div class="line"><a name="l02501"></a><span class="lineno"> 2501</span>  true : <span class="keyword">false</span>;</div><div class="line"><a name="l02502"></a><span class="lineno"> 2502</span> </div><div class="line"><a name="l02503"></a><span class="lineno"> 2503</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Mean:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02504"></a><span class="lineno"> 2504</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddMeanLayer(desc, layerName.c_str());</div><div class="line"><a name="l02505"></a><span class="lineno"> 2505</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02506"></a><span class="lineno"> 2506</span> </div><div class="line"><a name="l02507"></a><span class="lineno"> 2507</span>  outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l02508"></a><span class="lineno"> 2508</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02509"></a><span class="lineno"> 2509</span> </div><div class="line"><a name="l02510"></a><span class="lineno"> 2510</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02511"></a><span class="lineno"> 2511</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02512"></a><span class="lineno"> 2512</span> </div><div class="line"><a name="l02513"></a><span class="lineno"> 2513</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02514"></a><span class="lineno"> 2514</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02515"></a><span class="lineno"> 2515</span> }</div><div class="line"><a name="l02516"></a><span class="lineno"> 2516</span> </div><div class="line"><a name="l02517"></a><span class="lineno"> 2517</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParsePad(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02518"></a><span class="lineno"> 2518</span> {</div><div class="line"><a name="l02519"></a><span class="lineno"> 2519</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02520"></a><span class="lineno"> 2520</span> </div><div class="line"><a name="l02521"></a><span class="lineno"> 2521</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParserImpl::TensorRawPtrVector</a> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02522"></a><span class="lineno"> 2522</span> </div><div class="line"><a name="l02523"></a><span class="lineno"> 2523</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParserImpl::TensorRawPtrVector</a> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02524"></a><span class="lineno"> 2524</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02525"></a><span class="lineno"> 2525</span> </div><div class="line"><a name="l02526"></a><span class="lineno"> 2526</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l02527"></a><span class="lineno"> 2527</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> padTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l02528"></a><span class="lineno"> 2528</span> </div><div class="line"><a name="l02529"></a><span class="lineno"> 2529</span>  std::vector<unsigned int> padBuffer = GetUIntBuffer(padTensorInfo, m_Model, inputs[1]->buffer);</div><div class="line"><a name="l02530"></a><span class="lineno"> 2530</span> </div><div class="line"><a name="l02531"></a><span class="lineno"> 2531</span>  <span class="keywordtype">size_t</span> step = 2;</div><div class="line"><a name="l02532"></a><span class="lineno"> 2532</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a> desc;</div><div class="line"><a name="l02533"></a><span class="lineno"> 2533</span>  <span class="keyword">auto</span> opcode = GetOpCode(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02534"></a><span class="lineno"> 2534</span> </div><div class="line"><a name="l02535"></a><span class="lineno"> 2535</span>  <span class="keywordflow">if</span> (opcode == tflite::BuiltinOperator_PAD)</div><div class="line"><a name="l02536"></a><span class="lineno"> 2536</span>  {</div><div class="line"><a name="l02537"></a><span class="lineno"> 2537</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l02538"></a><span class="lineno"> 2538</span> </div><div class="line"><a name="l02539"></a><span class="lineno"> 2539</span>  <span class="keywordflow">if</span> (inputTensorInfo.IsQuantized())</div><div class="line"><a name="l02540"></a><span class="lineno"> 2540</span>  {</div><div class="line"><a name="l02541"></a><span class="lineno"> 2541</span>  desc.<a class="code" href="structarmnn_1_1_pad_descriptor.xhtml#a410fa919f78af0f0f100bd1594eca4ab">m_PadValue</a> = <span class="keyword">static_cast<</span><span class="keywordtype">float</span><span class="keyword">></span>(inputTensorInfo.GetQuantizationOffset());</div><div class="line"><a name="l02542"></a><span class="lineno"> 2542</span>  }</div><div class="line"><a name="l02543"></a><span class="lineno"> 2543</span>  }</div><div class="line"><a name="l02544"></a><span class="lineno"> 2544</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (opcode == tflite::BuiltinOperator_PADV2)</div><div class="line"><a name="l02545"></a><span class="lineno"> 2545</span>  {</div><div class="line"><a name="l02546"></a><span class="lineno"> 2546</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 3);</div><div class="line"><a name="l02547"></a><span class="lineno"> 2547</span> </div><div class="line"><a name="l02548"></a><span class="lineno"> 2548</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> padValueTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 2);</div><div class="line"><a name="l02549"></a><span class="lineno"> 2549</span> </div><div class="line"><a name="l02550"></a><span class="lineno"> 2550</span>  <span class="keywordflow">if</span> (padValueTensorInfo.GetNumElements() != 1)</div><div class="line"><a name="l02551"></a><span class="lineno"> 2551</span>  {</div><div class="line"><a name="l02552"></a><span class="lineno"> 2552</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>(<span class="stringliteral">"Multiple padding values are not supported in PADV2"</span>);</div><div class="line"><a name="l02553"></a><span class="lineno"> 2553</span>  }</div><div class="line"><a name="l02554"></a><span class="lineno"> 2554</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> padValueBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[2]->buffer);</div><div class="line"><a name="l02555"></a><span class="lineno"> 2555</span> </div><div class="line"><a name="l02556"></a><span class="lineno"> 2556</span>  <span class="comment">// Get the pad value from the input tensor</span></div><div class="line"><a name="l02557"></a><span class="lineno"> 2557</span>  <span class="keywordflow">if</span> (padValueBufferPtr->data.size() > 0)</div><div class="line"><a name="l02558"></a><span class="lineno"> 2558</span>  {</div><div class="line"><a name="l02559"></a><span class="lineno"> 2559</span>  <span class="keywordflow">switch</span> (padValueTensorInfo.GetDataType())</div><div class="line"><a name="l02560"></a><span class="lineno"> 2560</span>  {</div><div class="line"><a name="l02561"></a><span class="lineno"> 2561</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</div><div class="line"><a name="l02562"></a><span class="lineno"> 2562</span>  {</div><div class="line"><a name="l02563"></a><span class="lineno"> 2563</span>  std::vector<float> padValueBuffer(padValueTensorInfo.GetNumElements());</div><div class="line"><a name="l02564"></a><span class="lineno"> 2564</span>  ::memcpy(padValueBuffer.data(), padValueBufferPtr->data.data(), padValueBufferPtr->data.size());</div><div class="line"><a name="l02565"></a><span class="lineno"> 2565</span>  desc.<a class="code" href="structarmnn_1_1_pad_descriptor.xhtml#a410fa919f78af0f0f100bd1594eca4ab">m_PadValue</a> = padValueBuffer[0];</div><div class="line"><a name="l02566"></a><span class="lineno"> 2566</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02567"></a><span class="lineno"> 2567</span>  }</div><div class="line"><a name="l02568"></a><span class="lineno"> 2568</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</div><div class="line"><a name="l02569"></a><span class="lineno"> 2569</span>  {</div><div class="line"><a name="l02570"></a><span class="lineno"> 2570</span>  std::vector<uint8_t> padValueBuffer(padValueTensorInfo.GetNumElements());</div><div class="line"><a name="l02571"></a><span class="lineno"> 2571</span>  ::memcpy(padValueBuffer.data(), padValueBufferPtr->data.data(), padValueBufferPtr->data.size());</div><div class="line"><a name="l02572"></a><span class="lineno"> 2572</span>  desc.<a class="code" href="structarmnn_1_1_pad_descriptor.xhtml#a410fa919f78af0f0f100bd1594eca4ab">m_PadValue</a> = armnn::Dequantize<uint8_t>(padValueBuffer[0],</div><div class="line"><a name="l02573"></a><span class="lineno"> 2573</span>  padValueTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02574"></a><span class="lineno"> 2574</span>  padValueTensorInfo.GetQuantizationOffset());</div><div class="line"><a name="l02575"></a><span class="lineno"> 2575</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02576"></a><span class="lineno"> 2576</span>  }</div><div class="line"><a name="l02577"></a><span class="lineno"> 2577</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>:</div><div class="line"><a name="l02578"></a><span class="lineno"> 2578</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>:</div><div class="line"><a name="l02579"></a><span class="lineno"> 2579</span>  {</div><div class="line"><a name="l02580"></a><span class="lineno"> 2580</span>  std::vector<int8_t> padValueBuffer(padValueTensorInfo.GetNumElements());</div><div class="line"><a name="l02581"></a><span class="lineno"> 2581</span>  ::memcpy(padValueBuffer.data(), padValueBufferPtr->data.data(), padValueBufferPtr->data.size());</div><div class="line"><a name="l02582"></a><span class="lineno"> 2582</span>  desc.<a class="code" href="structarmnn_1_1_pad_descriptor.xhtml#a410fa919f78af0f0f100bd1594eca4ab">m_PadValue</a> = armnn::Dequantize<int8_t>(padValueBuffer[0],</div><div class="line"><a name="l02583"></a><span class="lineno"> 2583</span>  padValueTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02584"></a><span class="lineno"> 2584</span>  padValueTensorInfo.GetQuantizationOffset());</div><div class="line"><a name="l02585"></a><span class="lineno"> 2585</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02586"></a><span class="lineno"> 2586</span>  }</div><div class="line"><a name="l02587"></a><span class="lineno"> 2587</span>  <span class="keywordflow">default</span>: <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>(<span class="stringliteral">"Unsupported DataType"</span>);</div><div class="line"><a name="l02588"></a><span class="lineno"> 2588</span>  }</div><div class="line"><a name="l02589"></a><span class="lineno"> 2589</span>  }</div><div class="line"><a name="l02590"></a><span class="lineno"> 2590</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (inputTensorInfo.IsQuantized())</div><div class="line"><a name="l02591"></a><span class="lineno"> 2591</span>  {</div><div class="line"><a name="l02592"></a><span class="lineno"> 2592</span>  desc.<a class="code" href="structarmnn_1_1_pad_descriptor.xhtml#a410fa919f78af0f0f100bd1594eca4ab">m_PadValue</a> = <span class="keyword">static_cast<</span><span class="keywordtype">float</span><span class="keyword">></span>(inputTensorInfo.GetQuantizationOffset());</div><div class="line"><a name="l02593"></a><span class="lineno"> 2593</span>  }</div><div class="line"><a name="l02594"></a><span class="lineno"> 2594</span>  }</div><div class="line"><a name="l02595"></a><span class="lineno"> 2595</span> </div><div class="line"><a name="l02596"></a><span class="lineno"> 2596</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < padTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / step; ++i)</div><div class="line"><a name="l02597"></a><span class="lineno"> 2597</span>  {</div><div class="line"><a name="l02598"></a><span class="lineno"> 2598</span>  desc.<a class="code" href="structarmnn_1_1_pad_descriptor.xhtml#a85f98c94e11f65a6b73f831735c040f3">m_PadList</a>.emplace_back(padBuffer[i * step], padBuffer[i * step + 1]);</div><div class="line"><a name="l02599"></a><span class="lineno"> 2599</span>  }</div><div class="line"><a name="l02600"></a><span class="lineno"> 2600</span> </div><div class="line"><a name="l02601"></a><span class="lineno"> 2601</span>  <span class="keyword">auto</span> layerName = (opcode == tflite::BuiltinOperator_PAD) ? fmt::format(<span class="stringliteral">"Pad:{}:{}"</span>, subgraphIndex, operatorIndex)</div><div class="line"><a name="l02602"></a><span class="lineno"> 2602</span>  : fmt::format(<span class="stringliteral">"PadV2:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02603"></a><span class="lineno"> 2603</span> </div><div class="line"><a name="l02604"></a><span class="lineno"> 2604</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddPadLayer(desc, layerName.c_str());</div><div class="line"><a name="l02605"></a><span class="lineno"> 2605</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02606"></a><span class="lineno"> 2606</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l02607"></a><span class="lineno"> 2607</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02608"></a><span class="lineno"> 2608</span> </div><div class="line"><a name="l02609"></a><span class="lineno"> 2609</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02610"></a><span class="lineno"> 2610</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02611"></a><span class="lineno"> 2611</span> </div><div class="line"><a name="l02612"></a><span class="lineno"> 2612</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02613"></a><span class="lineno"> 2613</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02614"></a><span class="lineno"> 2614</span> }</div><div class="line"><a name="l02615"></a><span class="lineno"> 2615</span> </div><div class="line"><a name="l02616"></a><span class="lineno"> 2616</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseMirrorPad(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02617"></a><span class="lineno"> 2617</span> {</div><div class="line"><a name="l02618"></a><span class="lineno"> 2618</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02619"></a><span class="lineno"> 2619</span> </div><div class="line"><a name="l02620"></a><span class="lineno"> 2620</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParserImpl::TensorRawPtrVector</a> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02621"></a><span class="lineno"> 2621</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l02622"></a><span class="lineno"> 2622</span> </div><div class="line"><a name="l02623"></a><span class="lineno"> 2623</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParserImpl::TensorRawPtrVector</a> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02624"></a><span class="lineno"> 2624</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02625"></a><span class="lineno"> 2625</span> </div><div class="line"><a name="l02626"></a><span class="lineno"> 2626</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l02627"></a><span class="lineno"> 2627</span> </div><div class="line"><a name="l02628"></a><span class="lineno"> 2628</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> padTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l02629"></a><span class="lineno"> 2629</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> bufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l02630"></a><span class="lineno"> 2630</span> </div><div class="line"><a name="l02631"></a><span class="lineno"> 2631</span>  std::vector<unsigned int> padBuffer(padTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l02632"></a><span class="lineno"> 2632</span>  ::memcpy(padBuffer.data(), bufferPtr->data.data(), padTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l02633"></a><span class="lineno"> 2633</span> </div><div class="line"><a name="l02634"></a><span class="lineno"> 2634</span>  <span class="keywordtype">size_t</span> step = 2;</div><div class="line"><a name="l02635"></a><span class="lineno"> 2635</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a> desc;</div><div class="line"><a name="l02636"></a><span class="lineno"> 2636</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < padTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / step; ++i)</div><div class="line"><a name="l02637"></a><span class="lineno"> 2637</span>  {</div><div class="line"><a name="l02638"></a><span class="lineno"> 2638</span>  desc.<a class="code" href="structarmnn_1_1_pad_descriptor.xhtml#a85f98c94e11f65a6b73f831735c040f3">m_PadList</a>.emplace_back(padBuffer[i * step], padBuffer[i * step + 1]);</div><div class="line"><a name="l02639"></a><span class="lineno"> 2639</span>  }</div><div class="line"><a name="l02640"></a><span class="lineno"> 2640</span> </div><div class="line"><a name="l02641"></a><span class="lineno"> 2641</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l02642"></a><span class="lineno"> 2642</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsMirrorPadOptions();</div><div class="line"><a name="l02643"></a><span class="lineno"> 2643</span> </div><div class="line"><a name="l02644"></a><span class="lineno"> 2644</span>  <span class="keywordflow">if</span> (options->mode == tflite::MirrorPadMode_REFLECT)</div><div class="line"><a name="l02645"></a><span class="lineno"> 2645</span>  {</div><div class="line"><a name="l02646"></a><span class="lineno"> 2646</span>  desc.<a class="code" href="structarmnn_1_1_pad_descriptor.xhtml#a66f2c989f51ab6116de4380390250b69">m_PaddingMode</a> = PaddingMode::Reflect;</div><div class="line"><a name="l02647"></a><span class="lineno"> 2647</span>  }</div><div class="line"><a name="l02648"></a><span class="lineno"> 2648</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (options->mode == tflite::MirrorPadMode_SYMMETRIC)</div><div class="line"><a name="l02649"></a><span class="lineno"> 2649</span>  {</div><div class="line"><a name="l02650"></a><span class="lineno"> 2650</span>  desc.<a class="code" href="structarmnn_1_1_pad_descriptor.xhtml#a66f2c989f51ab6116de4380390250b69">m_PaddingMode</a> = PaddingMode::Symmetric;</div><div class="line"><a name="l02651"></a><span class="lineno"> 2651</span>  }</div><div class="line"><a name="l02652"></a><span class="lineno"> 2652</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l02653"></a><span class="lineno"> 2653</span>  {</div><div class="line"><a name="l02654"></a><span class="lineno"> 2654</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>(<span class="stringliteral">"PaddingMode must be either REFLECT or SYMMETRIC"</span>);</div><div class="line"><a name="l02655"></a><span class="lineno"> 2655</span>  }</div><div class="line"><a name="l02656"></a><span class="lineno"> 2656</span> </div><div class="line"><a name="l02657"></a><span class="lineno"> 2657</span>  <span class="comment">// If padding mode is Reflect then both paddings must be no greater than inputShape(i) - 1.</span></div><div class="line"><a name="l02658"></a><span class="lineno"> 2658</span>  <span class="comment">// If padding mode is Symmetric then both paddings must be no greater than inputShape(i).</span></div><div class="line"><a name="l02659"></a><span class="lineno"> 2659</span>  <span class="keyword">auto</span> inputShape = inputTensorInfo.GetShape();</div><div class="line"><a name="l02660"></a><span class="lineno"> 2660</span>  <span class="keyword">auto</span> padList = desc.<a class="code" href="structarmnn_1_1_pad_descriptor.xhtml#a85f98c94e11f65a6b73f831735c040f3">m_PadList</a>;</div><div class="line"><a name="l02661"></a><span class="lineno"> 2661</span> </div><div class="line"><a name="l02662"></a><span class="lineno"> 2662</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> isReflect = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(desc.<a class="code" href="structarmnn_1_1_pad_descriptor.xhtml#a66f2c989f51ab6116de4380390250b69">m_PaddingMode</a> == PaddingMode::Reflect);</div><div class="line"><a name="l02663"></a><span class="lineno"> 2663</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < padList.size(); ++i)</div><div class="line"><a name="l02664"></a><span class="lineno"> 2664</span>  {</div><div class="line"><a name="l02665"></a><span class="lineno"> 2665</span>  <span class="keywordflow">if</span>(padList.at(i).first > (inputShape[i] - isReflect) ||</div><div class="line"><a name="l02666"></a><span class="lineno"> 2666</span>  padList.at(i).second > (inputShape[i] - isReflect))</div><div class="line"><a name="l02667"></a><span class="lineno"> 2667</span>  {</div><div class="line"><a name="l02668"></a><span class="lineno"> 2668</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>(<span class="stringliteral">"Padding values must be less (Reflect) or "</span></div><div class="line"><a name="l02669"></a><span class="lineno"> 2669</span>  <span class="stringliteral">"equal (Symmetric) to the dimension size."</span>);</div><div class="line"><a name="l02670"></a><span class="lineno"> 2670</span>  }</div><div class="line"><a name="l02671"></a><span class="lineno"> 2671</span>  }</div><div class="line"><a name="l02672"></a><span class="lineno"> 2672</span> </div><div class="line"><a name="l02673"></a><span class="lineno"> 2673</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"MirrorPad:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02674"></a><span class="lineno"> 2674</span> </div><div class="line"><a name="l02675"></a><span class="lineno"> 2675</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddPadLayer(desc, layerName.c_str());</div><div class="line"><a name="l02676"></a><span class="lineno"> 2676</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02677"></a><span class="lineno"> 2677</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l02678"></a><span class="lineno"> 2678</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02679"></a><span class="lineno"> 2679</span> </div><div class="line"><a name="l02680"></a><span class="lineno"> 2680</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02681"></a><span class="lineno"> 2681</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02682"></a><span class="lineno"> 2682</span> </div><div class="line"><a name="l02683"></a><span class="lineno"> 2683</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02684"></a><span class="lineno"> 2684</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02685"></a><span class="lineno"> 2685</span> }</div><div class="line"><a name="l02686"></a><span class="lineno"> 2686</span> </div><div class="line"><a name="l02687"></a><span class="lineno"> 2687</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParsePrelu(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02688"></a><span class="lineno"> 2688</span> {</div><div class="line"><a name="l02689"></a><span class="lineno"> 2689</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02690"></a><span class="lineno"> 2690</span> </div><div class="line"><a name="l02691"></a><span class="lineno"> 2691</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02692"></a><span class="lineno"> 2692</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l02693"></a><span class="lineno"> 2693</span> </div><div class="line"><a name="l02694"></a><span class="lineno"> 2694</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02695"></a><span class="lineno"> 2695</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02696"></a><span class="lineno"> 2696</span> </div><div class="line"><a name="l02697"></a><span class="lineno"> 2697</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Prelu:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02698"></a><span class="lineno"> 2698</span> </div><div class="line"><a name="l02699"></a><span class="lineno"> 2699</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l02700"></a><span class="lineno"> 2700</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> alphaTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l02701"></a><span class="lineno"> 2701</span> </div><div class="line"><a name="l02702"></a><span class="lineno"> 2702</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddPreluLayer(layerName.c_str());</div><div class="line"><a name="l02703"></a><span class="lineno"> 2703</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02704"></a><span class="lineno"> 2704</span> </div><div class="line"><a name="l02705"></a><span class="lineno"> 2705</span> </div><div class="line"><a name="l02706"></a><span class="lineno"> 2706</span>  <span class="keywordflow">if</span> (IsConstTensor(inputs[1]))</div><div class="line"><a name="l02707"></a><span class="lineno"> 2707</span>  {</div><div class="line"><a name="l02708"></a><span class="lineno"> 2708</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02709"></a><span class="lineno"> 2709</span>  <a class="code" href="classarmnn_1_1_i_input_slot.xhtml">armnn::IInputSlot</a>* slot = &(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02710"></a><span class="lineno"> 2710</span>  RegisterConsumerOfTensor(subgraphIndex, inputTensorIndexes[0], slot);</div><div class="line"><a name="l02711"></a><span class="lineno"> 2711</span> </div><div class="line"><a name="l02712"></a><span class="lineno"> 2712</span>  <span class="keyword">auto</span> alphaTensorAndData = CreateConstTensorNonPermuted(inputs[1], alphaTensorInfo,</div><div class="line"><a name="l02713"></a><span class="lineno"> 2713</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>());</div><div class="line"><a name="l02714"></a><span class="lineno"> 2714</span>  std::string constLayerName = fmt::format(<span class="stringliteral">"Constant:{}"</span>, inputs[1]->name);</div><div class="line"><a name="l02715"></a><span class="lineno"> 2715</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* constLayer =</div><div class="line"><a name="l02716"></a><span class="lineno"> 2716</span>  m_Network->AddConstantLayer(alphaTensorAndData.first, constLayerName.c_str());</div><div class="line"><a name="l02717"></a><span class="lineno"> 2717</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(constLayer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02718"></a><span class="lineno"> 2718</span> </div><div class="line"><a name="l02719"></a><span class="lineno"> 2719</span>  constLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(alphaTensorInfo);</div><div class="line"><a name="l02720"></a><span class="lineno"> 2720</span>  constLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02721"></a><span class="lineno"> 2721</span>  RegisterOutputSlots(subgraphIndex,</div><div class="line"><a name="l02722"></a><span class="lineno"> 2722</span>  VIRTUAL_OPERATOR_ID,</div><div class="line"><a name="l02723"></a><span class="lineno"> 2723</span>  constLayer,</div><div class="line"><a name="l02724"></a><span class="lineno"> 2724</span>  { inputTensorIndexes[1] });</div><div class="line"><a name="l02725"></a><span class="lineno"> 2725</span>  }</div><div class="line"><a name="l02726"></a><span class="lineno"> 2726</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l02727"></a><span class="lineno"> 2727</span>  {</div><div class="line"><a name="l02728"></a><span class="lineno"> 2728</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02729"></a><span class="lineno"> 2729</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, inputTensorIndexes);</div><div class="line"><a name="l02730"></a><span class="lineno"> 2730</span>  }</div><div class="line"><a name="l02731"></a><span class="lineno"> 2731</span> </div><div class="line"><a name="l02732"></a><span class="lineno"> 2732</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});</div><div class="line"><a name="l02733"></a><span class="lineno"> 2733</span>  CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, <span class="stringliteral">"Input 0"</span>, <span class="stringliteral">"Output 0"</span>);</div><div class="line"><a name="l02734"></a><span class="lineno"> 2734</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02735"></a><span class="lineno"> 2735</span> </div><div class="line"><a name="l02736"></a><span class="lineno"> 2736</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02737"></a><span class="lineno"> 2737</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</div><div class="line"><a name="l02738"></a><span class="lineno"> 2738</span> }</div><div class="line"><a name="l02739"></a><span class="lineno"> 2739</span> </div><div class="line"><a name="l02740"></a><span class="lineno"> 2740</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseQuantize(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02741"></a><span class="lineno"> 2741</span> {</div><div class="line"><a name="l02742"></a><span class="lineno"> 2742</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02743"></a><span class="lineno"> 2743</span> </div><div class="line"><a name="l02744"></a><span class="lineno"> 2744</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02745"></a><span class="lineno"> 2745</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l02746"></a><span class="lineno"> 2746</span> </div><div class="line"><a name="l02747"></a><span class="lineno"> 2747</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02748"></a><span class="lineno"> 2748</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02749"></a><span class="lineno"> 2749</span> </div><div class="line"><a name="l02750"></a><span class="lineno"> 2750</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Quantize:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02751"></a><span class="lineno"> 2751</span> </div><div class="line"><a name="l02752"></a><span class="lineno"> 2752</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddQuantizeLayer(layerName.c_str());</div><div class="line"><a name="l02753"></a><span class="lineno"> 2753</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02754"></a><span class="lineno"> 2754</span> </div><div class="line"><a name="l02755"></a><span class="lineno"> 2755</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l02756"></a><span class="lineno"> 2756</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02757"></a><span class="lineno"> 2757</span> </div><div class="line"><a name="l02758"></a><span class="lineno"> 2758</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02759"></a><span class="lineno"> 2759</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02760"></a><span class="lineno"> 2760</span> </div><div class="line"><a name="l02761"></a><span class="lineno"> 2761</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02762"></a><span class="lineno"> 2762</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</div><div class="line"><a name="l02763"></a><span class="lineno"> 2763</span> }</div><div class="line"><a name="l02764"></a><span class="lineno"> 2764</span> </div><div class="line"><a name="l02765"></a><span class="lineno"> 2765</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseRelu(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02766"></a><span class="lineno"> 2766</span> {</div><div class="line"><a name="l02767"></a><span class="lineno"> 2767</span>  ParseActivation(subgraphIndex,operatorIndex, ActivationFunction::ReLu);</div><div class="line"><a name="l02768"></a><span class="lineno"> 2768</span> }</div><div class="line"><a name="l02769"></a><span class="lineno"> 2769</span> </div><div class="line"><a name="l02770"></a><span class="lineno"> 2770</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseRelu6(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02771"></a><span class="lineno"> 2771</span> {</div><div class="line"><a name="l02772"></a><span class="lineno"> 2772</span>  ParseActivation(subgraphIndex,operatorIndex, ActivationFunction::BoundedReLu);</div><div class="line"><a name="l02773"></a><span class="lineno"> 2773</span> }</div><div class="line"><a name="l02774"></a><span class="lineno"> 2774</span> </div><div class="line"><a name="l02775"></a><span class="lineno"> 2775</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseLeakyRelu(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02776"></a><span class="lineno"> 2776</span> {</div><div class="line"><a name="l02777"></a><span class="lineno"> 2777</span>  ParseActivation(subgraphIndex, operatorIndex, ActivationFunction::LeakyReLu);</div><div class="line"><a name="l02778"></a><span class="lineno"> 2778</span> }</div><div class="line"><a name="l02779"></a><span class="lineno"> 2779</span> </div><div class="line"><a name="l02780"></a><span class="lineno"> 2780</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseLogistic(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02781"></a><span class="lineno"> 2781</span> {</div><div class="line"><a name="l02782"></a><span class="lineno"> 2782</span>  ParseActivation(subgraphIndex,operatorIndex,ActivationFunction::Sigmoid);</div><div class="line"><a name="l02783"></a><span class="lineno"> 2783</span> }</div><div class="line"><a name="l02784"></a><span class="lineno"> 2784</span> </div><div class="line"><a name="l02785"></a><span class="lineno"> 2785</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseTanH(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02786"></a><span class="lineno"> 2786</span> {</div><div class="line"><a name="l02787"></a><span class="lineno"> 2787</span>  ParseActivation(subgraphIndex,operatorIndex,ActivationFunction::TanH);</div><div class="line"><a name="l02788"></a><span class="lineno"> 2788</span> }</div><div class="line"><a name="l02789"></a><span class="lineno"> 2789</span> </div><div class="line"><a name="l02790"></a><span class="lineno"> 2790</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseElu(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02791"></a><span class="lineno"> 2791</span> {</div><div class="line"><a name="l02792"></a><span class="lineno"> 2792</span>  ParseActivation(subgraphIndex, operatorIndex, ActivationFunction::Elu);</div><div class="line"><a name="l02793"></a><span class="lineno"> 2793</span> }</div><div class="line"><a name="l02794"></a><span class="lineno"> 2794</span> </div><div class="line"><a name="l02795"></a><span class="lineno"> 2795</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseHardSwish(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02796"></a><span class="lineno"> 2796</span> {</div><div class="line"><a name="l02797"></a><span class="lineno"> 2797</span>  ParseActivation(subgraphIndex, operatorIndex, ActivationFunction::HardSwish);</div><div class="line"><a name="l02798"></a><span class="lineno"> 2798</span> }</div><div class="line"><a name="l02799"></a><span class="lineno"> 2799</span> </div><div class="line"><a name="l02800"></a><span class="lineno"> 2800</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseActivation(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex, <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9ea">ActivationFunction</a> activationType)</div><div class="line"><a name="l02801"></a><span class="lineno"> 2801</span> {</div><div class="line"><a name="l02802"></a><span class="lineno"> 2802</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02803"></a><span class="lineno"> 2803</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l02804"></a><span class="lineno"> 2804</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(operatorPtr);</div><div class="line"><a name="l02805"></a><span class="lineno"> 2805</span> </div><div class="line"><a name="l02806"></a><span class="lineno"> 2806</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02807"></a><span class="lineno"> 2807</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l02808"></a><span class="lineno"> 2808</span> </div><div class="line"><a name="l02809"></a><span class="lineno"> 2809</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02810"></a><span class="lineno"> 2810</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02811"></a><span class="lineno"> 2811</span> </div><div class="line"><a name="l02812"></a><span class="lineno"> 2812</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Activation:"</span>);</div><div class="line"><a name="l02813"></a><span class="lineno"> 2813</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDesc;</div><div class="line"><a name="l02814"></a><span class="lineno"> 2814</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = activationType;</div><div class="line"><a name="l02815"></a><span class="lineno"> 2815</span> </div><div class="line"><a name="l02816"></a><span class="lineno"> 2816</span>  <span class="keywordflow">switch</span> (activationType)</div><div class="line"><a name="l02817"></a><span class="lineno"> 2817</span>  {</div><div class="line"><a name="l02818"></a><span class="lineno"> 2818</span>  <span class="keywordflow">case</span> ActivationFunction::ReLu:</div><div class="line"><a name="l02819"></a><span class="lineno"> 2819</span>  {</div><div class="line"><a name="l02820"></a><span class="lineno"> 2820</span>  layerName += fmt::format(<span class="stringliteral">"RELU:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02821"></a><span class="lineno"> 2821</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02822"></a><span class="lineno"> 2822</span>  }</div><div class="line"><a name="l02823"></a><span class="lineno"> 2823</span>  <span class="keywordflow">case</span> ActivationFunction::BoundedReLu:</div><div class="line"><a name="l02824"></a><span class="lineno"> 2824</span>  {</div><div class="line"><a name="l02825"></a><span class="lineno"> 2825</span>  layerName += fmt::format(<span class="stringliteral">"RELU6:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02826"></a><span class="lineno"> 2826</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 6.0f;</div><div class="line"><a name="l02827"></a><span class="lineno"> 2827</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = 0.0f;</div><div class="line"><a name="l02828"></a><span class="lineno"> 2828</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02829"></a><span class="lineno"> 2829</span>  }</div><div class="line"><a name="l02830"></a><span class="lineno"> 2830</span>  <span class="keywordflow">case</span> ActivationFunction::Sigmoid:</div><div class="line"><a name="l02831"></a><span class="lineno"> 2831</span>  {</div><div class="line"><a name="l02832"></a><span class="lineno"> 2832</span>  layerName += fmt::format(<span class="stringliteral">"SIGMOID:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02833"></a><span class="lineno"> 2833</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02834"></a><span class="lineno"> 2834</span>  }</div><div class="line"><a name="l02835"></a><span class="lineno"> 2835</span>  <span class="keywordflow">case</span> ActivationFunction::TanH:</div><div class="line"><a name="l02836"></a><span class="lineno"> 2836</span>  {</div><div class="line"><a name="l02837"></a><span class="lineno"> 2837</span>  layerName += fmt::format(<span class="stringliteral">"TANH:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02838"></a><span class="lineno"> 2838</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l02839"></a><span class="lineno"> 2839</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = 1.0f;</div><div class="line"><a name="l02840"></a><span class="lineno"> 2840</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02841"></a><span class="lineno"> 2841</span>  }</div><div class="line"><a name="l02842"></a><span class="lineno"> 2842</span>  <span class="keywordflow">case</span> ActivationFunction::LeakyReLu:</div><div class="line"><a name="l02843"></a><span class="lineno"> 2843</span>  {</div><div class="line"><a name="l02844"></a><span class="lineno"> 2844</span>  layerName += fmt::format(<span class="stringliteral">"LEAKYRELU:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02845"></a><span class="lineno"> 2845</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsLeakyReluOptions();</div><div class="line"><a name="l02846"></a><span class="lineno"> 2846</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = options->alpha;</div><div class="line"><a name="l02847"></a><span class="lineno"> 2847</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02848"></a><span class="lineno"> 2848</span>  }</div><div class="line"><a name="l02849"></a><span class="lineno"> 2849</span>  <span class="keywordflow">case</span> ActivationFunction::Elu:</div><div class="line"><a name="l02850"></a><span class="lineno"> 2850</span>  {</div><div class="line"><a name="l02851"></a><span class="lineno"> 2851</span>  layerName += fmt::format(<span class="stringliteral">"ELU:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02852"></a><span class="lineno"> 2852</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l02853"></a><span class="lineno"> 2853</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02854"></a><span class="lineno"> 2854</span>  }</div><div class="line"><a name="l02855"></a><span class="lineno"> 2855</span>  <span class="keywordflow">case</span> ActivationFunction::HardSwish:</div><div class="line"><a name="l02856"></a><span class="lineno"> 2856</span>  {</div><div class="line"><a name="l02857"></a><span class="lineno"> 2857</span>  layerName += fmt::format(<span class="stringliteral">"HARDSWISH:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02858"></a><span class="lineno"> 2858</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02859"></a><span class="lineno"> 2859</span>  }</div><div class="line"><a name="l02860"></a><span class="lineno"> 2860</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l02861"></a><span class="lineno"> 2861</span>  {</div><div class="line"><a name="l02862"></a><span class="lineno"> 2862</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02863"></a><span class="lineno"> 2863</span>  fmt::format(<span class="stringliteral">"Unexpected ActivationFunction[{}] when creating layerName {} "</span>,</div><div class="line"><a name="l02864"></a><span class="lineno"> 2864</span>  static_cast<int>(activationType), <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02865"></a><span class="lineno"> 2865</span>  }</div><div class="line"><a name="l02866"></a><span class="lineno"> 2866</span>  }</div><div class="line"><a name="l02867"></a><span class="lineno"> 2867</span> </div><div class="line"><a name="l02868"></a><span class="lineno"> 2868</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network->AddActivationLayer(activationDesc, layerName.c_str());</div><div class="line"><a name="l02869"></a><span class="lineno"> 2869</span> </div><div class="line"><a name="l02870"></a><span class="lineno"> 2870</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l02871"></a><span class="lineno"> 2871</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02872"></a><span class="lineno"> 2872</span> </div><div class="line"><a name="l02873"></a><span class="lineno"> 2873</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l02874"></a><span class="lineno"> 2874</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l02875"></a><span class="lineno"> 2875</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02876"></a><span class="lineno"> 2876</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02877"></a><span class="lineno"> 2877</span> </div><div class="line"><a name="l02878"></a><span class="lineno"> 2878</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l02879"></a><span class="lineno"> 2879</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02880"></a><span class="lineno"> 2880</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02881"></a><span class="lineno"> 2881</span> }</div><div class="line"><a name="l02882"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaebfa9a01a0bb8a0935114ff0140cc45"> 2882</a></span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaebfa9a01a0bb8a0935114ff0140cc45">TfLiteParserImpl::OutputShapeOfReshape</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& inputTensorInfo,</div><div class="line"><a name="l02883"></a><span class="lineno"> 2883</span>  <span class="keyword">const</span> std::vector<int32_t>& targetDimsIn)</div><div class="line"><a name="l02884"></a><span class="lineno"> 2884</span> {</div><div class="line"><a name="l02885"></a><span class="lineno"> 2885</span>  std::vector<unsigned int> outputDims(targetDimsIn.begin(), targetDimsIn.end());</div><div class="line"><a name="l02886"></a><span class="lineno"> 2886</span>  <span class="keyword">const</span> <span class="keyword">auto</span> stretchDim = std::find(targetDimsIn.begin(), targetDimsIn.end(), -1);</div><div class="line"><a name="l02887"></a><span class="lineno"> 2887</span> </div><div class="line"><a name="l02888"></a><span class="lineno"> 2888</span>  <span class="keywordflow">if</span> (stretchDim != targetDimsIn.end())</div><div class="line"><a name="l02889"></a><span class="lineno"> 2889</span>  {</div><div class="line"><a name="l02890"></a><span class="lineno"> 2890</span>  <span class="keywordflow">if</span> (std::find(std::next(stretchDim), targetDimsIn.end(), -1) != targetDimsIn.end())</div><div class="line"><a name="l02891"></a><span class="lineno"> 2891</span>  {</div><div class="line"><a name="l02892"></a><span class="lineno"> 2892</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02893"></a><span class="lineno"> 2893</span>  fmt::format(<span class="stringliteral">"At most one component of shape can be -1 {}"</span>, <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02894"></a><span class="lineno"> 2894</span>  }</div><div class="line"><a name="l02895"></a><span class="lineno"> 2895</span> </div><div class="line"><a name="l02896"></a><span class="lineno"> 2896</span>  <span class="keyword">auto</span> targetNumElements =</div><div class="line"><a name="l02897"></a><span class="lineno"> 2897</span>  <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(</div><div class="line"><a name="l02898"></a><span class="lineno"> 2898</span>  std::accumulate(targetDimsIn.begin(), targetDimsIn.end(), -1, std::multiplies<int32_t>()));</div><div class="line"><a name="l02899"></a><span class="lineno"> 2899</span> </div><div class="line"><a name="l02900"></a><span class="lineno"> 2900</span>  <span class="keyword">auto</span> stretchIndex = <span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(std::distance(targetDimsIn.begin(), stretchDim));</div><div class="line"><a name="l02901"></a><span class="lineno"> 2901</span>  outputDims[stretchIndex] = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / targetNumElements;</div><div class="line"><a name="l02902"></a><span class="lineno"> 2902</span>  }</div><div class="line"><a name="l02903"></a><span class="lineno"> 2903</span> </div><div class="line"><a name="l02904"></a><span class="lineno"> 2904</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape = <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(static_cast<unsigned int>(outputDims.size()), outputDims.data());</div><div class="line"><a name="l02905"></a><span class="lineno"> 2905</span> </div><div class="line"><a name="l02906"></a><span class="lineno"> 2906</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> reshapeInfo = inputTensorInfo;</div><div class="line"><a name="l02907"></a><span class="lineno"> 2907</span>  reshapeInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(outputShape);</div><div class="line"><a name="l02908"></a><span class="lineno"> 2908</span> </div><div class="line"><a name="l02909"></a><span class="lineno"> 2909</span>  <span class="keywordflow">return</span> reshapeInfo;</div><div class="line"><a name="l02910"></a><span class="lineno"> 2910</span> }</div><div class="line"><a name="l02911"></a><span class="lineno"> 2911</span> </div><div class="line"><a name="l02912"></a><span class="lineno"> 2912</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseReshape(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02913"></a><span class="lineno"> 2913</span> {</div><div class="line"><a name="l02914"></a><span class="lineno"> 2914</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02915"></a><span class="lineno"> 2915</span> </div><div class="line"><a name="l02916"></a><span class="lineno"> 2916</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02917"></a><span class="lineno"> 2917</span> </div><div class="line"><a name="l02918"></a><span class="lineno"> 2918</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02919"></a><span class="lineno"> 2919</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02920"></a><span class="lineno"> 2920</span> </div><div class="line"><a name="l02921"></a><span class="lineno"> 2921</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l02922"></a><span class="lineno"> 2922</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsReshapeOptions();</div><div class="line"><a name="l02923"></a><span class="lineno"> 2923</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Reshape:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02924"></a><span class="lineno"> 2924</span> </div><div class="line"><a name="l02925"></a><span class="lineno"> 2925</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l02926"></a><span class="lineno"> 2926</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> actualOutputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l02927"></a><span class="lineno"> 2927</span>  CheckMatchingQuantization(inputTensorInfo, actualOutputTensorInfo, layerName, <span class="stringliteral">"Input 0"</span>, <span class="stringliteral">"Output 0"</span>);</div><div class="line"><a name="l02928"></a><span class="lineno"> 2928</span> </div><div class="line"><a name="l02929"></a><span class="lineno"> 2929</span>  <span class="comment">// Extracting new shape for the output</span></div><div class="line"><a name="l02930"></a><span class="lineno"> 2930</span>  <span class="comment">// There are two ways it can be passed</span></div><div class="line"><a name="l02931"></a><span class="lineno"> 2931</span>  <span class="comment">// * First is to define the target shape in the operator built-in options</span></div><div class="line"><a name="l02932"></a><span class="lineno"> 2932</span>  <span class="comment">// * Second is to pass it as a second input tensor</span></div><div class="line"><a name="l02933"></a><span class="lineno"> 2933</span>  std::vector<int32_t> targetShape;</div><div class="line"><a name="l02934"></a><span class="lineno"> 2934</span>  <span class="keywordtype">bool</span> targetShapeFound = <span class="keyword">false</span>;</div><div class="line"><a name="l02935"></a><span class="lineno"> 2935</span>  <span class="comment">// Check if built-in options were given</span></div><div class="line"><a name="l02936"></a><span class="lineno"> 2936</span>  <span class="keywordflow">if</span> (options != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l02937"></a><span class="lineno"> 2937</span>  {</div><div class="line"><a name="l02938"></a><span class="lineno"> 2938</span>  <span class="comment">// make sure the parameter is given</span></div><div class="line"><a name="l02939"></a><span class="lineno"> 2939</span>  <span class="keywordflow">if</span> (options->new_shape.empty() == <span class="keyword">false</span>)</div><div class="line"><a name="l02940"></a><span class="lineno"> 2940</span>  {</div><div class="line"><a name="l02941"></a><span class="lineno"> 2941</span>  targetShape = options->new_shape;</div><div class="line"><a name="l02942"></a><span class="lineno"> 2942</span>  targetShapeFound = <span class="keyword">true</span>;</div><div class="line"><a name="l02943"></a><span class="lineno"> 2943</span>  }</div><div class="line"><a name="l02944"></a><span class="lineno"> 2944</span>  }</div><div class="line"><a name="l02945"></a><span class="lineno"> 2945</span> </div><div class="line"><a name="l02946"></a><span class="lineno"> 2946</span>  <span class="comment">// If there is no built-in option given or if the built-in new_shape parameter was empty</span></div><div class="line"><a name="l02947"></a><span class="lineno"> 2947</span>  <span class="keywordflow">if</span> (!targetShapeFound)</div><div class="line"><a name="l02948"></a><span class="lineno"> 2948</span>  {</div><div class="line"><a name="l02949"></a><span class="lineno"> 2949</span>  <span class="comment">// Check for a second input tensor</span></div><div class="line"><a name="l02950"></a><span class="lineno"> 2950</span>  <span class="keywordflow">if</span> (inputs.size() > 1 && inputs[1] != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l02951"></a><span class="lineno"> 2951</span>  {</div><div class="line"><a name="l02952"></a><span class="lineno"> 2952</span>  <span class="keywordflow">if</span> (inputs[1]->is_variable)</div><div class="line"><a name="l02953"></a><span class="lineno"> 2953</span>  {</div><div class="line"><a name="l02954"></a><span class="lineno"> 2954</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>( <span class="stringliteral">"Target shapes defined in non-const input tensors is not supported"</span>);</div><div class="line"><a name="l02955"></a><span class="lineno"> 2955</span>  }</div><div class="line"><a name="l02956"></a><span class="lineno"> 2956</span> </div><div class="line"><a name="l02957"></a><span class="lineno"> 2957</span>  <span class="keywordflow">if</span> (inputs[1]->shape.size() != 1)</div><div class="line"><a name="l02958"></a><span class="lineno"> 2958</span>  {</div><div class="line"><a name="l02959"></a><span class="lineno"> 2959</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>(<span class="stringliteral">"Target 'shape' input is not a 1D tensor"</span>);</div><div class="line"><a name="l02960"></a><span class="lineno"> 2960</span>  }</div><div class="line"><a name="l02961"></a><span class="lineno"> 2961</span> </div><div class="line"><a name="l02962"></a><span class="lineno"> 2962</span>  <span class="keywordflow">if</span> (inputs[1]->type != tflite::TensorType_INT32)</div><div class="line"><a name="l02963"></a><span class="lineno"> 2963</span>  {</div><div class="line"><a name="l02964"></a><span class="lineno"> 2964</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>(<span class="stringliteral">"Target 'shape' input is not an int32 type"</span>);</div><div class="line"><a name="l02965"></a><span class="lineno"> 2965</span>  }</div><div class="line"><a name="l02966"></a><span class="lineno"> 2966</span> </div><div class="line"><a name="l02967"></a><span class="lineno"> 2967</span>  <span class="comment">// Extract target shape from input</span></div><div class="line"><a name="l02968"></a><span class="lineno"> 2968</span>  <span class="keyword">auto</span> bufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l02969"></a><span class="lineno"> 2969</span>  <span class="keyword">auto</span> values = <span class="keyword">reinterpret_cast<</span><span class="keyword">const </span>int32_t*<span class="keyword">></span>(bufferPtr->data.data());</div><div class="line"><a name="l02970"></a><span class="lineno"> 2970</span>  <span class="keywordflow">if</span> (values)</div><div class="line"><a name="l02971"></a><span class="lineno"> 2971</span>  {</div><div class="line"><a name="l02972"></a><span class="lineno"> 2972</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < inputs[1]->shape[0]; ++i)</div><div class="line"><a name="l02973"></a><span class="lineno"> 2973</span>  {</div><div class="line"><a name="l02974"></a><span class="lineno"> 2974</span>  targetShape.push_back(values[i]);</div><div class="line"><a name="l02975"></a><span class="lineno"> 2975</span>  }</div><div class="line"><a name="l02976"></a><span class="lineno"> 2976</span>  }</div><div class="line"><a name="l02977"></a><span class="lineno"> 2977</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l02978"></a><span class="lineno"> 2978</span>  {</div><div class="line"><a name="l02979"></a><span class="lineno"> 2979</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l02980"></a><span class="lineno"> 2980</span>  {</div><div class="line"><a name="l02981"></a><span class="lineno"> 2981</span>  <span class="comment">// We attempt to infer during Runtime.</span></div><div class="line"><a name="l02982"></a><span class="lineno"> 2982</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> reshapeShapes = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(inputs[1]).<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l02983"></a><span class="lineno"> 2983</span> </div><div class="line"><a name="l02984"></a><span class="lineno"> 2984</span>  <span class="keywordflow">if</span> (reshapeShapes[0] == actualOutputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l02985"></a><span class="lineno"> 2985</span>  {</div><div class="line"><a name="l02986"></a><span class="lineno"> 2986</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < actualOutputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>().<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++i)</div><div class="line"><a name="l02987"></a><span class="lineno"> 2987</span>  {</div><div class="line"><a name="l02988"></a><span class="lineno"> 2988</span>  targetShape.push_back(actualOutputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i]);</div><div class="line"><a name="l02989"></a><span class="lineno"> 2989</span>  }</div><div class="line"><a name="l02990"></a><span class="lineno"> 2990</span>  }</div><div class="line"><a name="l02991"></a><span class="lineno"> 2991</span>  <span class="comment">// The parser only supports shape (batch, -1) or (-1) for non-constant shape input.</span></div><div class="line"><a name="l02992"></a><span class="lineno"> 2992</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (reshapeShapes[0] > 2)</div><div class="line"><a name="l02993"></a><span class="lineno"> 2993</span>  {</div><div class="line"><a name="l02994"></a><span class="lineno"> 2994</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(fmt::format(<span class="stringliteral">"Invalid input shape '{}' in Reshape layer '{}' {}. "</span></div><div class="line"><a name="l02995"></a><span class="lineno"> 2995</span>  <span class="stringliteral">"When inferring during runtime, the parser only supports "</span></div><div class="line"><a name="l02996"></a><span class="lineno"> 2996</span>  <span class="stringliteral">"shape (batch, -1) or (-1) for target shape input."</span>,</div><div class="line"><a name="l02997"></a><span class="lineno"> 2997</span>  reshapeShapes[0],</div><div class="line"><a name="l02998"></a><span class="lineno"> 2998</span>  layerName,</div><div class="line"><a name="l02999"></a><span class="lineno"> 2999</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03000"></a><span class="lineno"> 3000</span>  }</div><div class="line"><a name="l03001"></a><span class="lineno"> 3001</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l03002"></a><span class="lineno"> 3002</span>  {</div><div class="line"><a name="l03003"></a><span class="lineno"> 3003</span>  <span class="keyword">const</span> int32_t numInputElements = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>();</div><div class="line"><a name="l03004"></a><span class="lineno"> 3004</span>  <span class="keyword">const</span> int32_t inputTensorShape = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0];</div><div class="line"><a name="l03005"></a><span class="lineno"> 3005</span>  <span class="keywordflow">if</span> (reshapeShapes[0] == 1)</div><div class="line"><a name="l03006"></a><span class="lineno"> 3006</span>  {</div><div class="line"><a name="l03007"></a><span class="lineno"> 3007</span>  targetShape = {numInputElements};</div><div class="line"><a name="l03008"></a><span class="lineno"> 3008</span>  }</div><div class="line"><a name="l03009"></a><span class="lineno"> 3009</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (reshapeShapes[0] == 2)</div><div class="line"><a name="l03010"></a><span class="lineno"> 3010</span>  {</div><div class="line"><a name="l03011"></a><span class="lineno"> 3011</span>  targetShape = {inputTensorShape, numInputElements / inputTensorShape};</div><div class="line"><a name="l03012"></a><span class="lineno"> 3012</span>  }</div><div class="line"><a name="l03013"></a><span class="lineno"> 3013</span>  }</div><div class="line"><a name="l03014"></a><span class="lineno"> 3014</span>  }</div><div class="line"><a name="l03015"></a><span class="lineno"> 3015</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> std::exception& exc)</div><div class="line"><a name="l03016"></a><span class="lineno"> 3016</span>  {</div><div class="line"><a name="l03017"></a><span class="lineno"> 3017</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>(<span class="stringliteral">"Failed attempt to infer during runtime the target shape input for "</span></div><div class="line"><a name="l03018"></a><span class="lineno"> 3018</span>  <span class="stringliteral">"Reshape operation. Reshape operator target shape input buffer data "</span></div><div class="line"><a name="l03019"></a><span class="lineno"> 3019</span>  <span class="stringliteral">"is null. "</span> << exc.what());</div><div class="line"><a name="l03020"></a><span class="lineno"> 3020</span>  }</div><div class="line"><a name="l03021"></a><span class="lineno"> 3021</span>  }</div><div class="line"><a name="l03022"></a><span class="lineno"> 3022</span>  }</div><div class="line"><a name="l03023"></a><span class="lineno"> 3023</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l03024"></a><span class="lineno"> 3024</span>  {</div><div class="line"><a name="l03025"></a><span class="lineno"> 3025</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>(<span class="stringliteral">"Target shape not defined in reshape parameters or input tensor. "</span></div><div class="line"><a name="l03026"></a><span class="lineno"> 3026</span>  <span class="stringliteral">"At least one method required"</span>);</div><div class="line"><a name="l03027"></a><span class="lineno"> 3027</span>  }</div><div class="line"><a name="l03028"></a><span class="lineno"> 3028</span>  }</div><div class="line"><a name="l03029"></a><span class="lineno"> 3029</span> </div><div class="line"><a name="l03030"></a><span class="lineno"> 3030</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> reshapeOutputTensorInfo =</div><div class="line"><a name="l03031"></a><span class="lineno"> 3031</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaebfa9a01a0bb8a0935114ff0140cc45">TfLiteParserImpl::OutputShapeOfReshape</a>(inputTensorInfo, targetShape);</div><div class="line"><a name="l03032"></a><span class="lineno"> 3032</span> </div><div class="line"><a name="l03033"></a><span class="lineno"> 3033</span>  <span class="comment">// Check for valid input size and that reshape parameters equal output shape</span></div><div class="line"><a name="l03034"></a><span class="lineno"> 3034</span>  <span class="comment">// The output shape can be provided to us in 2 ways:</span></div><div class="line"><a name="l03035"></a><span class="lineno"> 3035</span>  <span class="comment">// 1. through the normal 'shape' parameter given by outputs[indx]->shape</span></div><div class="line"><a name="l03036"></a><span class="lineno"> 3036</span>  <span class="comment">// 2. through additional parameter 'shape_signature' given by outputs[indx]->buffer.</span></div><div class="line"><a name="l03037"></a><span class="lineno"> 3037</span>  <span class="comment">// This parameter can sometimes contain -1 value not visible in the 'shape' parameter.</span></div><div class="line"><a name="l03038"></a><span class="lineno"> 3038</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& reshapeOutputTensorShape = reshapeOutputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l03039"></a><span class="lineno"> 3039</span>  <span class="keywordflow">if</span> (inputs.size() > 1 && !<a class="code" href="namespacearmnn_deserializer.xhtml#a97b50f22cd99f0e09e6e48d20a35f6b2">CheckShape</a>(reshapeOutputTensorShape, outputs[0]->shape))</div><div class="line"><a name="l03040"></a><span class="lineno"> 3040</span>  {</div><div class="line"><a name="l03041"></a><span class="lineno"> 3041</span>  <span class="comment">// Attempt to extract output shape from secondary 'shape_signature'</span></div><div class="line"><a name="l03042"></a><span class="lineno"> 3042</span>  <span class="comment">// parameter and try to CheckShape() with this param.</span></div><div class="line"><a name="l03043"></a><span class="lineno"> 3043</span>  std::vector<int32_t> secondaryOutputTargetShape = outputs[0]->shape_signature;</div><div class="line"><a name="l03044"></a><span class="lineno"> 3044</span> </div><div class="line"><a name="l03045"></a><span class="lineno"> 3045</span>  <span class="comment">// if outputs[0]->shape_signature contain a -1 value, we need to compute its actual value</span></div><div class="line"><a name="l03046"></a><span class="lineno"> 3046</span>  <span class="comment">// from reshape input in order to correctly verify reshape parameters equal output shape</span></div><div class="line"><a name="l03047"></a><span class="lineno"> 3047</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> secondaryReshapeOutputTensorInfo =</div><div class="line"><a name="l03048"></a><span class="lineno"> 3048</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaebfa9a01a0bb8a0935114ff0140cc45">TfLiteParserImpl::OutputShapeOfReshape</a>(inputTensorInfo, secondaryOutputTargetShape);</div><div class="line"><a name="l03049"></a><span class="lineno"> 3049</span> </div><div class="line"><a name="l03050"></a><span class="lineno"> 3050</span>  <span class="keywordflow">if</span> (!<a class="code" href="namespacearmnn_deserializer.xhtml#a97b50f22cd99f0e09e6e48d20a35f6b2">CheckShape</a>(reshapeOutputTensorShape, secondaryReshapeOutputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()))</div><div class="line"><a name="l03051"></a><span class="lineno"> 3051</span>  {</div><div class="line"><a name="l03052"></a><span class="lineno"> 3052</span>  std::stringstream ss;</div><div class="line"><a name="l03053"></a><span class="lineno"> 3053</span>  ss << <span class="stringliteral">"New shape defined in reshape parameters "</span></div><div class="line"><a name="l03054"></a><span class="lineno"> 3054</span>  << reshapeOutputTensorShape</div><div class="line"><a name="l03055"></a><span class="lineno"> 3055</span>  << <span class="stringliteral">" does not equal output shape "</span></div><div class="line"><a name="l03056"></a><span class="lineno"> 3056</span>  << actualOutputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()</div><div class="line"><a name="l03057"></a><span class="lineno"> 3057</span>  << <span class="stringliteral">": "</span></div><div class="line"><a name="l03058"></a><span class="lineno"> 3058</span>  << <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString();</div><div class="line"><a name="l03059"></a><span class="lineno"> 3059</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(ss.str());</div><div class="line"><a name="l03060"></a><span class="lineno"> 3060</span>  }</div><div class="line"><a name="l03061"></a><span class="lineno"> 3061</span>  }</div><div class="line"><a name="l03062"></a><span class="lineno"> 3062</span>  <span class="keyword">auto</span> outputTensorIds = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03063"></a><span class="lineno"> 3063</span> </div><div class="line"><a name="l03064"></a><span class="lineno"> 3064</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">ReshapeDescriptor</a> reshapeDesc;</div><div class="line"><a name="l03065"></a><span class="lineno"> 3065</span>  reshapeDesc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = reshapeOutputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l03066"></a><span class="lineno"> 3066</span>  m_TensorInfos[outputTensorIds[0]] = reshapeOutputTensorInfo;</div><div class="line"><a name="l03067"></a><span class="lineno"> 3067</span> </div><div class="line"><a name="l03068"></a><span class="lineno"> 3068</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str());</div><div class="line"><a name="l03069"></a><span class="lineno"> 3069</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l03070"></a><span class="lineno"> 3070</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(reshapeOutputTensorInfo);</div><div class="line"><a name="l03071"></a><span class="lineno"> 3071</span> </div><div class="line"><a name="l03072"></a><span class="lineno"> 3072</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l03073"></a><span class="lineno"> 3073</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l03074"></a><span class="lineno"> 3074</span> </div><div class="line"><a name="l03075"></a><span class="lineno"> 3075</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l03076"></a><span class="lineno"> 3076</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l03077"></a><span class="lineno"> 3077</span> }</div><div class="line"><a name="l03078"></a><span class="lineno"> 3078</span> </div><div class="line"><a name="l03079"></a><span class="lineno"> 3079</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseResizeBilinear(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l03080"></a><span class="lineno"> 3080</span> {</div><div class="line"><a name="l03081"></a><span class="lineno"> 3081</span>  ParseResize(subgraphIndex, operatorIndex, ResizeMethod::Bilinear);</div><div class="line"><a name="l03082"></a><span class="lineno"> 3082</span> }</div><div class="line"><a name="l03083"></a><span class="lineno"> 3083</span> </div><div class="line"><a name="l03084"></a><span class="lineno"> 3084</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseResizeNearestNeighbor(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l03085"></a><span class="lineno"> 3085</span> {</div><div class="line"><a name="l03086"></a><span class="lineno"> 3086</span>  ParseResize(subgraphIndex, operatorIndex, ResizeMethod::NearestNeighbor);</div><div class="line"><a name="l03087"></a><span class="lineno"> 3087</span> }</div><div class="line"><a name="l03088"></a><span class="lineno"> 3088</span> </div><div class="line"><a name="l03089"></a><span class="lineno"> 3089</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseResize(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex, <a class="code" href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4">ResizeMethod</a> resizeMethod)</div><div class="line"><a name="l03090"></a><span class="lineno"> 3090</span> {</div><div class="line"><a name="l03091"></a><span class="lineno"> 3091</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03092"></a><span class="lineno"> 3092</span> </div><div class="line"><a name="l03093"></a><span class="lineno"> 3093</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03094"></a><span class="lineno"> 3094</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l03095"></a><span class="lineno"> 3095</span> </div><div class="line"><a name="l03096"></a><span class="lineno"> 3096</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03097"></a><span class="lineno"> 3097</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l03098"></a><span class="lineno"> 3098</span> </div><div class="line"><a name="l03099"></a><span class="lineno"> 3099</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> sizeTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l03100"></a><span class="lineno"> 3100</span> </div><div class="line"><a name="l03101"></a><span class="lineno"> 3101</span>  <span class="comment">// Data for the parsed tensor args (size) must be stored locally.</span></div><div class="line"><a name="l03102"></a><span class="lineno"> 3102</span>  std::vector<int32_t> sizeTensorData(sizeTensorInfo.GetNumElements());</div><div class="line"><a name="l03103"></a><span class="lineno"> 3103</span> </div><div class="line"><a name="l03104"></a><span class="lineno"> 3104</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> sizeBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l03105"></a><span class="lineno"> 3105</span>  ::memcpy(sizeTensorData.data(), sizeBufferPtr->data.data(), sizeTensorInfo.GetNumBytes());</div><div class="line"><a name="l03106"></a><span class="lineno"> 3106</span> </div><div class="line"><a name="l03107"></a><span class="lineno"> 3107</span>  <a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">ResizeDescriptor</a> desc;</div><div class="line"><a name="l03108"></a><span class="lineno"> 3108</span>  desc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">m_Method</a> = resizeMethod;</div><div class="line"><a name="l03109"></a><span class="lineno"> 3109</span>  desc.m_TargetHeight = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span> (sizeTensorData[0]);</div><div class="line"><a name="l03110"></a><span class="lineno"> 3110</span>  desc.m_TargetWidth = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span> (sizeTensorData[1]);</div><div class="line"><a name="l03111"></a><span class="lineno"> 3111</span>  desc.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l03112"></a><span class="lineno"> 3112</span> </div><div class="line"><a name="l03113"></a><span class="lineno"> 3113</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Resize:"</span>);</div><div class="line"><a name="l03114"></a><span class="lineno"> 3114</span> </div><div class="line"><a name="l03115"></a><span class="lineno"> 3115</span>  <span class="keywordflow">switch</span> (resizeMethod)</div><div class="line"><a name="l03116"></a><span class="lineno"> 3116</span>  {</div><div class="line"><a name="l03117"></a><span class="lineno"> 3117</span>  <span class="keywordflow">case</span> ResizeMethod::Bilinear:</div><div class="line"><a name="l03118"></a><span class="lineno"> 3118</span>  {</div><div class="line"><a name="l03119"></a><span class="lineno"> 3119</span>  layerName += fmt::format(<span class="stringliteral">"BILINEAR:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03120"></a><span class="lineno"> 3120</span> </div><div class="line"><a name="l03121"></a><span class="lineno"> 3121</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l03122"></a><span class="lineno"> 3122</span>  <span class="keyword">const</span> <span class="keyword">auto</span> * options = operatorPtr->builtin_options.AsResizeBilinearOptions();</div><div class="line"><a name="l03123"></a><span class="lineno"> 3123</span> </div><div class="line"><a name="l03124"></a><span class="lineno"> 3124</span>  desc.m_AlignCorners = options->align_corners;</div><div class="line"><a name="l03125"></a><span class="lineno"> 3125</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l03126"></a><span class="lineno"> 3126</span>  }</div><div class="line"><a name="l03127"></a><span class="lineno"> 3127</span>  <span class="keywordflow">case</span> ResizeMethod::NearestNeighbor:</div><div class="line"><a name="l03128"></a><span class="lineno"> 3128</span>  {</div><div class="line"><a name="l03129"></a><span class="lineno"> 3129</span>  layerName += fmt::format(<span class="stringliteral">"NEARESTNEIGHBOR:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03130"></a><span class="lineno"> 3130</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l03131"></a><span class="lineno"> 3131</span>  }</div><div class="line"><a name="l03132"></a><span class="lineno"> 3132</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l03133"></a><span class="lineno"> 3133</span>  {</div><div class="line"><a name="l03134"></a><span class="lineno"> 3134</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03135"></a><span class="lineno"> 3135</span>  fmt::format(<span class="stringliteral">"Unexpected ResizeMethod[{}] when creating layerName {} "</span>,</div><div class="line"><a name="l03136"></a><span class="lineno"> 3136</span>  static_cast<int>(resizeMethod), <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03137"></a><span class="lineno"> 3137</span>  }</div><div class="line"><a name="l03138"></a><span class="lineno"> 3138</span>  }</div><div class="line"><a name="l03139"></a><span class="lineno"> 3139</span> </div><div class="line"><a name="l03140"></a><span class="lineno"> 3140</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l03141"></a><span class="lineno"> 3141</span> </div><div class="line"><a name="l03142"></a><span class="lineno"> 3142</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddResizeLayer(desc, layerName.c_str());</div><div class="line"><a name="l03143"></a><span class="lineno"> 3143</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l03144"></a><span class="lineno"> 3144</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l03145"></a><span class="lineno"> 3145</span>  CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, <span class="stringliteral">"Input 0"</span>, <span class="stringliteral">"Output 0"</span>);</div><div class="line"><a name="l03146"></a><span class="lineno"> 3146</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l03147"></a><span class="lineno"> 3147</span> </div><div class="line"><a name="l03148"></a><span class="lineno"> 3148</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l03149"></a><span class="lineno"> 3149</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l03150"></a><span class="lineno"> 3150</span> </div><div class="line"><a name="l03151"></a><span class="lineno"> 3151</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l03152"></a><span class="lineno"> 3152</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</div><div class="line"><a name="l03153"></a><span class="lineno"> 3153</span> }</div><div class="line"><a name="l03154"></a><span class="lineno"> 3154</span> </div><div class="line"><a name="l03155"></a><span class="lineno"> 3155</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseConcatenation(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l03156"></a><span class="lineno"> 3156</span> {</div><div class="line"><a name="l03157"></a><span class="lineno"> 3157</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03158"></a><span class="lineno"> 3158</span> </div><div class="line"><a name="l03159"></a><span class="lineno"> 3159</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l03160"></a><span class="lineno"> 3160</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsConcatenationOptions();</div><div class="line"><a name="l03161"></a><span class="lineno"> 3161</span> </div><div class="line"><a name="l03162"></a><span class="lineno"> 3162</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a>(options, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03163"></a><span class="lineno"> 3163</span> </div><div class="line"><a name="l03164"></a><span class="lineno"> 3164</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03165"></a><span class="lineno"> 3165</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03166"></a><span class="lineno"> 3166</span>  <span class="keyword">auto</span> inputTensorIds = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03167"></a><span class="lineno"> 3167</span> </div><div class="line"><a name="l03168"></a><span class="lineno"> 3168</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l03169"></a><span class="lineno"> 3169</span> </div><div class="line"><a name="l03170"></a><span class="lineno"> 3170</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numConcatView = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(inputs.size());</div><div class="line"><a name="l03171"></a><span class="lineno"> 3171</span>  uint32_t inputRank = InputTensorInfo(subgraphIndex, operatorIndex, 0).<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l03172"></a><span class="lineno"> 3172</span> </div><div class="line"><a name="l03173"></a><span class="lineno"> 3173</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatDimInput = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(</div><div class="line"><a name="l03174"></a><span class="lineno"> 3174</span>  (<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(inputRank) + options->axis) % <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(inputRank));</div><div class="line"><a name="l03175"></a><span class="lineno"> 3175</span> </div><div class="line"><a name="l03176"></a><span class="lineno"> 3176</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> concatDescriptor(static_cast<uint32_t>(numConcatView), inputRank);</div><div class="line"><a name="l03177"></a><span class="lineno"> 3177</span>  concatDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a5b192c5fcd96a0f75542524cf646b355">SetConcatAxis</a>(concatDimInput);</div><div class="line"><a name="l03178"></a><span class="lineno"> 3178</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> mergeDimOrigin = 0;</div><div class="line"><a name="l03179"></a><span class="lineno"> 3179</span> </div><div class="line"><a name="l03180"></a><span class="lineno"> 3180</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> viewIndex = 0; viewIndex < numConcatView; ++viewIndex)</div><div class="line"><a name="l03181"></a><span class="lineno"> 3181</span>  {</div><div class="line"><a name="l03182"></a><span class="lineno"> 3182</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, viewIndex);</div><div class="line"><a name="l03183"></a><span class="lineno"> 3183</span> </div><div class="line"><a name="l03184"></a><span class="lineno"> 3184</span>  <span class="comment">// This set up concatDescriptor view origin</span></div><div class="line"><a name="l03185"></a><span class="lineno"> 3185</span>  <a class="code" href="namespacearmnn_utils.xhtml#a523deabeb7d0a884028b35eebfd1cb6c">armnnUtils::ProcessConcatInputTensorInfo</a>(</div><div class="line"><a name="l03186"></a><span class="lineno"> 3186</span>  inputTensorInfo, concatDescriptor, concatDimInput, viewIndex, mergeDimOrigin);</div><div class="line"><a name="l03187"></a><span class="lineno"> 3187</span>  }</div><div class="line"><a name="l03188"></a><span class="lineno"> 3188</span> </div><div class="line"><a name="l03189"></a><span class="lineno"> 3189</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Concatenation:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03190"></a><span class="lineno"> 3190</span> </div><div class="line"><a name="l03191"></a><span class="lineno"> 3191</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddConcatLayer(concatDescriptor, layerName.c_str());</div><div class="line"><a name="l03192"></a><span class="lineno"> 3192</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l03193"></a><span class="lineno"> 3193</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {});</div><div class="line"><a name="l03194"></a><span class="lineno"> 3194</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l03195"></a><span class="lineno"> 3195</span> </div><div class="line"><a name="l03196"></a><span class="lineno"> 3196</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l03197"></a><span class="lineno"> 3197</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes});</div><div class="line"><a name="l03198"></a><span class="lineno"> 3198</span> </div><div class="line"><a name="l03199"></a><span class="lineno"> 3199</span>  <span class="comment">// add fused activation layer</span></div><div class="line"><a name="l03200"></a><span class="lineno"> 3200</span>  layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function);</div><div class="line"><a name="l03201"></a><span class="lineno"> 3201</span> </div><div class="line"><a name="l03202"></a><span class="lineno"> 3202</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l03203"></a><span class="lineno"> 3203</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l03204"></a><span class="lineno"> 3204</span> }</div><div class="line"><a name="l03205"></a><span class="lineno"> 3205</span> </div><div class="line"><a name="l03206"></a><span class="lineno"> 3206</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseFullyConnected(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l03207"></a><span class="lineno"> 3207</span> {</div><div class="line"><a name="l03208"></a><span class="lineno"> 3208</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03209"></a><span class="lineno"> 3209</span> </div><div class="line"><a name="l03210"></a><span class="lineno"> 3210</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorRfr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l03211"></a><span class="lineno"> 3211</span>  <span class="keyword">const</span> <span class="keyword">auto</span> options = operatorRfr->builtin_options.AsFullyConnectedOptions();</div><div class="line"><a name="l03212"></a><span class="lineno"> 3212</span> </div><div class="line"><a name="l03213"></a><span class="lineno"> 3213</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a>(options, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03214"></a><span class="lineno"> 3214</span> </div><div class="line"><a name="l03215"></a><span class="lineno"> 3215</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> desc;</div><div class="line"><a name="l03216"></a><span class="lineno"> 3216</span>  desc.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l03217"></a><span class="lineno"> 3217</span>  desc.<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="l03218"></a><span class="lineno"> 3218</span> </div><div class="line"><a name="l03219"></a><span class="lineno"> 3219</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03220"></a><span class="lineno"> 3220</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03221"></a><span class="lineno"> 3221</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l03222"></a><span class="lineno"> 3222</span> </div><div class="line"><a name="l03223"></a><span class="lineno"> 3223</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> filterTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l03224"></a><span class="lineno"> 3224</span> </div><div class="line"><a name="l03225"></a><span class="lineno"> 3225</span>  <span class="comment">// Fully Connected Layer accepts two dimensional weights input</span></div><div class="line"><a name="l03226"></a><span class="lineno"> 3226</span>  int32_t weightsDimension = <span class="keyword">static_cast<</span>int32_t<span class="keyword">></span>(filterTensorInfo.GetNumDimensions());</div><div class="line"><a name="l03227"></a><span class="lineno"> 3227</span>  <span class="keywordflow">if</span> (weightsDimension != 2)</div><div class="line"><a name="l03228"></a><span class="lineno"> 3228</span>  {</div><div class="line"><a name="l03229"></a><span class="lineno"> 3229</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03230"></a><span class="lineno"> 3230</span>  fmt::format(<span class="stringliteral">"Dimension {} for Fully Connected weights is not supported by Armnn. "</span></div><div class="line"><a name="l03231"></a><span class="lineno"> 3231</span>  <span class="stringliteral">"Node {}"</span>,</div><div class="line"><a name="l03232"></a><span class="lineno"> 3232</span>  weightsDimension,</div><div class="line"><a name="l03233"></a><span class="lineno"> 3233</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03234"></a><span class="lineno"> 3234</span>  }</div><div class="line"><a name="l03235"></a><span class="lineno"> 3235</span> </div><div class="line"><a name="l03236"></a><span class="lineno"> 3236</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l03237"></a><span class="lineno"> 3237</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"FullyConnected:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03238"></a><span class="lineno"> 3238</span> </div><div class="line"><a name="l03239"></a><span class="lineno"> 3239</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l03240"></a><span class="lineno"> 3240</span>  <span class="comment">// Add the first input tensor to the registration list</span></div><div class="line"><a name="l03241"></a><span class="lineno"> 3241</span>  std::vector<unsigned int> tensorIndexesToRegister = {inputTensorIndexes[0]};</div><div class="line"><a name="l03242"></a><span class="lineno"> 3242</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l03243"></a><span class="lineno"> 3243</span> </div><div class="line"><a name="l03244"></a><span class="lineno"> 3244</span>  desc.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a2d3dcfc10f90adedc995b64211dab6e9">m_ConstantWeights</a> = IsConstTensor(inputs[1]);</div><div class="line"><a name="l03245"></a><span class="lineno"> 3245</span> </div><div class="line"><a name="l03246"></a><span class="lineno"> 3246</span>  <span class="comment">// Add the weights input to the registration list, constant layers will be added by SetupConstantLayers if constant.</span></div><div class="line"><a name="l03247"></a><span class="lineno"> 3247</span>  tensorIndexesToRegister.emplace_back(inputTensorIndexes[1]);</div><div class="line"><a name="l03248"></a><span class="lineno"> 3248</span> </div><div class="line"><a name="l03249"></a><span class="lineno"> 3249</span>  <span class="keywordflow">if</span> (ShouldConstantTensorBeConverted(inputs[1], inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>(), filterTensorInfo.GetDataType()))</div><div class="line"><a name="l03250"></a><span class="lineno"> 3250</span>  {</div><div class="line"><a name="l03251"></a><span class="lineno"> 3251</span>  m_ConstantsToDequantize.emplace_back(inputs[1]->buffer);</div><div class="line"><a name="l03252"></a><span class="lineno"> 3252</span>  }</div><div class="line"><a name="l03253"></a><span class="lineno"> 3253</span> </div><div class="line"><a name="l03254"></a><span class="lineno"> 3254</span>  <span class="keywordflow">if</span> (inputs.size() == 3)</div><div class="line"><a name="l03255"></a><span class="lineno"> 3255</span>  {</div><div class="line"><a name="l03256"></a><span class="lineno"> 3256</span>  desc.<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="l03257"></a><span class="lineno"> 3257</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 2);</div><div class="line"><a name="l03258"></a><span class="lineno"> 3258</span> </div><div class="line"><a name="l03259"></a><span class="lineno"> 3259</span>  <span class="comment">// Add the biases input to the registration list, constant layer will be added by SetupConstantLayers.</span></div><div class="line"><a name="l03260"></a><span class="lineno"> 3260</span>  tensorIndexesToRegister.emplace_back(inputTensorIndexes[2]);</div><div class="line"><a name="l03261"></a><span class="lineno"> 3261</span> </div><div class="line"><a name="l03262"></a><span class="lineno"> 3262</span>  <span class="keywordflow">if</span> (ShouldConstantTensorBeConverted(inputs[2], inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>(), biasTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()))</div><div class="line"><a name="l03263"></a><span class="lineno"> 3263</span>  {</div><div class="line"><a name="l03264"></a><span class="lineno"> 3264</span>  m_ConstantsToDequantize.emplace_back(inputs[2]->buffer);</div><div class="line"><a name="l03265"></a><span class="lineno"> 3265</span>  }</div><div class="line"><a name="l03266"></a><span class="lineno"> 3266</span>  }</div><div class="line"><a name="l03267"></a><span class="lineno"> 3267</span> </div><div class="line"><a name="l03268"></a><span class="lineno"> 3268</span>  <span class="comment">// Filters and biases are always passed to fully connected as inputs</span></div><div class="line"><a name="l03269"></a><span class="lineno"> 3269</span>  layer = m_Network->AddFullyConnectedLayer(desc, layerName.c_str());</div><div class="line"><a name="l03270"></a><span class="lineno"> 3270</span> </div><div class="line"><a name="l03271"></a><span class="lineno"> 3271</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l03272"></a><span class="lineno"> 3272</span> </div><div class="line"><a name="l03273"></a><span class="lineno"> 3273</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> startingSlotIndex = 0;</div><div class="line"><a name="l03274"></a><span class="lineno"> 3274</span>  <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() > 2)</div><div class="line"><a name="l03275"></a><span class="lineno"> 3275</span>  {</div><div class="line"><a name="l03276"></a><span class="lineno"> 3276</span>  <span class="comment">// Add reshape to flatten to 2D [batch_size, input_size],</span></div><div class="line"><a name="l03277"></a><span class="lineno"> 3277</span>  <span class="comment">// where "input_size" corresponds to the number of inputs to the layer,</span></div><div class="line"><a name="l03278"></a><span class="lineno"> 3278</span>  <span class="comment">// matching the second dimension of weights,</span></div><div class="line"><a name="l03279"></a><span class="lineno"> 3279</span>  <span class="comment">// and "batch_size" is calculated by dividing the number of elements by "input_size".</span></div><div class="line"><a name="l03280"></a><span class="lineno"> 3280</span>  std::vector<unsigned int> reshapedDimensions(2);</div><div class="line"><a name="l03281"></a><span class="lineno"> 3281</span>  reshapedDimensions[1] = filterTensorInfo.GetShape()[1];</div><div class="line"><a name="l03282"></a><span class="lineno"> 3282</span>  reshapedDimensions[0] = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / reshapedDimensions[1];</div><div class="line"><a name="l03283"></a><span class="lineno"> 3283</span> </div><div class="line"><a name="l03284"></a><span class="lineno"> 3284</span>  <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() % reshapedDimensions[1] != 0)</div><div class="line"><a name="l03285"></a><span class="lineno"> 3285</span>  {</div><div class="line"><a name="l03286"></a><span class="lineno"> 3286</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03287"></a><span class="lineno"> 3287</span>  fmt::format(<span class="stringliteral">"Failed to deduce input tensor shape from filter size {} {}"</span>,</div><div class="line"><a name="l03288"></a><span class="lineno"> 3288</span>  reshapedDimensions[1],</div><div class="line"><a name="l03289"></a><span class="lineno"> 3289</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03290"></a><span class="lineno"> 3290</span>  }</div><div class="line"><a name="l03291"></a><span class="lineno"> 3291</span> </div><div class="line"><a name="l03292"></a><span class="lineno"> 3292</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> reshapedTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l03293"></a><span class="lineno"> 3293</span>  reshapedTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>{ 2, reshapedDimensions.data() });</div><div class="line"><a name="l03294"></a><span class="lineno"> 3294</span>  inputTensorInfo = reshapedTensorInfo;</div><div class="line"><a name="l03295"></a><span class="lineno"> 3295</span> </div><div class="line"><a name="l03296"></a><span class="lineno"> 3296</span>  std::string reshapeLayerName = fmt::format(<span class="stringliteral">"Reshape_for:{}"</span>, layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>());</div><div class="line"><a name="l03297"></a><span class="lineno"> 3297</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">armnn::ReshapeDescriptor</a> reshapeDescriptor;</div><div class="line"><a name="l03298"></a><span class="lineno"> 3298</span>  reshapeDescriptor.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = reshapedTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l03299"></a><span class="lineno"> 3299</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* reshapeLayer = m_Network->AddReshapeLayer(reshapeDescriptor,</div><div class="line"><a name="l03300"></a><span class="lineno"> 3300</span>  reshapeLayerName.c_str());</div><div class="line"><a name="l03301"></a><span class="lineno"> 3301</span> </div><div class="line"><a name="l03302"></a><span class="lineno"> 3302</span>  reshapeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(reshapedTensorInfo);</div><div class="line"><a name="l03303"></a><span class="lineno"> 3303</span>  reshapeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l03304"></a><span class="lineno"> 3304</span> </div><div class="line"><a name="l03305"></a><span class="lineno"> 3305</span>  RegisterInputSlots(subgraphIndex, operatorIndex, reshapeLayer, {inputTensorIndexes[0]});</div><div class="line"><a name="l03306"></a><span class="lineno"> 3306</span>  <span class="comment">// Fc layer connects to the reshape layer, so we skip the first input slot when registering fc's input slots</span></div><div class="line"><a name="l03307"></a><span class="lineno"> 3307</span>  tensorIndexesToRegister.erase(tensorIndexesToRegister.begin());</div><div class="line"><a name="l03308"></a><span class="lineno"> 3308</span>  startingSlotIndex = 1;</div><div class="line"><a name="l03309"></a><span class="lineno"> 3309</span>  }</div><div class="line"><a name="l03310"></a><span class="lineno"> 3310</span> </div><div class="line"><a name="l03311"></a><span class="lineno"> 3311</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, tensorIndexesToRegister, startingSlotIndex);</div><div class="line"><a name="l03312"></a><span class="lineno"> 3312</span> </div><div class="line"><a name="l03313"></a><span class="lineno"> 3313</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = OutputTensorInfoFromShapes(subgraphIndex, operatorIndex, layer, 0,</div><div class="line"><a name="l03314"></a><span class="lineno"> 3314</span>  { inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(),</div><div class="line"><a name="l03315"></a><span class="lineno"> 3315</span>  filterTensorInfo.GetShape() });</div><div class="line"><a name="l03316"></a><span class="lineno"> 3316</span> </div><div class="line"><a name="l03317"></a><span class="lineno"> 3317</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l03318"></a><span class="lineno"> 3318</span> </div><div class="line"><a name="l03319"></a><span class="lineno"> 3319</span>  <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() > 2)</div><div class="line"><a name="l03320"></a><span class="lineno"> 3320</span>  {</div><div class="line"><a name="l03321"></a><span class="lineno"> 3321</span>  <span class="comment">// Calculate reshape to flatten to 2D [batch_size, input_size]</span></div><div class="line"><a name="l03322"></a><span class="lineno"> 3322</span>  std::vector<unsigned int> reshapedDimensions(2);</div><div class="line"><a name="l03323"></a><span class="lineno"> 3323</span>  reshapedDimensions[1] = filterTensorInfo.GetShape()[0];</div><div class="line"><a name="l03324"></a><span class="lineno"> 3324</span>  reshapedDimensions[0] = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / reshapedDimensions[1];</div><div class="line"><a name="l03325"></a><span class="lineno"> 3325</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> reshapedOutputTensorInfo = outputTensorInfo;</div><div class="line"><a name="l03326"></a><span class="lineno"> 3326</span>  <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() % reshapedDimensions[1] != 0)</div><div class="line"><a name="l03327"></a><span class="lineno"> 3327</span>  {</div><div class="line"><a name="l03328"></a><span class="lineno"> 3328</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03329"></a><span class="lineno"> 3329</span>  fmt::format(<span class="stringliteral">"Failed to deduce output tensor shape from filter size {} {}"</span>,</div><div class="line"><a name="l03330"></a><span class="lineno"> 3330</span>  reshapedDimensions[1],</div><div class="line"><a name="l03331"></a><span class="lineno"> 3331</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03332"></a><span class="lineno"> 3332</span>  }</div><div class="line"><a name="l03333"></a><span class="lineno"> 3333</span>  reshapedOutputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>{ 2, reshapedDimensions.data() });</div><div class="line"><a name="l03334"></a><span class="lineno"> 3334</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(reshapedOutputTensorInfo);</div><div class="line"><a name="l03335"></a><span class="lineno"> 3335</span> </div><div class="line"><a name="l03336"></a><span class="lineno"> 3336</span>  std::string reshapeLayerName = fmt::format(<span class="stringliteral">"ExpandDims:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03337"></a><span class="lineno"> 3337</span>  layer = AddReshapeLayer(layer, 0, reshapeLayerName, outputTensorInfo);</div><div class="line"><a name="l03338"></a><span class="lineno"> 3338</span>  }</div><div class="line"><a name="l03339"></a><span class="lineno"> 3339</span> </div><div class="line"><a name="l03340"></a><span class="lineno"> 3340</span>  <span class="comment">// we need to add the activation layer and fortunately we don't need to care about the data layout</span></div><div class="line"><a name="l03341"></a><span class="lineno"> 3341</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* fusedActivationLayer = AddFusedActivationLayer(layer, 0,</div><div class="line"><a name="l03342"></a><span class="lineno"> 3342</span>  options->fused_activation_function);</div><div class="line"><a name="l03343"></a><span class="lineno"> 3343</span> </div><div class="line"><a name="l03344"></a><span class="lineno"> 3344</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l03345"></a><span class="lineno"> 3345</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l03346"></a><span class="lineno"> 3346</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, fusedActivationLayer, {outputTensorIndexes[0]});</div><div class="line"><a name="l03347"></a><span class="lineno"> 3347</span> </div><div class="line"><a name="l03348"></a><span class="lineno"> 3348</span>  m_TensorInfos[outputTensorIndexes[0]] = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l03349"></a><span class="lineno"> 3349</span> }</div><div class="line"><a name="l03350"></a><span class="lineno"> 3350</span> </div><div class="line"><a name="l03351"></a><span class="lineno"> 3351</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseDetectionPostProcess(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l03352"></a><span class="lineno"> 3352</span> {</div><div class="line"><a name="l03353"></a><span class="lineno"> 3353</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03354"></a><span class="lineno"> 3354</span> </div><div class="line"><a name="l03355"></a><span class="lineno"> 3355</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l03356"></a><span class="lineno"> 3356</span> </div><div class="line"><a name="l03357"></a><span class="lineno"> 3357</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03358"></a><span class="lineno"> 3358</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03359"></a><span class="lineno"> 3359</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 4);</div><div class="line"><a name="l03360"></a><span class="lineno"> 3360</span> </div><div class="line"><a name="l03361"></a><span class="lineno"> 3361</span>  <span class="comment">// Obtain custom options from flexbuffers</span></div><div class="line"><a name="l03362"></a><span class="lineno"> 3362</span>  <span class="keyword">auto</span> custom_options = operatorPtr->custom_options;</div><div class="line"><a name="l03363"></a><span class="lineno"> 3363</span>  <span class="keyword">const</span> flexbuffers::Map& m = flexbuffers::GetRoot(custom_options.data(), custom_options.size()).AsMap();</div><div class="line"><a name="l03364"></a><span class="lineno"> 3364</span> </div><div class="line"><a name="l03365"></a><span class="lineno"> 3365</span>  <span class="comment">// Obtain descriptor information from tf lite</span></div><div class="line"><a name="l03366"></a><span class="lineno"> 3366</span>  <a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml">DetectionPostProcessDescriptor</a> desc;</div><div class="line"><a name="l03367"></a><span class="lineno"> 3367</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae72089bcab60ac175557f4241b16a014">m_MaxDetections</a> = m[<span class="stringliteral">"max_detections"</span>].AsUInt32();</div><div class="line"><a name="l03368"></a><span class="lineno"> 3368</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a9ae2c9796692ebeafe19a4d3f09c8ea8">m_MaxClassesPerDetection</a> = m[<span class="stringliteral">"max_classes_per_detection"</span>].AsUInt32();</div><div class="line"><a name="l03369"></a><span class="lineno"> 3369</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a4392dd6b4862cc9cf95ae8f1001ba592">m_NmsScoreThreshold</a> = m[<span class="stringliteral">"nms_score_threshold"</span>].AsFloat();</div><div class="line"><a name="l03370"></a><span class="lineno"> 3370</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a53c8a7f33a40e1e240256bcfcf41b101">m_NmsIouThreshold</a> = m[<span class="stringliteral">"nms_iou_threshold"</span>].AsFloat();</div><div class="line"><a name="l03371"></a><span class="lineno"> 3371</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a3a04b0ccee4bb2f21721ee5045e83df4">m_NumClasses</a> = m[<span class="stringliteral">"num_classes"</span>].AsUInt32();</div><div class="line"><a name="l03372"></a><span class="lineno"> 3372</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#aa61510cbd529870182e918ac6e8b9d72">m_ScaleH</a> = m[<span class="stringliteral">"h_scale"</span>].AsFloat();</div><div class="line"><a name="l03373"></a><span class="lineno"> 3373</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ab509802c659de19929f18bad14a35c58">m_ScaleW</a> = m[<span class="stringliteral">"w_scale"</span>].AsFloat();</div><div class="line"><a name="l03374"></a><span class="lineno"> 3374</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae64523937ea910030ad66fee6fddd51f">m_ScaleX</a> = m[<span class="stringliteral">"x_scale"</span>].AsFloat();</div><div class="line"><a name="l03375"></a><span class="lineno"> 3375</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7a2156ec7d9c012ce00bbcc6afcb9028">m_ScaleY</a> = m[<span class="stringliteral">"y_scale"</span>].AsFloat();</div><div class="line"><a name="l03376"></a><span class="lineno"> 3376</span> </div><div class="line"><a name="l03377"></a><span class="lineno"> 3377</span>  <span class="keywordflow">if</span> (!(m[<span class="stringliteral">"use_regular_nms"</span>].IsNull()))</div><div class="line"><a name="l03378"></a><span class="lineno"> 3378</span>  {</div><div class="line"><a name="l03379"></a><span class="lineno"> 3379</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7ed9bc7c26df67d274d5dd4cd83adf0f">m_UseRegularNms</a> = m[<span class="stringliteral">"use_regular_nms"</span>].AsBool();</div><div class="line"><a name="l03380"></a><span class="lineno"> 3380</span>  }</div><div class="line"><a name="l03381"></a><span class="lineno"> 3381</span>  <span class="keywordflow">if</span> (!(m[<span class="stringliteral">"detections_per_class"</span>].IsNull()))</div><div class="line"><a name="l03382"></a><span class="lineno"> 3382</span>  {</div><div class="line"><a name="l03383"></a><span class="lineno"> 3383</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7e2f87544b8bc7e497e1dec8d3ca4055">m_DetectionsPerClass</a> = m[<span class="stringliteral">"detections_per_class"</span>].AsUInt32();</div><div class="line"><a name="l03384"></a><span class="lineno"> 3384</span>  }</div><div class="line"><a name="l03385"></a><span class="lineno"> 3385</span> </div><div class="line"><a name="l03386"></a><span class="lineno"> 3386</span>  <span class="keywordflow">if</span> (desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a53c8a7f33a40e1e240256bcfcf41b101">m_NmsIouThreshold</a> <= 0.0f || desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a53c8a7f33a40e1e240256bcfcf41b101">m_NmsIouThreshold</a> > 1.0f)</div><div class="line"><a name="l03387"></a><span class="lineno"> 3387</span>  {</div><div class="line"><a name="l03388"></a><span class="lineno"> 3388</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"DetectionPostProcessTFLiteParser: Intersection over union threshold "</span></div><div class="line"><a name="l03389"></a><span class="lineno"> 3389</span>  <span class="stringliteral">"must be positive and less than or equal to 1."</span>);</div><div class="line"><a name="l03390"></a><span class="lineno"> 3390</span>  }</div><div class="line"><a name="l03391"></a><span class="lineno"> 3391</span> </div><div class="line"><a name="l03392"></a><span class="lineno"> 3392</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> anchorTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 2);</div><div class="line"><a name="l03393"></a><span class="lineno"> 3393</span>  <span class="keyword">auto</span> anchorTensorAndData = CreateConstTensorNonPermuted(inputs[2], anchorTensorInfo);</div><div class="line"><a name="l03394"></a><span class="lineno"> 3394</span> </div><div class="line"><a name="l03395"></a><span class="lineno"> 3395</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"DetectionPostProcess:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03396"></a><span class="lineno"> 3396</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddDetectionPostProcessLayer(desc, anchorTensorAndData,</div><div class="line"><a name="l03397"></a><span class="lineno"> 3397</span>  layerName.c_str());</div><div class="line"><a name="l03398"></a><span class="lineno"> 3398</span> </div><div class="line"><a name="l03399"></a><span class="lineno"> 3399</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l03400"></a><span class="lineno"> 3400</span> </div><div class="line"><a name="l03401"></a><span class="lineno"> 3401</span>  <span class="comment">// The model does not specify the output shapes.</span></div><div class="line"><a name="l03402"></a><span class="lineno"> 3402</span>  <span class="comment">// The output shapes are calculated from the max_detection and max_classes_per_detection.</span></div><div class="line"><a name="l03403"></a><span class="lineno"> 3403</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDetectedBox = desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae72089bcab60ac175557f4241b16a014">m_MaxDetections</a> * desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a9ae2c9796692ebeafe19a4d3f09c8ea8">m_MaxClassesPerDetection</a>;</div><div class="line"><a name="l03404"></a><span class="lineno"> 3404</span>  m_OverriddenOutputShapes.push_back({ 1, numDetectedBox, 4 });</div><div class="line"><a name="l03405"></a><span class="lineno"> 3405</span>  m_OverriddenOutputShapes.push_back({ 1, numDetectedBox });</div><div class="line"><a name="l03406"></a><span class="lineno"> 3406</span>  m_OverriddenOutputShapes.push_back({ 1, numDetectedBox });</div><div class="line"><a name="l03407"></a><span class="lineno"> 3407</span>  m_OverriddenOutputShapes.push_back({ 1 });</div><div class="line"><a name="l03408"></a><span class="lineno"> 3408</span> </div><div class="line"><a name="l03409"></a><span class="lineno"> 3409</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0 ; i < outputs.size() ; ++i)</div><div class="line"><a name="l03410"></a><span class="lineno"> 3410</span>  {</div><div class="line"><a name="l03411"></a><span class="lineno"> 3411</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> detectionBoxOutputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(outputs[i], m_OverriddenOutputShapes[i]);</div><div class="line"><a name="l03412"></a><span class="lineno"> 3412</span>  layer-><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>(detectionBoxOutputTensorInfo);</div><div class="line"><a name="l03413"></a><span class="lineno"> 3413</span>  }</div><div class="line"><a name="l03414"></a><span class="lineno"> 3414</span> </div><div class="line"><a name="l03415"></a><span class="lineno"> 3415</span>  <span class="comment">// Register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l03416"></a><span class="lineno"> 3416</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l03417"></a><span class="lineno"> 3417</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l03418"></a><span class="lineno"> 3418</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l03419"></a><span class="lineno"> 3419</span> </div><div class="line"><a name="l03420"></a><span class="lineno"> 3420</span>  <span class="comment">// Register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l03421"></a><span class="lineno"> 3421</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l03422"></a><span class="lineno"> 3422</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0],</div><div class="line"><a name="l03423"></a><span class="lineno"> 3423</span>  outputTensorIndexes[1],</div><div class="line"><a name="l03424"></a><span class="lineno"> 3424</span>  outputTensorIndexes[2],</div><div class="line"><a name="l03425"></a><span class="lineno"> 3425</span>  outputTensorIndexes[3]});</div><div class="line"><a name="l03426"></a><span class="lineno"> 3426</span> }</div><div class="line"><a name="l03427"></a><span class="lineno"> 3427</span> <span class="comment"></span></div><div class="line"><a name="l03428"></a><span class="lineno"> 3428</span> <span class="comment">/// The TfLite Pack operator is equivalent to the ArmNN Stack operator</span></div><div class="line"><a name="l03429"></a><span class="lineno"> 3429</span> <span class="comment"></span><span class="keywordtype">void</span> TfLiteParserImpl::ParsePack(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l03430"></a><span class="lineno"> 3430</span> {</div><div class="line"><a name="l03431"></a><span class="lineno"> 3431</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03432"></a><span class="lineno"> 3432</span> </div><div class="line"><a name="l03433"></a><span class="lineno"> 3433</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03434"></a><span class="lineno"> 3434</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03435"></a><span class="lineno"> 3435</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l03436"></a><span class="lineno"> 3436</span> </div><div class="line"><a name="l03437"></a><span class="lineno"> 3437</span>  <span class="keywordflow">if</span> (inputs.size() < 1)</div><div class="line"><a name="l03438"></a><span class="lineno"> 3438</span>  {</div><div class="line"><a name="l03439"></a><span class="lineno"> 3439</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"Pack must have at least one input."</span>);</div><div class="line"><a name="l03440"></a><span class="lineno"> 3440</span>  }</div><div class="line"><a name="l03441"></a><span class="lineno"> 3441</span> </div><div class="line"><a name="l03442"></a><span class="lineno"> 3442</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l03443"></a><span class="lineno"> 3443</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsPackOptions();</div><div class="line"><a name="l03444"></a><span class="lineno"> 3444</span> </div><div class="line"><a name="l03445"></a><span class="lineno"> 3445</span>  <a class="code" href="structarmnn_1_1_stack_descriptor.xhtml">StackDescriptor</a> desc;</div><div class="line"><a name="l03446"></a><span class="lineno"> 3446</span>  desc.<a class="code" href="structarmnn_1_1_stack_descriptor.xhtml#ab218de7805899c8412d75d1fd1d846d2">m_Axis</a> = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(options->axis);</div><div class="line"><a name="l03447"></a><span class="lineno"> 3447</span>  desc.<a class="code" href="structarmnn_1_1_stack_descriptor.xhtml#aed6086070440ceb94129bef06f70173f">m_NumInputs</a> = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(inputs.size());</div><div class="line"><a name="l03448"></a><span class="lineno"> 3448</span> </div><div class="line"><a name="l03449"></a><span class="lineno"> 3449</span>  <span class="comment">// Use the tensor shape of the first input as the "correct" input shape in the descriptor</span></div><div class="line"><a name="l03450"></a><span class="lineno"> 3450</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l03451"></a><span class="lineno"> 3451</span>  desc.<a class="code" href="structarmnn_1_1_stack_descriptor.xhtml#a2bea87b470268bb0b73457c3733dbc04">m_InputShape</a> = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l03452"></a><span class="lineno"> 3452</span> </div><div class="line"><a name="l03453"></a><span class="lineno"> 3453</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Pack:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03454"></a><span class="lineno"> 3454</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddStackLayer(desc, layerName.c_str());</div><div class="line"><a name="l03455"></a><span class="lineno"> 3455</span> </div><div class="line"><a name="l03456"></a><span class="lineno"> 3456</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l03457"></a><span class="lineno"> 3457</span> </div><div class="line"><a name="l03458"></a><span class="lineno"> 3458</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {});</div><div class="line"><a name="l03459"></a><span class="lineno"> 3459</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l03460"></a><span class="lineno"> 3460</span> </div><div class="line"><a name="l03461"></a><span class="lineno"> 3461</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l03462"></a><span class="lineno"> 3462</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes});</div><div class="line"><a name="l03463"></a><span class="lineno"> 3463</span> </div><div class="line"><a name="l03464"></a><span class="lineno"> 3464</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l03465"></a><span class="lineno"> 3465</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l03466"></a><span class="lineno"> 3466</span> }</div><div class="line"><a name="l03467"></a><span class="lineno"> 3467</span> </div><div class="line"><a name="l03468"></a><span class="lineno"> 3468</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseUnidirectionalSequenceLSTM(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l03469"></a><span class="lineno"> 3469</span> {</div><div class="line"><a name="l03470"></a><span class="lineno"> 3470</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03471"></a><span class="lineno"> 3471</span> </div><div class="line"><a name="l03472"></a><span class="lineno"> 3472</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03473"></a><span class="lineno"> 3473</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03474"></a><span class="lineno"> 3474</span> </div><div class="line"><a name="l03475"></a><span class="lineno"> 3475</span>  <span class="keywordflow">if</span> (inputs.size() < 2)</div><div class="line"><a name="l03476"></a><span class="lineno"> 3476</span>  {</div><div class="line"><a name="l03477"></a><span class="lineno"> 3477</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"UnidirectionalSequenceLSTM must have at least 2 input."</span>);</div><div class="line"><a name="l03478"></a><span class="lineno"> 3478</span>  }</div><div class="line"><a name="l03479"></a><span class="lineno"> 3479</span> </div><div class="line"><a name="l03480"></a><span class="lineno"> 3480</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l03481"></a><span class="lineno"> 3481</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& subgraphPtr = m_Model->subgraphs[subgraphIndex];</div><div class="line"><a name="l03482"></a><span class="lineno"> 3482</span>  <span class="keyword">const</span> <span class="keyword">auto</span> nodeParams = operatorPtr->builtin_options.AsUnidirectionalSequenceLSTMOptions();</div><div class="line"><a name="l03483"></a><span class="lineno"> 3483</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a>(nodeParams, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03484"></a><span class="lineno"> 3484</span>  <span class="keyword">auto</span> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l03485"></a><span class="lineno"> 3485</span>  <span class="keyword">auto</span> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l03486"></a><span class="lineno"> 3486</span> </div><div class="line"><a name="l03487"></a><span class="lineno"> 3487</span>  <span class="comment">// Set the params structure for the AddUnidirectionalSequenceLstmLayer call</span></div><div class="line"><a name="l03488"></a><span class="lineno"> 3488</span>  <span class="comment">// Please refer to each operand at</span></div><div class="line"><a name="l03489"></a><span class="lineno"> 3489</span>  <span class="comment">// https://www.tensorflow.org/mlir/tfl_ops#tflunidirectional_sequence_lstm_tflunidirectionalsequencelstmop</span></div><div class="line"><a name="l03490"></a><span class="lineno"> 3490</span>  <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> params;</div><div class="line"><a name="l03491"></a><span class="lineno"> 3491</span> </div><div class="line"><a name="l03492"></a><span class="lineno"> 3492</span>  <span class="keywordflow">if</span> (IsOptionalOperandPresent(operatorPtr->inputs[1]))</div><div class="line"><a name="l03493"></a><span class="lineno"> 3493</span>  {</div><div class="line"><a name="l03494"></a><span class="lineno"> 3494</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[1]].get(),</div><div class="line"><a name="l03495"></a><span class="lineno"> 3495</span>  inputTensorInfo).first;</div><div class="line"><a name="l03496"></a><span class="lineno"> 3496</span>  }</div><div class="line"><a name="l03497"></a><span class="lineno"> 3497</span> </div><div class="line"><a name="l03498"></a><span class="lineno"> 3498</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[2]].get(),</div><div class="line"><a name="l03499"></a><span class="lineno"> 3499</span>  inputTensorInfo).first;</div><div class="line"><a name="l03500"></a><span class="lineno"> 3500</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[3]].get(),</div><div class="line"><a name="l03501"></a><span class="lineno"> 3501</span>  inputTensorInfo).first;</div><div class="line"><a name="l03502"></a><span class="lineno"> 3502</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[4]].get(),</div><div class="line"><a name="l03503"></a><span class="lineno"> 3503</span>  inputTensorInfo).first;</div><div class="line"><a name="l03504"></a><span class="lineno"> 3504</span> </div><div class="line"><a name="l03505"></a><span class="lineno"> 3505</span>  <span class="comment">// Recurrent weight tensors of size {n_cell, n_output}</span></div><div class="line"><a name="l03506"></a><span class="lineno"> 3506</span>  <span class="keywordflow">if</span> (IsOptionalOperandPresent(operatorPtr->inputs[5]))</div><div class="line"><a name="l03507"></a><span class="lineno"> 3507</span>  {</div><div class="line"><a name="l03508"></a><span class="lineno"> 3508</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[5]].get(),</div><div class="line"><a name="l03509"></a><span class="lineno"> 3509</span>  inputTensorInfo).first;</div><div class="line"><a name="l03510"></a><span class="lineno"> 3510</span>  }</div><div class="line"><a name="l03511"></a><span class="lineno"> 3511</span> </div><div class="line"><a name="l03512"></a><span class="lineno"> 3512</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[6]].get(),</div><div class="line"><a name="l03513"></a><span class="lineno"> 3513</span>  inputTensorInfo).first;</div><div class="line"><a name="l03514"></a><span class="lineno"> 3514</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[7]].get(),</div><div class="line"><a name="l03515"></a><span class="lineno"> 3515</span>  inputTensorInfo).first;</div><div class="line"><a name="l03516"></a><span class="lineno"> 3516</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[8]].get(),</div><div class="line"><a name="l03517"></a><span class="lineno"> 3517</span>  inputTensorInfo).first;</div><div class="line"><a name="l03518"></a><span class="lineno"> 3518</span> </div><div class="line"><a name="l03519"></a><span class="lineno"> 3519</span>  <span class="comment">// Peephole weights tensors of size {n_cell}, representing a diagonal matrix.</span></div><div class="line"><a name="l03520"></a><span class="lineno"> 3520</span>  <span class="keywordflow">if</span> (IsOptionalOperandPresent(operatorPtr->inputs[9]))</div><div class="line"><a name="l03521"></a><span class="lineno"> 3521</span>  {</div><div class="line"><a name="l03522"></a><span class="lineno"> 3522</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[9]].get(),</div><div class="line"><a name="l03523"></a><span class="lineno"> 3523</span>  inputTensorInfo).first;</div><div class="line"><a name="l03524"></a><span class="lineno"> 3524</span>  }</div><div class="line"><a name="l03525"></a><span class="lineno"> 3525</span> </div><div class="line"><a name="l03526"></a><span class="lineno"> 3526</span>  <span class="keywordflow">if</span> (IsOptionalOperandPresent(operatorPtr->inputs[10]))</div><div class="line"><a name="l03527"></a><span class="lineno"> 3527</span>  {</div><div class="line"><a name="l03528"></a><span class="lineno"> 3528</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[10]].get(),</div><div class="line"><a name="l03529"></a><span class="lineno"> 3529</span>  inputTensorInfo).first;</div><div class="line"><a name="l03530"></a><span class="lineno"> 3530</span>  }</div><div class="line"><a name="l03531"></a><span class="lineno"> 3531</span> </div><div class="line"><a name="l03532"></a><span class="lineno"> 3532</span>  <span class="keywordflow">if</span> (IsOptionalOperandPresent(operatorPtr->inputs[11]))</div><div class="line"><a name="l03533"></a><span class="lineno"> 3533</span>  {</div><div class="line"><a name="l03534"></a><span class="lineno"> 3534</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[11]].get(),</div><div class="line"><a name="l03535"></a><span class="lineno"> 3535</span>  inputTensorInfo).first;</div><div class="line"><a name="l03536"></a><span class="lineno"> 3536</span>  }</div><div class="line"><a name="l03537"></a><span class="lineno"> 3537</span> </div><div class="line"><a name="l03538"></a><span class="lineno"> 3538</span>  <span class="comment">// Gates bias tensors of size {n_cell}</span></div><div class="line"><a name="l03539"></a><span class="lineno"> 3539</span>  <span class="keywordflow">if</span> (IsOptionalOperandPresent(operatorPtr->inputs[12]))</div><div class="line"><a name="l03540"></a><span class="lineno"> 3540</span>  {</div><div class="line"><a name="l03541"></a><span class="lineno"> 3541</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[12]].get(),</div><div class="line"><a name="l03542"></a><span class="lineno"> 3542</span>  inputTensorInfo).first;</div><div class="line"><a name="l03543"></a><span class="lineno"> 3543</span>  }</div><div class="line"><a name="l03544"></a><span class="lineno"> 3544</span> </div><div class="line"><a name="l03545"></a><span class="lineno"> 3545</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[13]].get(),</div><div class="line"><a name="l03546"></a><span class="lineno"> 3546</span>  inputTensorInfo).first;</div><div class="line"><a name="l03547"></a><span class="lineno"> 3547</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[14]].get(),</div><div class="line"><a name="l03548"></a><span class="lineno"> 3548</span>  inputTensorInfo).first;</div><div class="line"><a name="l03549"></a><span class="lineno"> 3549</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[15]].get(),</div><div class="line"><a name="l03550"></a><span class="lineno"> 3550</span>  inputTensorInfo).first;</div><div class="line"><a name="l03551"></a><span class="lineno"> 3551</span> </div><div class="line"><a name="l03552"></a><span class="lineno"> 3552</span>  <span class="comment">// Projection weight tensor of size {n_output, n_cell}</span></div><div class="line"><a name="l03553"></a><span class="lineno"> 3553</span>  <span class="keywordflow">if</span> (IsOptionalOperandPresent(operatorPtr->inputs[16]))</div><div class="line"><a name="l03554"></a><span class="lineno"> 3554</span>  {</div><div class="line"><a name="l03555"></a><span class="lineno"> 3555</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[16]].get(),</div><div class="line"><a name="l03556"></a><span class="lineno"> 3556</span>  inputTensorInfo).first;</div><div class="line"><a name="l03557"></a><span class="lineno"> 3557</span>  }</div><div class="line"><a name="l03558"></a><span class="lineno"> 3558</span>  <span class="comment">// Projection bias tensor of size {n_output}</span></div><div class="line"><a name="l03559"></a><span class="lineno"> 3559</span>  <span class="keywordflow">if</span> (IsOptionalOperandPresent(operatorPtr->inputs[17]))</div><div class="line"><a name="l03560"></a><span class="lineno"> 3560</span>  {</div><div class="line"><a name="l03561"></a><span class="lineno"> 3561</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[17]].get(),</div><div class="line"><a name="l03562"></a><span class="lineno"> 3562</span>  inputTensorInfo).first;</div><div class="line"><a name="l03563"></a><span class="lineno"> 3563</span>  }</div><div class="line"><a name="l03564"></a><span class="lineno"> 3564</span> </div><div class="line"><a name="l03565"></a><span class="lineno"> 3565</span>  <span class="comment">// These state tensors are defined as variable tensors, and will be modified by this op.</span></div><div class="line"><a name="l03566"></a><span class="lineno"> 3566</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(subgraphPtr->tensors[operatorPtr->inputs[18]].get());</div><div class="line"><a name="l03567"></a><span class="lineno"> 3567</span>  m_ConstantsToBeCreated.push_back(operatorPtr->inputs[18]);</div><div class="line"><a name="l03568"></a><span class="lineno"> 3568</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(subgraphPtr->tensors[operatorPtr->inputs[19]].get());</div><div class="line"><a name="l03569"></a><span class="lineno"> 3569</span>  m_ConstantsToBeCreated.push_back(operatorPtr->inputs[19]);</div><div class="line"><a name="l03570"></a><span class="lineno"> 3570</span> </div><div class="line"><a name="l03571"></a><span class="lineno"> 3571</span>  <span class="comment">// Layer norm coefficient tensors of size {n_cell}, representing a diagonal matrix.</span></div><div class="line"><a name="l03572"></a><span class="lineno"> 3572</span>  <span class="keywordflow">if</span> (inputs.size() >= 21 && IsOptionalOperandPresent(operatorPtr->inputs[20]))</div><div class="line"><a name="l03573"></a><span class="lineno"> 3573</span>  {</div><div class="line"><a name="l03574"></a><span class="lineno"> 3574</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">m_InputLayerNormWeights</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[20]].get(),</div><div class="line"><a name="l03575"></a><span class="lineno"> 3575</span>  inputTensorInfo).first;</div><div class="line"><a name="l03576"></a><span class="lineno"> 3576</span>  }</div><div class="line"><a name="l03577"></a><span class="lineno"> 3577</span> </div><div class="line"><a name="l03578"></a><span class="lineno"> 3578</span>  <span class="keywordflow">if</span> (inputs.size() >= 22 && IsOptionalOperandPresent(operatorPtr->inputs[21]))</div><div class="line"><a name="l03579"></a><span class="lineno"> 3579</span>  {</div><div class="line"><a name="l03580"></a><span class="lineno"> 3580</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">m_ForgetLayerNormWeights</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[21]].get(),</div><div class="line"><a name="l03581"></a><span class="lineno"> 3581</span>  inputTensorInfo).first;</div><div class="line"><a name="l03582"></a><span class="lineno"> 3582</span>  }</div><div class="line"><a name="l03583"></a><span class="lineno"> 3583</span> </div><div class="line"><a name="l03584"></a><span class="lineno"> 3584</span>  <span class="keywordflow">if</span> (inputs.size() >= 23 && IsOptionalOperandPresent(operatorPtr->inputs[22]))</div><div class="line"><a name="l03585"></a><span class="lineno"> 3585</span>  {</div><div class="line"><a name="l03586"></a><span class="lineno"> 3586</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">m_CellLayerNormWeights</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[22]].get(),</div><div class="line"><a name="l03587"></a><span class="lineno"> 3587</span>  inputTensorInfo).first;</div><div class="line"><a name="l03588"></a><span class="lineno"> 3588</span>  }</div><div class="line"><a name="l03589"></a><span class="lineno"> 3589</span> </div><div class="line"><a name="l03590"></a><span class="lineno"> 3590</span>  <span class="keywordflow">if</span> (inputs.size() >= 24 && IsOptionalOperandPresent(operatorPtr->inputs[23]))</div><div class="line"><a name="l03591"></a><span class="lineno"> 3591</span>  {</div><div class="line"><a name="l03592"></a><span class="lineno"> 3592</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">m_OutputLayerNormWeights</a> = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[23]].get(),</div><div class="line"><a name="l03593"></a><span class="lineno"> 3593</span>  inputTensorInfo).first;</div><div class="line"><a name="l03594"></a><span class="lineno"> 3594</span>  }</div><div class="line"><a name="l03595"></a><span class="lineno"> 3595</span> </div><div class="line"><a name="l03596"></a><span class="lineno"> 3596</span>  <span class="comment">// set the layer descriptor</span></div><div class="line"><a name="l03597"></a><span class="lineno"> 3597</span>  <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::UnidirectionalSequenceLstmDescriptor</a> desc;</div><div class="line"><a name="l03598"></a><span class="lineno"> 3598</span>  desc.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> = nodeParams->fused_activation_function;</div><div class="line"><a name="l03599"></a><span class="lineno"> 3599</span>  desc.m_ClippingThresCell = nodeParams->cell_clip;</div><div class="line"><a name="l03600"></a><span class="lineno"> 3600</span>  desc.m_ClippingThresProj = nodeParams->proj_clip;</div><div class="line"><a name="l03601"></a><span class="lineno"> 3601</span>  desc.m_CifgEnabled = (params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a> == <span class="keyword">nullptr</span></div><div class="line"><a name="l03602"></a><span class="lineno"> 3602</span>  || params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a> == <span class="keyword">nullptr</span></div><div class="line"><a name="l03603"></a><span class="lineno"> 3603</span>  || params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l03604"></a><span class="lineno"> 3604</span>  desc.m_PeepholeEnabled = (params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a> != <span class="keyword">nullptr</span> || params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a> != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l03605"></a><span class="lineno"> 3605</span>  desc.m_ProjectionEnabled = (params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a> != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l03606"></a><span class="lineno"> 3606</span>  desc.m_LayerNormEnabled = (params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">m_InputLayerNormWeights</a> != <span class="keyword">nullptr</span></div><div class="line"><a name="l03607"></a><span class="lineno"> 3607</span>  || params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">m_ForgetLayerNormWeights</a> != <span class="keyword">nullptr</span></div><div class="line"><a name="l03608"></a><span class="lineno"> 3608</span>  || params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">m_CellLayerNormWeights</a> != <span class="keyword">nullptr</span></div><div class="line"><a name="l03609"></a><span class="lineno"> 3609</span>  || params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">m_OutputLayerNormWeights</a> != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l03610"></a><span class="lineno"> 3610</span>  desc.m_TimeMajor = nodeParams->time_major;</div><div class="line"><a name="l03611"></a><span class="lineno"> 3611</span> </div><div class="line"><a name="l03612"></a><span class="lineno"> 3612</span>  <span class="keywordflow">if</span> (operatorPtr->intermediates.size() > 3 && desc.m_LayerNormEnabled)</div><div class="line"><a name="l03613"></a><span class="lineno"> 3613</span>  {</div><div class="line"><a name="l03614"></a><span class="lineno"> 3614</span>  <span class="keyword">auto</span> inputIntermediate = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->intermediates[0]].get(),</div><div class="line"><a name="l03615"></a><span class="lineno"> 3615</span>  inputTensorInfo).first;</div><div class="line"><a name="l03616"></a><span class="lineno"> 3616</span>  <span class="keyword">auto</span> inputIntermediateTensorInfo = inputIntermediate->GetInfo();</div><div class="line"><a name="l03617"></a><span class="lineno"> 3617</span>  desc.m_InputIntermediateScale = inputIntermediateTensorInfo.GetQuantizationScale();</div><div class="line"><a name="l03618"></a><span class="lineno"> 3618</span> </div><div class="line"><a name="l03619"></a><span class="lineno"> 3619</span>  <span class="keyword">auto</span> forgetIntermediate = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->intermediates[1]].get(),</div><div class="line"><a name="l03620"></a><span class="lineno"> 3620</span>  inputTensorInfo).first;</div><div class="line"><a name="l03621"></a><span class="lineno"> 3621</span>  <span class="keyword">auto</span> forgetIntermediateTensorInfo = forgetIntermediate->GetInfo();</div><div class="line"><a name="l03622"></a><span class="lineno"> 3622</span>  desc.m_ForgetIntermediateScale = forgetIntermediateTensorInfo.GetQuantizationScale();</div><div class="line"><a name="l03623"></a><span class="lineno"> 3623</span> </div><div class="line"><a name="l03624"></a><span class="lineno"> 3624</span>  <span class="keyword">auto</span> cellIntermediate = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->intermediates[2]].get(),</div><div class="line"><a name="l03625"></a><span class="lineno"> 3625</span>  inputTensorInfo).first;</div><div class="line"><a name="l03626"></a><span class="lineno"> 3626</span>  <span class="keyword">auto</span> cellIntermediateTensorInfo = cellIntermediate->GetInfo();</div><div class="line"><a name="l03627"></a><span class="lineno"> 3627</span>  desc.m_CellIntermediateScale = cellIntermediateTensorInfo.GetQuantizationScale();</div><div class="line"><a name="l03628"></a><span class="lineno"> 3628</span> </div><div class="line"><a name="l03629"></a><span class="lineno"> 3629</span>  <span class="keyword">auto</span> outputIntermediate = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->intermediates[3]].get(),</div><div class="line"><a name="l03630"></a><span class="lineno"> 3630</span>  inputTensorInfo).first;</div><div class="line"><a name="l03631"></a><span class="lineno"> 3631</span>  <span class="keyword">auto</span> outputIntermediateTensorInfo = outputIntermediate->GetInfo();</div><div class="line"><a name="l03632"></a><span class="lineno"> 3632</span>  desc.m_OutputIntermediateScale = outputIntermediateTensorInfo.GetQuantizationScale();</div><div class="line"><a name="l03633"></a><span class="lineno"> 3633</span>  }</div><div class="line"><a name="l03634"></a><span class="lineno"> 3634</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l03635"></a><span class="lineno"> 3635</span>  {</div><div class="line"><a name="l03636"></a><span class="lineno"> 3636</span>  <span class="keywordtype">float</span> defaultIntermediate = std::pow(2, -12);</div><div class="line"><a name="l03637"></a><span class="lineno"> 3637</span>  desc.m_InputIntermediateScale = defaultIntermediate;</div><div class="line"><a name="l03638"></a><span class="lineno"> 3638</span>  desc.m_ForgetIntermediateScale = defaultIntermediate;</div><div class="line"><a name="l03639"></a><span class="lineno"> 3639</span>  desc.m_CellIntermediateScale = defaultIntermediate;</div><div class="line"><a name="l03640"></a><span class="lineno"> 3640</span>  desc.m_OutputIntermediateScale = defaultIntermediate;</div><div class="line"><a name="l03641"></a><span class="lineno"> 3641</span>  }</div><div class="line"><a name="l03642"></a><span class="lineno"> 3642</span> </div><div class="line"><a name="l03643"></a><span class="lineno"> 3643</span>  <span class="keywordflow">if</span> (operatorPtr->intermediates.size() > 4)</div><div class="line"><a name="l03644"></a><span class="lineno"> 3644</span>  {</div><div class="line"><a name="l03645"></a><span class="lineno"> 3645</span>  <span class="keyword">auto</span> hiddentensor = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->intermediates[4]].get(),</div><div class="line"><a name="l03646"></a><span class="lineno"> 3646</span>  inputTensorInfo).first;</div><div class="line"><a name="l03647"></a><span class="lineno"> 3647</span> </div><div class="line"><a name="l03648"></a><span class="lineno"> 3648</span>  desc.m_HiddenStateScale = hiddentensor->GetInfo().GetQuantizationScale();</div><div class="line"><a name="l03649"></a><span class="lineno"> 3649</span>  desc.m_HiddenStateZeroPoint = hiddentensor->GetInfo().GetQuantizationOffset();</div><div class="line"><a name="l03650"></a><span class="lineno"> 3650</span>  }</div><div class="line"><a name="l03651"></a><span class="lineno"> 3651</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = inputTensorInfo.GetShape()[0];</div><div class="line"><a name="l03652"></a><span class="lineno"> 3652</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = outputTensorInfo.GetShape()[2];</div><div class="line"><a name="l03653"></a><span class="lineno"> 3653</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numUnits = cellStateInInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l03654"></a><span class="lineno"> 3654</span> </div><div class="line"><a name="l03655"></a><span class="lineno"> 3655</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> dataType = inputTensorInfo.GetDataType();</div><div class="line"><a name="l03656"></a><span class="lineno"> 3656</span>  <span class="keywordtype">float</span> qScale = inputTensorInfo.GetQuantizationScale();</div><div class="line"><a name="l03657"></a><span class="lineno"> 3657</span>  <span class="keywordtype">float</span> qOffset = inputTensorInfo.GetQuantizationOffset();</div><div class="line"><a name="l03658"></a><span class="lineno"> 3658</span> </div><div class="line"><a name="l03659"></a><span class="lineno"> 3659</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> scratchBufferTensorInfo({batchSize, numUnits * 3}, dataType, qScale, qOffset);</div><div class="line"><a name="l03660"></a><span class="lineno"> 3660</span>  <span class="keywordflow">if</span> (!desc.m_CifgEnabled)</div><div class="line"><a name="l03661"></a><span class="lineno"> 3661</span>  {</div><div class="line"><a name="l03662"></a><span class="lineno"> 3662</span>  scratchBufferTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({batchSize, numUnits * 4}, dataType, qScale, qOffset);</div><div class="line"><a name="l03663"></a><span class="lineno"> 3663</span>  }</div><div class="line"><a name="l03664"></a><span class="lineno"> 3664</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateOutTensorInfo({batchSize, numUnits},</div><div class="line"><a name="l03665"></a><span class="lineno"> 3665</span>  cellStateInInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>(),</div><div class="line"><a name="l03666"></a><span class="lineno"> 3666</span>  cellStateInInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l03667"></a><span class="lineno"> 3667</span>  cellStateInInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>());</div><div class="line"><a name="l03668"></a><span class="lineno"> 3668</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateOutTensorInfo({batchSize, outputSize}, dataType, qScale, qOffset);</div><div class="line"><a name="l03669"></a><span class="lineno"> 3669</span> </div><div class="line"><a name="l03670"></a><span class="lineno"> 3670</span>  <a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml">armnn::LstmInputParamsInfo</a> paramsInfo;</div><div class="line"><a name="l03671"></a><span class="lineno"> 3671</span>  paramsInfo.<a class="code" href="structarmnn_1_1_lstm_input_params_info.xhtml#a4b3f6e6f2268416ffd7a34fda95ffd0b">m_InputToForgetWeights</a> = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03672"></a><span class="lineno"> 3672</span>  paramsInfo.m_InputToCellWeights = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03673"></a><span class="lineno"> 3673</span>  paramsInfo.m_InputToOutputWeights = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03674"></a><span class="lineno"> 3674</span>  paramsInfo.m_RecurrentToForgetWeights = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03675"></a><span class="lineno"> 3675</span>  paramsInfo.m_RecurrentToCellWeights = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03676"></a><span class="lineno"> 3676</span>  paramsInfo.m_RecurrentToOutputWeights = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03677"></a><span class="lineno"> 3677</span>  paramsInfo.m_ForgetGateBias = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03678"></a><span class="lineno"> 3678</span>  paramsInfo.m_CellBias = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03679"></a><span class="lineno"> 3679</span>  paramsInfo.m_OutputGateBias = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03680"></a><span class="lineno"> 3680</span> </div><div class="line"><a name="l03681"></a><span class="lineno"> 3681</span>  <span class="keywordflow">if</span> (!desc.m_CifgEnabled)</div><div class="line"><a name="l03682"></a><span class="lineno"> 3682</span>  {</div><div class="line"><a name="l03683"></a><span class="lineno"> 3683</span>  paramsInfo.m_InputToInputWeights = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03684"></a><span class="lineno"> 3684</span>  paramsInfo.m_RecurrentToInputWeights = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03685"></a><span class="lineno"> 3685</span>  <span class="keywordflow">if</span> (params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a> != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l03686"></a><span class="lineno"> 3686</span>  {</div><div class="line"><a name="l03687"></a><span class="lineno"> 3687</span>  paramsInfo.m_CellToInputWeights = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03688"></a><span class="lineno"> 3688</span>  }</div><div class="line"><a name="l03689"></a><span class="lineno"> 3689</span>  paramsInfo.m_InputGateBias = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03690"></a><span class="lineno"> 3690</span>  }</div><div class="line"><a name="l03691"></a><span class="lineno"> 3691</span> </div><div class="line"><a name="l03692"></a><span class="lineno"> 3692</span>  <span class="keywordflow">if</span> (desc.m_ProjectionEnabled)</div><div class="line"><a name="l03693"></a><span class="lineno"> 3693</span>  {</div><div class="line"><a name="l03694"></a><span class="lineno"> 3694</span>  paramsInfo.m_ProjectionWeights = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03695"></a><span class="lineno"> 3695</span>  <span class="keywordflow">if</span> (params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a> != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l03696"></a><span class="lineno"> 3696</span>  {</div><div class="line"><a name="l03697"></a><span class="lineno"> 3697</span>  paramsInfo.m_ProjectionBias = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03698"></a><span class="lineno"> 3698</span>  }</div><div class="line"><a name="l03699"></a><span class="lineno"> 3699</span>  }</div><div class="line"><a name="l03700"></a><span class="lineno"> 3700</span> </div><div class="line"><a name="l03701"></a><span class="lineno"> 3701</span>  <span class="keywordflow">if</span> (desc.m_PeepholeEnabled)</div><div class="line"><a name="l03702"></a><span class="lineno"> 3702</span>  {</div><div class="line"><a name="l03703"></a><span class="lineno"> 3703</span>  paramsInfo.m_CellToForgetWeights = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03704"></a><span class="lineno"> 3704</span>  paramsInfo.m_CellToOutputWeights = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03705"></a><span class="lineno"> 3705</span>  }</div><div class="line"><a name="l03706"></a><span class="lineno"> 3706</span> </div><div class="line"><a name="l03707"></a><span class="lineno"> 3707</span>  <span class="keywordflow">if</span> (desc.m_LayerNormEnabled)</div><div class="line"><a name="l03708"></a><span class="lineno"> 3708</span>  {</div><div class="line"><a name="l03709"></a><span class="lineno"> 3709</span>  <span class="keywordflow">if</span>(!desc.m_CifgEnabled)</div><div class="line"><a name="l03710"></a><span class="lineno"> 3710</span>  {</div><div class="line"><a name="l03711"></a><span class="lineno"> 3711</span>  paramsInfo.m_InputLayerNormWeights = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">m_InputLayerNormWeights</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03712"></a><span class="lineno"> 3712</span>  }</div><div class="line"><a name="l03713"></a><span class="lineno"> 3713</span>  paramsInfo.m_ForgetLayerNormWeights = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">m_ForgetLayerNormWeights</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03714"></a><span class="lineno"> 3714</span>  paramsInfo.m_CellLayerNormWeights = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">m_CellLayerNormWeights</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03715"></a><span class="lineno"> 3715</span>  paramsInfo.m_OutputLayerNormWeights = &(params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">m_OutputLayerNormWeights</a>-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div><div class="line"><a name="l03716"></a><span class="lineno"> 3716</span>  }</div><div class="line"><a name="l03717"></a><span class="lineno"> 3717</span> </div><div class="line"><a name="l03718"></a><span class="lineno"> 3718</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"UnidirectionalSequenceLSTM:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03719"></a><span class="lineno"> 3719</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer = m_Network->AddUnidirectionalSequenceLstmLayer(desc, params);</div><div class="line"><a name="l03720"></a><span class="lineno"> 3720</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l03721"></a><span class="lineno"> 3721</span> </div><div class="line"><a name="l03722"></a><span class="lineno"> 3722</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l03723"></a><span class="lineno"> 3723</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l03724"></a><span class="lineno"> 3724</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector({operatorPtr->inputs[0],</div><div class="line"><a name="l03725"></a><span class="lineno"> 3725</span>  operatorPtr->inputs[18],</div><div class="line"><a name="l03726"></a><span class="lineno"> 3726</span>  operatorPtr->inputs[19]});</div><div class="line"><a name="l03727"></a><span class="lineno"> 3727</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0],</div><div class="line"><a name="l03728"></a><span class="lineno"> 3728</span>  inputTensorIndexes[1],</div><div class="line"><a name="l03729"></a><span class="lineno"> 3729</span>  inputTensorIndexes[2]});</div><div class="line"><a name="l03730"></a><span class="lineno"> 3730</span> </div><div class="line"><a name="l03731"></a><span class="lineno"> 3731</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l03732"></a><span class="lineno"> 3732</span> </div><div class="line"><a name="l03733"></a><span class="lineno"> 3733</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateOutTensorInfo);</div><div class="line"><a name="l03734"></a><span class="lineno"> 3734</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateOutTensorInfo);</div><div class="line"><a name="l03735"></a><span class="lineno"> 3735</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l03736"></a><span class="lineno"> 3736</span> </div><div class="line"><a name="l03737"></a><span class="lineno"> 3737</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tensorIndex = outputTensorIndexes[0];</div><div class="line"><a name="l03738"></a><span class="lineno"> 3738</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>* slot = &(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2));</div><div class="line"><a name="l03739"></a><span class="lineno"> 3739</span>  RegisterProducerOfTensor(subgraphIndex, tensorIndex, slot);</div><div class="line"><a name="l03740"></a><span class="lineno"> 3740</span> }</div><div class="line"><a name="l03741"></a><span class="lineno"> 3741</span> </div><div class="line"><a name="l03742"></a><span class="lineno"> 3742</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseUnpack(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l03743"></a><span class="lineno"> 3743</span> {</div><div class="line"><a name="l03744"></a><span class="lineno"> 3744</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03745"></a><span class="lineno"> 3745</span> </div><div class="line"><a name="l03746"></a><span class="lineno"> 3746</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l03747"></a><span class="lineno"> 3747</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsUnpackOptions();</div><div class="line"><a name="l03748"></a><span class="lineno"> 3748</span> </div><div class="line"><a name="l03749"></a><span class="lineno"> 3749</span>  <span class="comment">// This unpackAxis indicates the axis to unpack</span></div><div class="line"><a name="l03750"></a><span class="lineno"> 3750</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> unpackAxis = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->axis);</div><div class="line"><a name="l03751"></a><span class="lineno"> 3751</span> </div><div class="line"><a name="l03752"></a><span class="lineno"> 3752</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03753"></a><span class="lineno"> 3753</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l03754"></a><span class="lineno"> 3754</span> </div><div class="line"><a name="l03755"></a><span class="lineno"> 3755</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l03756"></a><span class="lineno"> 3756</span> </div><div class="line"><a name="l03757"></a><span class="lineno"> 3757</span>  <span class="keywordflow">if</span> (unpackAxis >= inputTensorInfo.GetNumDimensions())</div><div class="line"><a name="l03758"></a><span class="lineno"> 3758</span>  {</div><div class="line"><a name="l03759"></a><span class="lineno"> 3759</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03760"></a><span class="lineno"> 3760</span>  fmt::format(<span class="stringliteral">"The unpack axis: {} cannot be greater than or equal to "</span></div><div class="line"><a name="l03761"></a><span class="lineno"> 3761</span>  <span class="stringliteral">"the number of input dimension {} {}"</span>,</div><div class="line"><a name="l03762"></a><span class="lineno"> 3762</span>  unpackAxis,</div><div class="line"><a name="l03763"></a><span class="lineno"> 3763</span>  inputTensorInfo.GetNumDimensions(),</div><div class="line"><a name="l03764"></a><span class="lineno"> 3764</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03765"></a><span class="lineno"> 3765</span>  }</div><div class="line"><a name="l03766"></a><span class="lineno"> 3766</span> </div><div class="line"><a name="l03767"></a><span class="lineno"> 3767</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> unpackNum = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->num);</div><div class="line"><a name="l03768"></a><span class="lineno"> 3768</span>  <span class="comment">// If num is not defined, automatically infer from the length of the dimension axis.</span></div><div class="line"><a name="l03769"></a><span class="lineno"> 3769</span>  <span class="keywordflow">if</span>(unpackNum == 0)</div><div class="line"><a name="l03770"></a><span class="lineno"> 3770</span>  {</div><div class="line"><a name="l03771"></a><span class="lineno"> 3771</span>  unpackNum = inputTensorInfo.GetShape()[unpackAxis];</div><div class="line"><a name="l03772"></a><span class="lineno"> 3772</span>  }</div><div class="line"><a name="l03773"></a><span class="lineno"> 3773</span> </div><div class="line"><a name="l03774"></a><span class="lineno"> 3774</span>  <span class="comment">// If unpack number cannot be inferred and is still zero, throw ParseException.</span></div><div class="line"><a name="l03775"></a><span class="lineno"> 3775</span>  <span class="keywordflow">if</span>(unpackNum == 0)</div><div class="line"><a name="l03776"></a><span class="lineno"> 3776</span>  {</div><div class="line"><a name="l03777"></a><span class="lineno"> 3777</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"Number to unpack must greater than zero."</span>);</div><div class="line"><a name="l03778"></a><span class="lineno"> 3778</span>  }</div><div class="line"><a name="l03779"></a><span class="lineno"> 3779</span> </div><div class="line"><a name="l03780"></a><span class="lineno"> 3780</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03781"></a><span class="lineno"> 3781</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), unpackNum);</div><div class="line"><a name="l03782"></a><span class="lineno"> 3782</span> </div><div class="line"><a name="l03783"></a><span class="lineno"> 3783</span>  <span class="keyword">auto</span> inputDimSize = inputTensorInfo.GetNumDimensions();</div><div class="line"><a name="l03784"></a><span class="lineno"> 3784</span>  std::vector<unsigned int> unpackDimSizes(inputDimSize);</div><div class="line"><a name="l03785"></a><span class="lineno"> 3785</span> </div><div class="line"><a name="l03786"></a><span class="lineno"> 3786</span>  <span class="comment">// Add current input shape to unpackDimSizes</span></div><div class="line"><a name="l03787"></a><span class="lineno"> 3787</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputDimSize; ++i)</div><div class="line"><a name="l03788"></a><span class="lineno"> 3788</span>  {</div><div class="line"><a name="l03789"></a><span class="lineno"> 3789</span>  unpackDimSizes[i] = inputTensorInfo.GetShape()[i];</div><div class="line"><a name="l03790"></a><span class="lineno"> 3790</span>  }</div><div class="line"><a name="l03791"></a><span class="lineno"> 3791</span> </div><div class="line"><a name="l03792"></a><span class="lineno"> 3792</span>  <span class="keywordflow">if</span> (unpackDimSizes[unpackAxis] != unpackNum)</div><div class="line"><a name="l03793"></a><span class="lineno"> 3793</span>  {</div><div class="line"><a name="l03794"></a><span class="lineno"> 3794</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"Number to unpack must be the same as length of the dimension to "</span></div><div class="line"><a name="l03795"></a><span class="lineno"> 3795</span>  <span class="stringliteral">"unpack along."</span>);</div><div class="line"><a name="l03796"></a><span class="lineno"> 3796</span>  }</div><div class="line"><a name="l03797"></a><span class="lineno"> 3797</span> </div><div class="line"><a name="l03798"></a><span class="lineno"> 3798</span>  unpackDimSizes[unpackAxis] /= unpackNum;</div><div class="line"><a name="l03799"></a><span class="lineno"> 3799</span> </div><div class="line"><a name="l03800"></a><span class="lineno"> 3800</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">SplitterDescriptor</a> splitDesc(unpackNum, static_cast<unsigned int>(unpackDimSizes.size()));</div><div class="line"><a name="l03801"></a><span class="lineno"> 3801</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> j = 0; j < unpackNum; ++j)</div><div class="line"><a name="l03802"></a><span class="lineno"> 3802</span>  {</div><div class="line"><a name="l03803"></a><span class="lineno"> 3803</span>  <span class="comment">// Set the size of the views.</span></div><div class="line"><a name="l03804"></a><span class="lineno"> 3804</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx = 0; dimIdx < unpackDimSizes.size(); ++dimIdx)</div><div class="line"><a name="l03805"></a><span class="lineno"> 3805</span>  {</div><div class="line"><a name="l03806"></a><span class="lineno"> 3806</span>  splitDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(j, dimIdx, unpackDimSizes[dimIdx]);</div><div class="line"><a name="l03807"></a><span class="lineno"> 3807</span>  }</div><div class="line"><a name="l03808"></a><span class="lineno"> 3808</span>  splitDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(j, unpackAxis, unpackDimSizes[unpackAxis] * j);</div><div class="line"><a name="l03809"></a><span class="lineno"> 3809</span>  }</div><div class="line"><a name="l03810"></a><span class="lineno"> 3810</span> </div><div class="line"><a name="l03811"></a><span class="lineno"> 3811</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Unpack:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03812"></a><span class="lineno"> 3812</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddSplitterLayer(splitDesc, layerName.c_str());</div><div class="line"><a name="l03813"></a><span class="lineno"> 3813</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l03814"></a><span class="lineno"> 3814</span> </div><div class="line"><a name="l03815"></a><span class="lineno"> 3815</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> splitOutShape = <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(static_cast<unsigned int>(unpackDimSizes.size()),</div><div class="line"><a name="l03816"></a><span class="lineno"> 3816</span>  unpackDimSizes.data());</div><div class="line"><a name="l03817"></a><span class="lineno"> 3817</span> </div><div class="line"><a name="l03818"></a><span class="lineno"> 3818</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l03819"></a><span class="lineno"> 3819</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l03820"></a><span class="lineno"> 3820</span> </div><div class="line"><a name="l03821"></a><span class="lineno"> 3821</span>  std::vector<unsigned int> reshapeDims;</div><div class="line"><a name="l03822"></a><span class="lineno"> 3822</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> axis = 0; axis < splitOutShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++axis)</div><div class="line"><a name="l03823"></a><span class="lineno"> 3823</span>  {</div><div class="line"><a name="l03824"></a><span class="lineno"> 3824</span>  <span class="keywordflow">if</span> (axis != unpackAxis)</div><div class="line"><a name="l03825"></a><span class="lineno"> 3825</span>  {</div><div class="line"><a name="l03826"></a><span class="lineno"> 3826</span>  reshapeDims.push_back(splitOutShape[axis]);</div><div class="line"><a name="l03827"></a><span class="lineno"> 3827</span>  }</div><div class="line"><a name="l03828"></a><span class="lineno"> 3828</span>  }</div><div class="line"><a name="l03829"></a><span class="lineno"> 3829</span> </div><div class="line"><a name="l03830"></a><span class="lineno"> 3830</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> reshapeOutputShape(splitOutShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() -1, reshapeDims.data());</div><div class="line"><a name="l03831"></a><span class="lineno"> 3831</span> </div><div class="line"><a name="l03832"></a><span class="lineno"> 3832</span>  <span class="comment">// Create reshape to remove the unpacked dimension for unpack operator of each output from Splitter.</span></div><div class="line"><a name="l03833"></a><span class="lineno"> 3833</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> k = 0; k < layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(); ++k)</div><div class="line"><a name="l03834"></a><span class="lineno"> 3834</span>  {</div><div class="line"><a name="l03835"></a><span class="lineno"> 3835</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(outputs[k], <span class="keyword">true</span>);</div><div class="line"><a name="l03836"></a><span class="lineno"> 3836</span>  std::string reshapeLayerName = fmt::format(<span class="stringliteral">"Reshape_for:{}"</span>, layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>());</div><div class="line"><a name="l03837"></a><span class="lineno"> 3837</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">armnn::ReshapeDescriptor</a> desc;</div><div class="line"><a name="l03838"></a><span class="lineno"> 3838</span>  desc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = reshapeOutputShape;</div><div class="line"><a name="l03839"></a><span class="lineno"> 3839</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* reshapeLayer = m_Network->AddReshapeLayer(desc, layerName.c_str());</div><div class="line"><a name="l03840"></a><span class="lineno"> 3840</span> </div><div class="line"><a name="l03841"></a><span class="lineno"> 3841</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(k).<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>(splitOutShape,</div><div class="line"><a name="l03842"></a><span class="lineno"> 3842</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>(),</div><div class="line"><a name="l03843"></a><span class="lineno"> 3843</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l03844"></a><span class="lineno"> 3844</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>()));</div><div class="line"><a name="l03845"></a><span class="lineno"> 3845</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(k).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(reshapeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l03846"></a><span class="lineno"> 3846</span> </div><div class="line"><a name="l03847"></a><span class="lineno"> 3847</span>  reshapeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l03848"></a><span class="lineno"> 3848</span> </div><div class="line"><a name="l03849"></a><span class="lineno"> 3849</span>  uint32_t reshapedOutputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(operatorPtr->outputs[k]);</div><div class="line"><a name="l03850"></a><span class="lineno"> 3850</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>* slot = &(reshapeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l03851"></a><span class="lineno"> 3851</span>  RegisterProducerOfTensor(subgraphIndex, reshapedOutputId, slot);</div><div class="line"><a name="l03852"></a><span class="lineno"> 3852</span>  }</div><div class="line"><a name="l03853"></a><span class="lineno"> 3853</span> }</div><div class="line"><a name="l03854"></a><span class="lineno"> 3854</span> </div><div class="line"><a name="l03855"></a><span class="lineno"> 3855</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseSplit(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l03856"></a><span class="lineno"> 3856</span> {</div><div class="line"><a name="l03857"></a><span class="lineno"> 3857</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03858"></a><span class="lineno"> 3858</span> </div><div class="line"><a name="l03859"></a><span class="lineno"> 3859</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l03860"></a><span class="lineno"> 3860</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsSplitOptions();</div><div class="line"><a name="l03861"></a><span class="lineno"> 3861</span> </div><div class="line"><a name="l03862"></a><span class="lineno"> 3862</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numSplits = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->num_splits);</div><div class="line"><a name="l03863"></a><span class="lineno"> 3863</span> </div><div class="line"><a name="l03864"></a><span class="lineno"> 3864</span>  <span class="comment">// If number of splits cannot be inferred and is zero, throw ParseException.</span></div><div class="line"><a name="l03865"></a><span class="lineno"> 3865</span>  <span class="keywordflow">if</span>(numSplits == 0)</div><div class="line"><a name="l03866"></a><span class="lineno"> 3866</span>  {</div><div class="line"><a name="l03867"></a><span class="lineno"> 3867</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"Number to splits must greater than zero."</span>);</div><div class="line"><a name="l03868"></a><span class="lineno"> 3868</span>  }</div><div class="line"><a name="l03869"></a><span class="lineno"> 3869</span> </div><div class="line"><a name="l03870"></a><span class="lineno"> 3870</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03871"></a><span class="lineno"> 3871</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l03872"></a><span class="lineno"> 3872</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03873"></a><span class="lineno"> 3873</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), numSplits);</div><div class="line"><a name="l03874"></a><span class="lineno"> 3874</span> </div><div class="line"><a name="l03875"></a><span class="lineno"> 3875</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l03876"></a><span class="lineno"> 3876</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> axisTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l03877"></a><span class="lineno"> 3877</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(axisTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() == 1);</div><div class="line"><a name="l03878"></a><span class="lineno"> 3878</span> </div><div class="line"><a name="l03879"></a><span class="lineno"> 3879</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> axisBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[0]->buffer);</div><div class="line"><a name="l03880"></a><span class="lineno"> 3880</span>  <span class="keywordflow">if</span> (axisBufferPtr == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l03881"></a><span class="lineno"> 3881</span>  {</div><div class="line"><a name="l03882"></a><span class="lineno"> 3882</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03883"></a><span class="lineno"> 3883</span>  fmt::format(<span class="stringliteral">"Operation has invalid inputs. Failed to read axis. {}"</span>,</div><div class="line"><a name="l03884"></a><span class="lineno"> 3884</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03885"></a><span class="lineno"> 3885</span>  }</div><div class="line"><a name="l03886"></a><span class="lineno"> 3886</span> </div><div class="line"><a name="l03887"></a><span class="lineno"> 3887</span>  std::vector<int32_t> axisData(axisTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l03888"></a><span class="lineno"> 3888</span>  ::memcpy(axisData.data(), axisBufferPtr->data.data(), axisTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l03889"></a><span class="lineno"> 3889</span>  int32_t axis = axisData[0];</div><div class="line"><a name="l03890"></a><span class="lineno"> 3890</span> </div><div class="line"><a name="l03891"></a><span class="lineno"> 3891</span>  <span class="keyword">auto</span> inputDimensions = <span class="keyword">static_cast<</span>int32_t<span class="keyword">></span>(inputTensorInfo.GetNumDimensions());</div><div class="line"><a name="l03892"></a><span class="lineno"> 3892</span>  <span class="keywordflow">if</span> (((axis < -inputDimensions) && (axis < 0)) || ((axis >= inputDimensions) && (axis > 0)))</div><div class="line"><a name="l03893"></a><span class="lineno"> 3893</span>  {</div><div class="line"><a name="l03894"></a><span class="lineno"> 3894</span>  <span class="comment">// Square bracket denotes inclusive n while parenthesis denotes exclusive n</span></div><div class="line"><a name="l03895"></a><span class="lineno"> 3895</span>  <span class="comment">// E.g. Rank 4 tensor can have axis in range [-4, 3)</span></div><div class="line"><a name="l03896"></a><span class="lineno"> 3896</span>  <span class="comment">// -1 == 3, -2 == 2, -3 == 1, -4 == 0</span></div><div class="line"><a name="l03897"></a><span class="lineno"> 3897</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03898"></a><span class="lineno"> 3898</span>  fmt::format(<span class="stringliteral">"Operation has invalid axis: {}. Axis must be in range [-n, n) {}"</span>,</div><div class="line"><a name="l03899"></a><span class="lineno"> 3899</span>  axis,</div><div class="line"><a name="l03900"></a><span class="lineno"> 3900</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03901"></a><span class="lineno"> 3901</span>  }</div><div class="line"><a name="l03902"></a><span class="lineno"> 3902</span> </div><div class="line"><a name="l03903"></a><span class="lineno"> 3903</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitDim = <a class="code" href="namespacearmnn_utils.xhtml#ac93cb1365b4bcb67df2a3164606096c5">armnnUtils::GetUnsignedAxis</a>(inputTensorInfo.GetNumDimensions(), axis);</div><div class="line"><a name="l03904"></a><span class="lineno"> 3904</span> </div><div class="line"><a name="l03905"></a><span class="lineno"> 3905</span>  <span class="keyword">auto</span> inputDimSize = inputTensorInfo.GetNumDimensions();</div><div class="line"><a name="l03906"></a><span class="lineno"> 3906</span>  <span class="keywordflow">if</span> (inputDimSize > <a class="code" href="namespacearmnn.xhtml#abdcd184ed3bd648bb31d385040cafd5d">MaxNumOfTensorDimensions</a>)</div><div class="line"><a name="l03907"></a><span class="lineno"> 3907</span>  {</div><div class="line"><a name="l03908"></a><span class="lineno"> 3908</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03909"></a><span class="lineno"> 3909</span>  fmt::format(<span class="stringliteral">"The number of dimensions: {} for input tensors of the split op cannot be greater than {} {}"</span>,</div><div class="line"><a name="l03910"></a><span class="lineno"> 3910</span>  inputTensorInfo.GetNumDimensions(),</div><div class="line"><a name="l03911"></a><span class="lineno"> 3911</span>  <a class="code" href="namespacearmnn.xhtml#abdcd184ed3bd648bb31d385040cafd5d">MaxNumOfTensorDimensions</a>,</div><div class="line"><a name="l03912"></a><span class="lineno"> 3912</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03913"></a><span class="lineno"> 3913</span>  }</div><div class="line"><a name="l03914"></a><span class="lineno"> 3914</span> </div><div class="line"><a name="l03915"></a><span class="lineno"> 3915</span>  std::vector<unsigned int> splitterDimSizes(inputDimSize);</div><div class="line"><a name="l03916"></a><span class="lineno"> 3916</span> </div><div class="line"><a name="l03917"></a><span class="lineno"> 3917</span>  <span class="comment">// Add current input shape to splitterDimSizes</span></div><div class="line"><a name="l03918"></a><span class="lineno"> 3918</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputDimSize; ++i)</div><div class="line"><a name="l03919"></a><span class="lineno"> 3919</span>  {</div><div class="line"><a name="l03920"></a><span class="lineno"> 3920</span>  splitterDimSizes[i] = inputTensorInfo.GetShape()[i];</div><div class="line"><a name="l03921"></a><span class="lineno"> 3921</span>  }</div><div class="line"><a name="l03922"></a><span class="lineno"> 3922</span> </div><div class="line"><a name="l03923"></a><span class="lineno"> 3923</span>  <span class="keywordflow">if</span> (splitterDimSizes[splitDim] % numSplits != 0)</div><div class="line"><a name="l03924"></a><span class="lineno"> 3924</span>  {</div><div class="line"><a name="l03925"></a><span class="lineno"> 3925</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"Number of splits must evenly divide the dimension"</span>);</div><div class="line"><a name="l03926"></a><span class="lineno"> 3926</span>  }</div><div class="line"><a name="l03927"></a><span class="lineno"> 3927</span>  splitterDimSizes[splitDim] /= numSplits;</div><div class="line"><a name="l03928"></a><span class="lineno"> 3928</span> </div><div class="line"><a name="l03929"></a><span class="lineno"> 3929</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">SplitterDescriptor</a> splitDesc(numSplits, inputDimSize);</div><div class="line"><a name="l03930"></a><span class="lineno"> 3930</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> j = 0; j < numSplits; ++j)</div><div class="line"><a name="l03931"></a><span class="lineno"> 3931</span>  {</div><div class="line"><a name="l03932"></a><span class="lineno"> 3932</span>  <span class="comment">// Set the size of the views.</span></div><div class="line"><a name="l03933"></a><span class="lineno"> 3933</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx = 0; dimIdx < splitterDimSizes.size(); ++dimIdx)</div><div class="line"><a name="l03934"></a><span class="lineno"> 3934</span>  {</div><div class="line"><a name="l03935"></a><span class="lineno"> 3935</span>  splitDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(j, dimIdx, splitterDimSizes[dimIdx]);</div><div class="line"><a name="l03936"></a><span class="lineno"> 3936</span>  }</div><div class="line"><a name="l03937"></a><span class="lineno"> 3937</span>  splitDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(j, splitDim, splitterDimSizes[splitDim] * j);</div><div class="line"><a name="l03938"></a><span class="lineno"> 3938</span>  }</div><div class="line"><a name="l03939"></a><span class="lineno"> 3939</span> </div><div class="line"><a name="l03940"></a><span class="lineno"> 3940</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Split:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03941"></a><span class="lineno"> 3941</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddSplitterLayer(splitDesc, layerName.c_str());</div><div class="line"><a name="l03942"></a><span class="lineno"> 3942</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l03943"></a><span class="lineno"> 3943</span> </div><div class="line"><a name="l03944"></a><span class="lineno"> 3944</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l03945"></a><span class="lineno"> 3945</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[1]});</div><div class="line"><a name="l03946"></a><span class="lineno"> 3946</span> </div><div class="line"><a name="l03947"></a><span class="lineno"> 3947</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> k = 0; k < layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(); ++k)</div><div class="line"><a name="l03948"></a><span class="lineno"> 3948</span>  {</div><div class="line"><a name="l03949"></a><span class="lineno"> 3949</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(outputs[k], <span class="keyword">true</span>);</div><div class="line"><a name="l03950"></a><span class="lineno"> 3950</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(k).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l03951"></a><span class="lineno"> 3951</span>  }</div><div class="line"><a name="l03952"></a><span class="lineno"> 3952</span> </div><div class="line"><a name="l03953"></a><span class="lineno"> 3953</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l03954"></a><span class="lineno"> 3954</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</div><div class="line"><a name="l03955"></a><span class="lineno"> 3955</span> }</div><div class="line"><a name="l03956"></a><span class="lineno"> 3956</span> </div><div class="line"><a name="l03957"></a><span class="lineno"><a class="line" href="namespacearmnn_tf_lite_parser.xhtml#a85c693ec44ec126eb56462857fe634fc"> 3957</a></span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearmnn_tf_lite_parser.xhtml#a85c693ec44ec126eb56462857fe634fc">ComputeWrappedIndex</a>(<span class="keywordtype">int</span> idx, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDimsIn)</div><div class="line"><a name="l03958"></a><span class="lineno"> 3958</span> {</div><div class="line"><a name="l03959"></a><span class="lineno"> 3959</span>  <span class="keywordtype">int</span> numDims = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">int</span>>(numDimsIn);</div><div class="line"><a name="l03960"></a><span class="lineno"> 3960</span>  <span class="keywordtype">int</span> v = idx < 0 ? numDims + idx : idx;</div><div class="line"><a name="l03961"></a><span class="lineno"> 3961</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(v >= 0);</div><div class="line"><a name="l03962"></a><span class="lineno"> 3962</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(v < numDims);</div><div class="line"><a name="l03963"></a><span class="lineno"> 3963</span> </div><div class="line"><a name="l03964"></a><span class="lineno"> 3964</span>  <span class="keywordflow">return</span> <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(v);</div><div class="line"><a name="l03965"></a><span class="lineno"> 3965</span> }</div><div class="line"><a name="l03966"></a><span class="lineno"> 3966</span> </div><div class="line"><a name="l03967"></a><span class="lineno"> 3967</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseSplitV(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l03968"></a><span class="lineno"> 3968</span> {</div><div class="line"><a name="l03969"></a><span class="lineno"> 3969</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03970"></a><span class="lineno"> 3970</span> </div><div class="line"><a name="l03971"></a><span class="lineno"> 3971</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l03972"></a><span class="lineno"> 3972</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsSplitVOptions();</div><div class="line"><a name="l03973"></a><span class="lineno"> 3973</span> </div><div class="line"><a name="l03974"></a><span class="lineno"> 3974</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l03975"></a><span class="lineno"> 3975</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 3);</div><div class="line"><a name="l03976"></a><span class="lineno"> 3976</span> </div><div class="line"><a name="l03977"></a><span class="lineno"> 3977</span>  <span class="keyword">auto</span>& inputTensor = inputs[0];</div><div class="line"><a name="l03978"></a><span class="lineno"> 3978</span>  <span class="keyword">auto</span>& splitsTensor = inputs[1];</div><div class="line"><a name="l03979"></a><span class="lineno"> 3979</span>  <span class="keyword">auto</span>& axisTensor = inputs[2];</div><div class="line"><a name="l03980"></a><span class="lineno"> 3980</span> </div><div class="line"><a name="l03981"></a><span class="lineno"> 3981</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(inputTensor);</div><div class="line"><a name="l03982"></a><span class="lineno"> 3982</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> splitsInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(splitsTensor);</div><div class="line"><a name="l03983"></a><span class="lineno"> 3983</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> axisTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(axisTensor);</div><div class="line"><a name="l03984"></a><span class="lineno"> 3984</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(axisTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() == 1);</div><div class="line"><a name="l03985"></a><span class="lineno"> 3985</span> </div><div class="line"><a name="l03986"></a><span class="lineno"> 3986</span>  <span class="comment">// Inputs</span></div><div class="line"><a name="l03987"></a><span class="lineno"> 3987</span>  <span class="keyword">auto</span> inputDimSize = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l03988"></a><span class="lineno"> 3988</span>  <span class="keywordflow">if</span> (inputDimSize > <a class="code" href="namespacearmnn.xhtml#abdcd184ed3bd648bb31d385040cafd5d">MaxNumOfTensorDimensions</a>)</div><div class="line"><a name="l03989"></a><span class="lineno"> 3989</span>  {</div><div class="line"><a name="l03990"></a><span class="lineno"> 3990</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03991"></a><span class="lineno"> 3991</span>  fmt::format(<span class="stringliteral">"The number of dimensions: {} for input tensors of the "</span></div><div class="line"><a name="l03992"></a><span class="lineno"> 3992</span>  <span class="stringliteral">"SplitV op cannot be greater than {} {}"</span>,</div><div class="line"><a name="l03993"></a><span class="lineno"> 3993</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(),</div><div class="line"><a name="l03994"></a><span class="lineno"> 3994</span>  <a class="code" href="namespacearmnn.xhtml#abdcd184ed3bd648bb31d385040cafd5d">MaxNumOfTensorDimensions</a>,</div><div class="line"><a name="l03995"></a><span class="lineno"> 3995</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03996"></a><span class="lineno"> 3996</span>  }</div><div class="line"><a name="l03997"></a><span class="lineno"> 3997</span> </div><div class="line"><a name="l03998"></a><span class="lineno"> 3998</span>  <span class="comment">// Get split axis</span></div><div class="line"><a name="l03999"></a><span class="lineno"> 3999</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> axisBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, axisTensor->buffer);</div><div class="line"><a name="l04000"></a><span class="lineno"> 4000</span>  <span class="keywordflow">if</span> (axisBufferPtr == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l04001"></a><span class="lineno"> 4001</span>  {</div><div class="line"><a name="l04002"></a><span class="lineno"> 4002</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l04003"></a><span class="lineno"> 4003</span>  fmt::format(<span class="stringliteral">"Operation has invalid inputs. Failed to read axis. {}"</span>,</div><div class="line"><a name="l04004"></a><span class="lineno"> 4004</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l04005"></a><span class="lineno"> 4005</span>  }</div><div class="line"><a name="l04006"></a><span class="lineno"> 4006</span> </div><div class="line"><a name="l04007"></a><span class="lineno"> 4007</span>  std::vector<int> axisData(axisTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l04008"></a><span class="lineno"> 4008</span>  ::memcpy(axisData.data(), axisBufferPtr->data.data(), axisTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l04009"></a><span class="lineno"> 4009</span>  int32_t axis = axisData[0];</div><div class="line"><a name="l04010"></a><span class="lineno"> 4010</span> </div><div class="line"><a name="l04011"></a><span class="lineno"> 4011</span>  <span class="keyword">auto</span> inputDimensions = <span class="keyword">static_cast<</span>int32_t<span class="keyword">></span>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>());</div><div class="line"><a name="l04012"></a><span class="lineno"> 4012</span>  <span class="keywordflow">if</span> (((axis < -inputDimensions) && (axis < 0)) || ((axis >= inputDimensions) && (axis > 0)))</div><div class="line"><a name="l04013"></a><span class="lineno"> 4013</span>  {</div><div class="line"><a name="l04014"></a><span class="lineno"> 4014</span>  <span class="comment">// Square bracket denotes inclusive n while parenthesis denotes exclusive n</span></div><div class="line"><a name="l04015"></a><span class="lineno"> 4015</span>  <span class="comment">// E.g. Rank 4 tensor can have axis in range [-4, 3)</span></div><div class="line"><a name="l04016"></a><span class="lineno"> 4016</span>  <span class="comment">// -1 == 3, -2 == 2, -3 == 1, -4 == 0</span></div><div class="line"><a name="l04017"></a><span class="lineno"> 4017</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l04018"></a><span class="lineno"> 4018</span>  fmt::format(<span class="stringliteral">"Operation has invalid axis: {}. Axis must be in range [-n, n) {}"</span>,</div><div class="line"><a name="l04019"></a><span class="lineno"> 4019</span>  axis,</div><div class="line"><a name="l04020"></a><span class="lineno"> 4020</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l04021"></a><span class="lineno"> 4021</span>  }</div><div class="line"><a name="l04022"></a><span class="lineno"> 4022</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitDim = <a class="code" href="namespacearmnn_tf_lite_parser.xhtml#a85c693ec44ec126eb56462857fe634fc">ComputeWrappedIndex</a>(axis, inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>());</div><div class="line"><a name="l04023"></a><span class="lineno"> 4023</span> </div><div class="line"><a name="l04024"></a><span class="lineno"> 4024</span>  <span class="comment">// Set split sizes</span></div><div class="line"><a name="l04025"></a><span class="lineno"> 4025</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(splitsInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(), 1);</div><div class="line"><a name="l04026"></a><span class="lineno"> 4026</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numSplits{0};</div><div class="line"><a name="l04027"></a><span class="lineno"> 4027</span> </div><div class="line"><a name="l04028"></a><span class="lineno"> 4028</span>  <span class="keywordflow">if</span>(options)</div><div class="line"><a name="l04029"></a><span class="lineno"> 4029</span>  {</div><div class="line"><a name="l04030"></a><span class="lineno"> 4030</span>  numSplits = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(options->num_splits);</div><div class="line"><a name="l04031"></a><span class="lineno"> 4031</span>  }</div><div class="line"><a name="l04032"></a><span class="lineno"> 4032</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l04033"></a><span class="lineno"> 4033</span>  {</div><div class="line"><a name="l04034"></a><span class="lineno"> 4034</span>  numSplits = splitsInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>();</div><div class="line"><a name="l04035"></a><span class="lineno"> 4035</span>  }</div><div class="line"><a name="l04036"></a><span class="lineno"> 4036</span> </div><div class="line"><a name="l04037"></a><span class="lineno"> 4037</span>  <span class="keywordflow">if</span> (numSplits <=0)</div><div class="line"><a name="l04038"></a><span class="lineno"> 4038</span>  {</div><div class="line"><a name="l04039"></a><span class="lineno"> 4039</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"SplitV has invalid number of splits"</span>);</div><div class="line"><a name="l04040"></a><span class="lineno"> 4040</span>  }</div><div class="line"><a name="l04041"></a><span class="lineno"> 4041</span> </div><div class="line"><a name="l04042"></a><span class="lineno"> 4042</span>  std::vector<int> splitsData(numSplits);</div><div class="line"><a name="l04043"></a><span class="lineno"> 4043</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> splitsBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, splitsTensor->buffer);</div><div class="line"><a name="l04044"></a><span class="lineno"> 4044</span>  ::memcpy(splitsData.data(), splitsBufferPtr->data.data(), splitsInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l04045"></a><span class="lineno"> 4045</span> </div><div class="line"><a name="l04046"></a><span class="lineno"> 4046</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx = 0;</div><div class="line"><a name="l04047"></a><span class="lineno"> 4047</span>  <span class="keywordtype">int</span> numInferred{0};</div><div class="line"><a name="l04048"></a><span class="lineno"> 4048</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inferIdx{0};</div><div class="line"><a name="l04049"></a><span class="lineno"> 4049</span>  <span class="keywordtype">int</span> splitSum{0};</div><div class="line"><a name="l04050"></a><span class="lineno"> 4050</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> split : splitsData)</div><div class="line"><a name="l04051"></a><span class="lineno"> 4051</span>  {</div><div class="line"><a name="l04052"></a><span class="lineno"> 4052</span>  <span class="keywordflow">if</span> (split < 0)</div><div class="line"><a name="l04053"></a><span class="lineno"> 4053</span>  {</div><div class="line"><a name="l04054"></a><span class="lineno"> 4054</span>  numInferred++;</div><div class="line"><a name="l04055"></a><span class="lineno"> 4055</span>  inferIdx = idx;</div><div class="line"><a name="l04056"></a><span class="lineno"> 4056</span>  }</div><div class="line"><a name="l04057"></a><span class="lineno"> 4057</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l04058"></a><span class="lineno"> 4058</span>  {</div><div class="line"><a name="l04059"></a><span class="lineno"> 4059</span>  splitSum += split;</div><div class="line"><a name="l04060"></a><span class="lineno"> 4060</span>  }</div><div class="line"><a name="l04061"></a><span class="lineno"> 4061</span>  idx++;</div><div class="line"><a name="l04062"></a><span class="lineno"> 4062</span>  }</div><div class="line"><a name="l04063"></a><span class="lineno"> 4063</span>  <span class="comment">// Check for inferred Axis</span></div><div class="line"><a name="l04064"></a><span class="lineno"> 4064</span>  <span class="keywordflow">if</span> (numInferred == 0)</div><div class="line"><a name="l04065"></a><span class="lineno"> 4065</span>  {</div><div class="line"><a name="l04066"></a><span class="lineno"> 4066</span>  <span class="keywordflow">if</span> (splitSum != armnn::numeric_cast<int>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[splitDim]))</div><div class="line"><a name="l04067"></a><span class="lineno"> 4067</span>  {</div><div class="line"><a name="l04068"></a><span class="lineno"> 4068</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"SplitV split_sizes does not sum to the dimension of value along split_dim."</span>);</div><div class="line"><a name="l04069"></a><span class="lineno"> 4069</span>  }</div><div class="line"><a name="l04070"></a><span class="lineno"> 4070</span>  }</div><div class="line"><a name="l04071"></a><span class="lineno"> 4071</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (numInferred == 1)</div><div class="line"><a name="l04072"></a><span class="lineno"> 4072</span>  {</div><div class="line"><a name="l04073"></a><span class="lineno"> 4073</span>  splitsData[inferIdx] = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">int</span>>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[splitDim]) - splitSum;</div><div class="line"><a name="l04074"></a><span class="lineno"> 4074</span>  }</div><div class="line"><a name="l04075"></a><span class="lineno"> 4075</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l04076"></a><span class="lineno"> 4076</span>  {</div><div class="line"><a name="l04077"></a><span class="lineno"> 4077</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"Cannot infer split size for more than one split"</span>);</div><div class="line"><a name="l04078"></a><span class="lineno"> 4078</span>  }</div><div class="line"><a name="l04079"></a><span class="lineno"> 4079</span> </div><div class="line"><a name="l04080"></a><span class="lineno"> 4080</span>  <span class="comment">//Ouput size validation</span></div><div class="line"><a name="l04081"></a><span class="lineno"> 4081</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04082"></a><span class="lineno"> 4082</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), numSplits);</div><div class="line"><a name="l04083"></a><span class="lineno"> 4083</span> </div><div class="line"><a name="l04084"></a><span class="lineno"> 4084</span>  <span class="comment">// Setup Armnn descriptor</span></div><div class="line"><a name="l04085"></a><span class="lineno"> 4085</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">SplitterDescriptor</a> splitDesc(numSplits, inputDimSize);</div><div class="line"><a name="l04086"></a><span class="lineno"> 4086</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> accumSplit = 0;</div><div class="line"><a name="l04087"></a><span class="lineno"> 4087</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> j = 0; j < numSplits; ++j)</div><div class="line"><a name="l04088"></a><span class="lineno"> 4088</span>  {</div><div class="line"><a name="l04089"></a><span class="lineno"> 4089</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitSize = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(splitsData[j]);</div><div class="line"><a name="l04090"></a><span class="lineno"> 4090</span> </div><div class="line"><a name="l04091"></a><span class="lineno"> 4091</span>  <span class="comment">// Set the size of the views.</span></div><div class="line"><a name="l04092"></a><span class="lineno"> 4092</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx = 0; dimIdx < inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++dimIdx)</div><div class="line"><a name="l04093"></a><span class="lineno"> 4093</span>  {</div><div class="line"><a name="l04094"></a><span class="lineno"> 4094</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimSize = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[dimIdx];</div><div class="line"><a name="l04095"></a><span class="lineno"> 4095</span>  <span class="keywordflow">if</span> (dimIdx == splitDim)</div><div class="line"><a name="l04096"></a><span class="lineno"> 4096</span>  {</div><div class="line"><a name="l04097"></a><span class="lineno"> 4097</span>  dimSize = splitSize;</div><div class="line"><a name="l04098"></a><span class="lineno"> 4098</span>  }</div><div class="line"><a name="l04099"></a><span class="lineno"> 4099</span>  splitDesc.SetViewSize(j, dimIdx, dimSize);</div><div class="line"><a name="l04100"></a><span class="lineno"> 4100</span>  }</div><div class="line"><a name="l04101"></a><span class="lineno"> 4101</span> </div><div class="line"><a name="l04102"></a><span class="lineno"> 4102</span>  splitDesc.SetViewOriginCoord(j, splitDim, accumSplit);</div><div class="line"><a name="l04103"></a><span class="lineno"> 4103</span>  accumSplit += splitSize;</div><div class="line"><a name="l04104"></a><span class="lineno"> 4104</span>  }</div><div class="line"><a name="l04105"></a><span class="lineno"> 4105</span> </div><div class="line"><a name="l04106"></a><span class="lineno"> 4106</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"SplitV:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04107"></a><span class="lineno"> 4107</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddSplitterLayer(splitDesc, layerName.c_str());</div><div class="line"><a name="l04108"></a><span class="lineno"> 4108</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l04109"></a><span class="lineno"> 4109</span> </div><div class="line"><a name="l04110"></a><span class="lineno"> 4110</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04111"></a><span class="lineno"> 4111</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l04112"></a><span class="lineno"> 4112</span> </div><div class="line"><a name="l04113"></a><span class="lineno"> 4113</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> k = 0; k < layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(); ++k)</div><div class="line"><a name="l04114"></a><span class="lineno"> 4114</span>  {</div><div class="line"><a name="l04115"></a><span class="lineno"> 4115</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(outputs[k], <span class="keyword">true</span>);</div><div class="line"><a name="l04116"></a><span class="lineno"> 4116</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(k).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l04117"></a><span class="lineno"> 4117</span>  }</div><div class="line"><a name="l04118"></a><span class="lineno"> 4118</span> </div><div class="line"><a name="l04119"></a><span class="lineno"> 4119</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04120"></a><span class="lineno"> 4120</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</div><div class="line"><a name="l04121"></a><span class="lineno"> 4121</span> }</div><div class="line"><a name="l04122"></a><span class="lineno"> 4122</span> </div><div class="line"><a name="l04123"></a><span class="lineno"> 4123</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseArgMin(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04124"></a><span class="lineno"> 4124</span> {</div><div class="line"><a name="l04125"></a><span class="lineno"> 4125</span>  ParseArgMinMax(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeaea78d811e98514cd165dda532286610fd2">armnn::ArgMinMaxFunction::Min</a>);</div><div class="line"><a name="l04126"></a><span class="lineno"> 4126</span> }</div><div class="line"><a name="l04127"></a><span class="lineno"> 4127</span> </div><div class="line"><a name="l04128"></a><span class="lineno"> 4128</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseArgMax(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04129"></a><span class="lineno"> 4129</span> {</div><div class="line"><a name="l04130"></a><span class="lineno"> 4130</span>  ParseArgMinMax(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">armnn::ArgMinMaxFunction::Max</a>);</div><div class="line"><a name="l04131"></a><span class="lineno"> 4131</span> }</div><div class="line"><a name="l04132"></a><span class="lineno"> 4132</span> </div><div class="line"><a name="l04133"></a><span class="lineno"> 4133</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseArgMinMax(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex, <a class="code" href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeae">ArgMinMaxFunction</a> argMinMaxFunction)</div><div class="line"><a name="l04134"></a><span class="lineno"> 4134</span> {</div><div class="line"><a name="l04135"></a><span class="lineno"> 4135</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04136"></a><span class="lineno"> 4136</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04137"></a><span class="lineno"> 4137</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l04138"></a><span class="lineno"> 4138</span> </div><div class="line"><a name="l04139"></a><span class="lineno"> 4139</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04140"></a><span class="lineno"> 4140</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l04141"></a><span class="lineno"> 4141</span> </div><div class="line"><a name="l04142"></a><span class="lineno"> 4142</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l04143"></a><span class="lineno"> 4143</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> axisTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l04144"></a><span class="lineno"> 4144</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l04145"></a><span class="lineno"> 4145</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(axisTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() == 1);</div><div class="line"><a name="l04146"></a><span class="lineno"> 4146</span> </div><div class="line"><a name="l04147"></a><span class="lineno"> 4147</span>  <span class="comment">// Check if output tensor type is Signed32 or Signed64</span></div><div class="line"><a name="l04148"></a><span class="lineno"> 4148</span>  <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a> &&</div><div class="line"><a name="l04149"></a><span class="lineno"> 4149</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::DataType::Signed64</a>)</div><div class="line"><a name="l04150"></a><span class="lineno"> 4150</span>  {</div><div class="line"><a name="l04151"></a><span class="lineno"> 4151</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l04152"></a><span class="lineno"> 4152</span>  fmt::format(</div><div class="line"><a name="l04153"></a><span class="lineno"> 4153</span>  <span class="stringliteral">"Output tensor data type is not supported. (Supported types: Signed32 & Signed64) {}"</span>,</div><div class="line"><a name="l04154"></a><span class="lineno"> 4154</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l04155"></a><span class="lineno"> 4155</span>  }</div><div class="line"><a name="l04156"></a><span class="lineno"> 4156</span> </div><div class="line"><a name="l04157"></a><span class="lineno"> 4157</span>  <span class="comment">// Get const axis value from model and set it to descriptor.</span></div><div class="line"><a name="l04158"></a><span class="lineno"> 4158</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> axisBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l04159"></a><span class="lineno"> 4159</span>  <span class="keywordflow">if</span> (axisBufferPtr == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l04160"></a><span class="lineno"> 4160</span>  {</div><div class="line"><a name="l04161"></a><span class="lineno"> 4161</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l04162"></a><span class="lineno"> 4162</span>  fmt::format(<span class="stringliteral">"Operation has invalid inputs. Failed to read axis. {}"</span>,</div><div class="line"><a name="l04163"></a><span class="lineno"> 4163</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l04164"></a><span class="lineno"> 4164</span>  }</div><div class="line"><a name="l04165"></a><span class="lineno"> 4165</span> </div><div class="line"><a name="l04166"></a><span class="lineno"> 4166</span>  std::vector<int32_t> axisData(axisTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l04167"></a><span class="lineno"> 4167</span>  ::memcpy(axisData.data(), axisBufferPtr->data.data(), axisTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l04168"></a><span class="lineno"> 4168</span>  int32_t axis = axisData.front();</div><div class="line"><a name="l04169"></a><span class="lineno"> 4169</span> </div><div class="line"><a name="l04170"></a><span class="lineno"> 4170</span>  <span class="keyword">auto</span> inputDimensions = <span class="keyword">static_cast<</span>int32_t<span class="keyword">></span>(inputTensorInfo.GetNumDimensions());</div><div class="line"><a name="l04171"></a><span class="lineno"> 4171</span>  <span class="keywordflow">if</span> (((axis < -inputDimensions) && (axis < 0)) || ((axis >= inputDimensions) && (axis > 0)))</div><div class="line"><a name="l04172"></a><span class="lineno"> 4172</span>  {</div><div class="line"><a name="l04173"></a><span class="lineno"> 4173</span>  <span class="comment">// Square bracket denotes inclusive n while parenthesis denotes exclusive n</span></div><div class="line"><a name="l04174"></a><span class="lineno"> 4174</span>  <span class="comment">// E.g. Rank 4 tensor can have axis in range [-4, 3)</span></div><div class="line"><a name="l04175"></a><span class="lineno"> 4175</span>  <span class="comment">// -1 == 3, -2 == 2, -3 == 1, -4 == 0</span></div><div class="line"><a name="l04176"></a><span class="lineno"> 4176</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l04177"></a><span class="lineno"> 4177</span>  fmt::format(<span class="stringliteral">"Operation has invalid axis: {}. Axis must be in range [-n, n) {}"</span>,</div><div class="line"><a name="l04178"></a><span class="lineno"> 4178</span>  axis,</div><div class="line"><a name="l04179"></a><span class="lineno"> 4179</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l04180"></a><span class="lineno"> 4180</span>  }</div><div class="line"><a name="l04181"></a><span class="lineno"> 4181</span> </div><div class="line"><a name="l04182"></a><span class="lineno"> 4182</span>  <a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml">ArgMinMaxDescriptor</a> desc;</div><div class="line"><a name="l04183"></a><span class="lineno"> 4183</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="l04184"></a><span class="lineno"> 4184</span>  desc.<a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml#ab1ae6f520bb1a4da191a0ae907477f23">m_Function</a> = argMinMaxFunction;</div><div class="line"><a name="l04185"></a><span class="lineno"> 4185</span> </div><div class="line"><a name="l04186"></a><span class="lineno"> 4186</span>  <span class="comment">// Register a ArgMin/ArgMax layer.</span></div><div class="line"><a name="l04187"></a><span class="lineno"> 4187</span>  <span class="keyword">auto</span> layerName = argMinMaxFunction == ArgMinMaxFunction::Max ? <span class="stringliteral">"ArgMax:{}:{}"</span> : <span class="stringliteral">"ArgMin:{}:{}"</span>;</div><div class="line"><a name="l04188"></a><span class="lineno"> 4188</span>  <span class="keyword">auto</span> layerNameFormatted = fmt::format(layerName, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04189"></a><span class="lineno"> 4189</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a> *layer = m_Network->AddArgMinMaxLayer(desc, layerNameFormatted.c_str());</div><div class="line"><a name="l04190"></a><span class="lineno"> 4190</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l04191"></a><span class="lineno"> 4191</span>  outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l04192"></a><span class="lineno"> 4192</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l04193"></a><span class="lineno"> 4193</span> </div><div class="line"><a name="l04194"></a><span class="lineno"> 4194</span>  <span class="comment">// Register input tensor to the layer.</span></div><div class="line"><a name="l04195"></a><span class="lineno"> 4195</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04196"></a><span class="lineno"> 4196</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l04197"></a><span class="lineno"> 4197</span> </div><div class="line"><a name="l04198"></a><span class="lineno"> 4198</span>  <span class="comment">// Register output tensor to the layer.</span></div><div class="line"><a name="l04199"></a><span class="lineno"> 4199</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04200"></a><span class="lineno"> 4200</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</div><div class="line"><a name="l04201"></a><span class="lineno"> 4201</span> }</div><div class="line"><a name="l04202"></a><span class="lineno"> 4202</span> </div><div class="line"><a name="l04203"></a><span class="lineno"> 4203</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseGather(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04204"></a><span class="lineno"> 4204</span> {</div><div class="line"><a name="l04205"></a><span class="lineno"> 4205</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04206"></a><span class="lineno"> 4206</span> </div><div class="line"><a name="l04207"></a><span class="lineno"> 4207</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParserImpl::TensorRawPtrVector</a> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04208"></a><span class="lineno"> 4208</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l04209"></a><span class="lineno"> 4209</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParserImpl::TensorRawPtrVector</a> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04210"></a><span class="lineno"> 4210</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l04211"></a><span class="lineno"> 4211</span> </div><div class="line"><a name="l04212"></a><span class="lineno"> 4212</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l04213"></a><span class="lineno"> 4213</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> indicesTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l04214"></a><span class="lineno"> 4214</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l04215"></a><span class="lineno"> 4215</span> </div><div class="line"><a name="l04216"></a><span class="lineno"> 4216</span>  <a class="code" href="structarmnn_1_1_gather_descriptor.xhtml">armnn::GatherDescriptor</a> gatherDescriptor;</div><div class="line"><a name="l04217"></a><span class="lineno"> 4217</span> </div><div class="line"><a name="l04218"></a><span class="lineno"> 4218</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l04219"></a><span class="lineno"> 4219</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsGatherOptions();</div><div class="line"><a name="l04220"></a><span class="lineno"> 4220</span>  <span class="keyword">auto</span> axis = options->axis;</div><div class="line"><a name="l04221"></a><span class="lineno"> 4221</span> </div><div class="line"><a name="l04222"></a><span class="lineno"> 4222</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Gather:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04223"></a><span class="lineno"> 4223</span> </div><div class="line"><a name="l04224"></a><span class="lineno"> 4224</span>  <span class="keyword">auto</span> inputDimensions = <span class="keyword">static_cast<</span>int32_t<span class="keyword">></span>(inputTensorInfo.GetNumDimensions());</div><div class="line"><a name="l04225"></a><span class="lineno"> 4225</span>  <span class="keyword">auto</span> indicesDimensions = indicesTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l04226"></a><span class="lineno"> 4226</span>  <span class="keyword">auto</span> outputDimensions = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l04227"></a><span class="lineno"> 4227</span>  <span class="keywordflow">if</span> (((axis < -inputDimensions) && (axis < 0)) || ((axis >= inputDimensions) && (axis > 0)))</div><div class="line"><a name="l04228"></a><span class="lineno"> 4228</span>  {</div><div class="line"><a name="l04229"></a><span class="lineno"> 4229</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l04230"></a><span class="lineno"> 4230</span>  fmt::format(<span class="stringliteral">"Operation has invalid axis: {} It is out of bounds [ -{}, {} ) {}"</span>,</div><div class="line"><a name="l04231"></a><span class="lineno"> 4231</span>  axis,</div><div class="line"><a name="l04232"></a><span class="lineno"> 4232</span>  inputDimensions, inputDimensions,</div><div class="line"><a name="l04233"></a><span class="lineno"> 4233</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l04234"></a><span class="lineno"> 4234</span>  }</div><div class="line"><a name="l04235"></a><span class="lineno"> 4235</span>  <span class="keywordflow">if</span> (outputDimensions != static_cast<unsigned int>(inputDimensions) + indicesDimensions - 1)</div><div class="line"><a name="l04236"></a><span class="lineno"> 4236</span>  {</div><div class="line"><a name="l04237"></a><span class="lineno"> 4237</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l04238"></a><span class="lineno"> 4238</span>  fmt::format(<span class="stringliteral">"Operation has invalid output dimensions: {} Output must be an ({} + {} - 1) -D tensor {}"</span>,</div><div class="line"><a name="l04239"></a><span class="lineno"> 4239</span>  outputDimensions,</div><div class="line"><a name="l04240"></a><span class="lineno"> 4240</span>  inputDimensions, indicesDimensions,</div><div class="line"><a name="l04241"></a><span class="lineno"> 4241</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l04242"></a><span class="lineno"> 4242</span>  }</div><div class="line"><a name="l04243"></a><span class="lineno"> 4243</span> </div><div class="line"><a name="l04244"></a><span class="lineno"> 4244</span>  gatherDescriptor.<a class="code" href="structarmnn_1_1_gather_descriptor.xhtml#a35d11c7d509d1adbae1ae01c58394a7f">m_Axis</a> = axis;</div><div class="line"><a name="l04245"></a><span class="lineno"> 4245</span> </div><div class="line"><a name="l04246"></a><span class="lineno"> 4246</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddGatherLayer(gatherDescriptor, layerName.c_str());</div><div class="line"><a name="l04247"></a><span class="lineno"> 4247</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l04248"></a><span class="lineno"> 4248</span>  outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});</div><div class="line"><a name="l04249"></a><span class="lineno"> 4249</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l04250"></a><span class="lineno"> 4250</span> </div><div class="line"><a name="l04251"></a><span class="lineno"> 4251</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04252"></a><span class="lineno"> 4252</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l04253"></a><span class="lineno"> 4253</span> </div><div class="line"><a name="l04254"></a><span class="lineno"> 4254</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04255"></a><span class="lineno"> 4255</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l04256"></a><span class="lineno"> 4256</span> }</div><div class="line"><a name="l04257"></a><span class="lineno"> 4257</span> </div><div class="line"><a name="l04258"></a><span class="lineno"> 4258</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseGatherNd(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04259"></a><span class="lineno"> 4259</span> {</div><div class="line"><a name="l04260"></a><span class="lineno"> 4260</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04261"></a><span class="lineno"> 4261</span> </div><div class="line"><a name="l04262"></a><span class="lineno"> 4262</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParserImpl::TensorRawPtrVector</a> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04263"></a><span class="lineno"> 4263</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l04264"></a><span class="lineno"> 4264</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParserImpl::TensorRawPtrVector</a> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04265"></a><span class="lineno"> 4265</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l04266"></a><span class="lineno"> 4266</span> </div><div class="line"><a name="l04267"></a><span class="lineno"> 4267</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l04268"></a><span class="lineno"> 4268</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> indicesTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l04269"></a><span class="lineno"> 4269</span> </div><div class="line"><a name="l04270"></a><span class="lineno"> 4270</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"GatherNd:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04271"></a><span class="lineno"> 4271</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddGatherNdLayer(layerName.c_str());</div><div class="line"><a name="l04272"></a><span class="lineno"> 4272</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l04273"></a><span class="lineno"> 4273</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});</div><div class="line"><a name="l04274"></a><span class="lineno"> 4274</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l04275"></a><span class="lineno"> 4275</span> </div><div class="line"><a name="l04276"></a><span class="lineno"> 4276</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04277"></a><span class="lineno"> 4277</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l04278"></a><span class="lineno"> 4278</span> </div><div class="line"><a name="l04279"></a><span class="lineno"> 4279</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04280"></a><span class="lineno"> 4280</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l04281"></a><span class="lineno"> 4281</span> }</div><div class="line"><a name="l04282"></a><span class="lineno"> 4282</span> </div><div class="line"><a name="l04283"></a><span class="lineno"> 4283</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseDepthToSpace(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04284"></a><span class="lineno"> 4284</span> {</div><div class="line"><a name="l04285"></a><span class="lineno"> 4285</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04286"></a><span class="lineno"> 4286</span> </div><div class="line"><a name="l04287"></a><span class="lineno"> 4287</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParserImpl::TensorRawPtrVector</a> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04288"></a><span class="lineno"> 4288</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l04289"></a><span class="lineno"> 4289</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParserImpl::TensorRawPtrVector</a> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04290"></a><span class="lineno"> 4290</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l04291"></a><span class="lineno"> 4291</span> </div><div class="line"><a name="l04292"></a><span class="lineno"> 4292</span>  <a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml">armnn::DepthToSpaceDescriptor</a> descriptor;</div><div class="line"><a name="l04293"></a><span class="lineno"> 4293</span> </div><div class="line"><a name="l04294"></a><span class="lineno"> 4294</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l04295"></a><span class="lineno"> 4295</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsDepthToSpaceOptions();</div><div class="line"><a name="l04296"></a><span class="lineno"> 4296</span>  <span class="keyword">auto</span> blockSize = options->block_size;</div><div class="line"><a name="l04297"></a><span class="lineno"> 4297</span>  <span class="keywordflow">if</span> (blockSize < 2)</div><div class="line"><a name="l04298"></a><span class="lineno"> 4298</span>  {</div><div class="line"><a name="l04299"></a><span class="lineno"> 4299</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l04300"></a><span class="lineno"> 4300</span>  fmt::format(<span class="stringliteral">"Operation has invalid block size: {} Block size should be >= 2 {}"</span>,</div><div class="line"><a name="l04301"></a><span class="lineno"> 4301</span>  blockSize,</div><div class="line"><a name="l04302"></a><span class="lineno"> 4302</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l04303"></a><span class="lineno"> 4303</span>  }</div><div class="line"><a name="l04304"></a><span class="lineno"> 4304</span>  descriptor.m_BlockSize = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><uint32_t>(blockSize);</div><div class="line"><a name="l04305"></a><span class="lineno"> 4305</span> </div><div class="line"><a name="l04306"></a><span class="lineno"> 4306</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"DepthToSpace:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04307"></a><span class="lineno"> 4307</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddDepthToSpaceLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l04308"></a><span class="lineno"> 4308</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l04309"></a><span class="lineno"> 4309</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l04310"></a><span class="lineno"> 4310</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l04311"></a><span class="lineno"> 4311</span> </div><div class="line"><a name="l04312"></a><span class="lineno"> 4312</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04313"></a><span class="lineno"> 4313</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l04314"></a><span class="lineno"> 4314</span> </div><div class="line"><a name="l04315"></a><span class="lineno"> 4315</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04316"></a><span class="lineno"> 4316</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l04317"></a><span class="lineno"> 4317</span> }</div><div class="line"><a name="l04318"></a><span class="lineno"> 4318</span> </div><div class="line"><a name="l04319"></a><span class="lineno"> 4319</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseSum(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04320"></a><span class="lineno"> 4320</span> {</div><div class="line"><a name="l04321"></a><span class="lineno"> 4321</span>  ParseReduce(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0aa0ec87054b5e5b7847d0d8780a01a3d5">armnn::ReduceOperation::Sum</a>);</div><div class="line"><a name="l04322"></a><span class="lineno"> 4322</span> }</div><div class="line"><a name="l04323"></a><span class="lineno"> 4323</span> </div><div class="line"><a name="l04324"></a><span class="lineno"> 4324</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseReduceProd(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04325"></a><span class="lineno"> 4325</span> {</div><div class="line"><a name="l04326"></a><span class="lineno"> 4326</span>  ParseReduce(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0a362a33c23b08e4a32a4ec53fbb82cccd">armnn::ReduceOperation::Prod</a>);</div><div class="line"><a name="l04327"></a><span class="lineno"> 4327</span> }</div><div class="line"><a name="l04328"></a><span class="lineno"> 4328</span> </div><div class="line"><a name="l04329"></a><span class="lineno"> 4329</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseReduceMax(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04330"></a><span class="lineno"> 4330</span> {</div><div class="line"><a name="l04331"></a><span class="lineno"> 4331</span>  ParseReduce(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0a6a061313d22e51e0f25b7cd4dc065233">armnn::ReduceOperation::Max</a>);</div><div class="line"><a name="l04332"></a><span class="lineno"> 4332</span> }</div><div class="line"><a name="l04333"></a><span class="lineno"> 4333</span> </div><div class="line"><a name="l04334"></a><span class="lineno"> 4334</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseReduceMin(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04335"></a><span class="lineno"> 4335</span> {</div><div class="line"><a name="l04336"></a><span class="lineno"> 4336</span>  ParseReduce(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeaea78d811e98514cd165dda532286610fd2">armnn::ReduceOperation::Min</a>);</div><div class="line"><a name="l04337"></a><span class="lineno"> 4337</span> }</div><div class="line"><a name="l04338"></a><span class="lineno"> 4338</span> </div><div class="line"><a name="l04339"></a><span class="lineno"> 4339</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseReduce(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex, <a class="code" href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0">ReduceOperation</a> reduceOperation)</div><div class="line"><a name="l04340"></a><span class="lineno"> 4340</span> {</div><div class="line"><a name="l04341"></a><span class="lineno"> 4341</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04342"></a><span class="lineno"> 4342</span> </div><div class="line"><a name="l04343"></a><span class="lineno"> 4343</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l04344"></a><span class="lineno"> 4344</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsReducerOptions();</div><div class="line"><a name="l04345"></a><span class="lineno"> 4345</span> </div><div class="line"><a name="l04346"></a><span class="lineno"> 4346</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04347"></a><span class="lineno"> 4347</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l04348"></a><span class="lineno"> 4348</span> </div><div class="line"><a name="l04349"></a><span class="lineno"> 4349</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04350"></a><span class="lineno"> 4350</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l04351"></a><span class="lineno"> 4351</span> </div><div class="line"><a name="l04352"></a><span class="lineno"> 4352</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"Reduce:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04353"></a><span class="lineno"> 4353</span> </div><div class="line"><a name="l04354"></a><span class="lineno"> 4354</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo0 = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l04355"></a><span class="lineno"> 4355</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo1 = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l04356"></a><span class="lineno"> 4356</span> </div><div class="line"><a name="l04357"></a><span class="lineno"> 4357</span>  <a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml">ReduceDescriptor</a> desc;</div><div class="line"><a name="l04358"></a><span class="lineno"> 4358</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> axisBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l04359"></a><span class="lineno"> 4359</span>  <span class="comment">// Get const axis value from model and set it to descriptor.</span></div><div class="line"><a name="l04360"></a><span class="lineno"> 4360</span>  <span class="keywordflow">if</span> (axisBufferPtr != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l04361"></a><span class="lineno"> 4361</span>  {</div><div class="line"><a name="l04362"></a><span class="lineno"> 4362</span>  std::vector<int32_t> axisData(inputTensorInfo1.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l04363"></a><span class="lineno"> 4363</span>  ::memcpy(axisData.data(), axisBufferPtr->data.data(), inputTensorInfo1.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l04364"></a><span class="lineno"> 4364</span> </div><div class="line"><a name="l04365"></a><span class="lineno"> 4365</span>  <span class="comment">// Convert the axis to unsigned int and remove duplicates.</span></div><div class="line"><a name="l04366"></a><span class="lineno"> 4366</span>  <span class="keyword">auto</span> rank = <span class="keyword">static_cast<</span>int32_t<span class="keyword">></span>(inputTensorInfo0.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>());</div><div class="line"><a name="l04367"></a><span class="lineno"> 4367</span>  std::set<unsigned int> uniqueAxis;</div><div class="line"><a name="l04368"></a><span class="lineno"> 4368</span>  std::transform(axisData.begin(),</div><div class="line"><a name="l04369"></a><span class="lineno"> 4369</span>  axisData.end(),</div><div class="line"><a name="l04370"></a><span class="lineno"> 4370</span>  std::inserter(uniqueAxis, uniqueAxis.begin()),</div><div class="line"><a name="l04371"></a><span class="lineno"> 4371</span>  [rank](<span class="keywordtype">int</span> i)->unsigned <span class="keywordtype">int</span>{</div><div class="line"><a name="l04372"></a><span class="lineno"> 4372</span>  <span class="keywordflow">return</span> <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(((i + rank) % rank)); });</div><div class="line"><a name="l04373"></a><span class="lineno"> 4373</span>  desc.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#aa1c6fc8c96404252f1072632fc5acb59">m_vAxis</a>.assign(uniqueAxis.begin(), uniqueAxis.end());</div><div class="line"><a name="l04374"></a><span class="lineno"> 4374</span>  }</div><div class="line"><a name="l04375"></a><span class="lineno"> 4375</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l04376"></a><span class="lineno"> 4376</span>  {</div><div class="line"><a name="l04377"></a><span class="lineno"> 4377</span>  <span class="keywordflow">for</span> (uint32_t i = 0; i < inputTensorInfo0.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++i)</div><div class="line"><a name="l04378"></a><span class="lineno"> 4378</span>  {</div><div class="line"><a name="l04379"></a><span class="lineno"> 4379</span>  desc.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#aa1c6fc8c96404252f1072632fc5acb59">m_vAxis</a>.push_back(i);</div><div class="line"><a name="l04380"></a><span class="lineno"> 4380</span>  }</div><div class="line"><a name="l04381"></a><span class="lineno"> 4381</span>  }</div><div class="line"><a name="l04382"></a><span class="lineno"> 4382</span> </div><div class="line"><a name="l04383"></a><span class="lineno"> 4383</span>  desc.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#a28e0548abfc4e79c48f29a3d11a062e9">m_KeepDims</a> = options->keep_dims;</div><div class="line"><a name="l04384"></a><span class="lineno"> 4384</span>  desc.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#aa57c67b1da0011b1abb30170146e870f">m_ReduceOperation</a> = reduceOperation;</div><div class="line"><a name="l04385"></a><span class="lineno"> 4385</span> </div><div class="line"><a name="l04386"></a><span class="lineno"> 4386</span>  <span class="comment">// Register a new layer object, Sum.</span></div><div class="line"><a name="l04387"></a><span class="lineno"> 4387</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddReduceLayer(desc, layerName.c_str());</div><div class="line"><a name="l04388"></a><span class="lineno"> 4388</span> </div><div class="line"><a name="l04389"></a><span class="lineno"> 4389</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l04390"></a><span class="lineno"> 4390</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l04391"></a><span class="lineno"> 4391</span> </div><div class="line"><a name="l04392"></a><span class="lineno"> 4392</span>  <span class="comment">// Register input tensor to the layer.</span></div><div class="line"><a name="l04393"></a><span class="lineno"> 4393</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04394"></a><span class="lineno"> 4394</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l04395"></a><span class="lineno"> 4395</span> </div><div class="line"><a name="l04396"></a><span class="lineno"> 4396</span>  <span class="comment">// Register output tensor to the layer.</span></div><div class="line"><a name="l04397"></a><span class="lineno"> 4397</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04398"></a><span class="lineno"> 4398</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</div><div class="line"><a name="l04399"></a><span class="lineno"> 4399</span> }</div><div class="line"><a name="l04400"></a><span class="lineno"> 4400</span> </div><div class="line"><a name="l04401"></a><span class="lineno"> 4401</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseLocalResponseNormalization(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04402"></a><span class="lineno"> 4402</span> {</div><div class="line"><a name="l04403"></a><span class="lineno"> 4403</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04404"></a><span class="lineno"> 4404</span> </div><div class="line"><a name="l04405"></a><span class="lineno"> 4405</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04406"></a><span class="lineno"> 4406</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l04407"></a><span class="lineno"> 4407</span> </div><div class="line"><a name="l04408"></a><span class="lineno"> 4408</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04409"></a><span class="lineno"> 4409</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l04410"></a><span class="lineno"> 4410</span> </div><div class="line"><a name="l04411"></a><span class="lineno"> 4411</span>  <span class="keyword">auto</span> layerName = fmt::format(<span class="stringliteral">"LRN:{}:{}"</span>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04412"></a><span class="lineno"> 4412</span>  std::string layerNameFormatted = fmt::format(layerName, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04413"></a><span class="lineno"> 4413</span> </div><div class="line"><a name="l04414"></a><span class="lineno"> 4414</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l04415"></a><span class="lineno"> 4415</span> </div><div class="line"><a name="l04416"></a><span class="lineno"> 4416</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l04417"></a><span class="lineno"> 4417</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* options = operatorPtr->builtin_options.AsLocalResponseNormalizationOptions();</div><div class="line"><a name="l04418"></a><span class="lineno"> 4418</span> </div><div class="line"><a name="l04419"></a><span class="lineno"> 4419</span>  <a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a> descriptor;</div><div class="line"><a name="l04420"></a><span class="lineno"> 4420</span>  descriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l04421"></a><span class="lineno"> 4421</span>  descriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">m_NormChannelType</a> = <a class="code" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc">armnn::NormalizationAlgorithmChannel::Across</a>;</div><div class="line"><a name="l04422"></a><span class="lineno"> 4422</span>  descriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a05945f080edf694b631960728b87aadb">m_NormMethodType</a> = <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d">armnn::NormalizationAlgorithmMethod::LocalBrightness</a>;</div><div class="line"><a name="l04423"></a><span class="lineno"> 4423</span>  descriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">m_NormSize</a> = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(options->radius);</div><div class="line"><a name="l04424"></a><span class="lineno"> 4424</span>  descriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">m_K</a> = options->bias;</div><div class="line"><a name="l04425"></a><span class="lineno"> 4425</span>  descriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">m_Alpha</a> = options->alpha;</div><div class="line"><a name="l04426"></a><span class="lineno"> 4426</span>  descriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = options->beta;</div><div class="line"><a name="l04427"></a><span class="lineno"> 4427</span> </div><div class="line"><a name="l04428"></a><span class="lineno"> 4428</span>  <span class="comment">// ArmNN expects normSize to be the full size of the normalization</span></div><div class="line"><a name="l04429"></a><span class="lineno"> 4429</span>  <span class="comment">// window rather than the radius as in TfLite.</span></div><div class="line"><a name="l04430"></a><span class="lineno"> 4430</span>  descriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">m_NormSize</a> = 1 + (2 * descriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">m_NormSize</a>);</div><div class="line"><a name="l04431"></a><span class="lineno"> 4431</span> </div><div class="line"><a name="l04432"></a><span class="lineno"> 4432</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddNormalizationLayer(descriptor, layerNameFormatted.c_str());</div><div class="line"><a name="l04433"></a><span class="lineno"> 4433</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l04434"></a><span class="lineno"> 4434</span> </div><div class="line"><a name="l04435"></a><span class="lineno"> 4435</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l04436"></a><span class="lineno"> 4436</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l04437"></a><span class="lineno"> 4437</span> </div><div class="line"><a name="l04438"></a><span class="lineno"> 4438</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04439"></a><span class="lineno"> 4439</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l04440"></a><span class="lineno"> 4440</span> </div><div class="line"><a name="l04441"></a><span class="lineno"> 4441</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04442"></a><span class="lineno"> 4442</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l04443"></a><span class="lineno"> 4443</span> }</div><div class="line"><a name="l04444"></a><span class="lineno"> 4444</span> </div><div class="line"><a name="l04445"></a><span class="lineno"> 4445</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseAbs(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04446"></a><span class="lineno"> 4446</span> {</div><div class="line"><a name="l04447"></a><span class="lineno"> 4447</span>  ParseElementwiseUnary(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa1e34af023adeb7d5f484f8eb4b9826b6">armnn::UnaryOperation::Abs</a>);</div><div class="line"><a name="l04448"></a><span class="lineno"> 4448</span> }</div><div class="line"><a name="l04449"></a><span class="lineno"> 4449</span> </div><div class="line"><a name="l04450"></a><span class="lineno"> 4450</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseExp(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04451"></a><span class="lineno"> 4451</span> {</div><div class="line"><a name="l04452"></a><span class="lineno"> 4452</span>  ParseElementwiseUnary(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8acad39a154bffb61175f674d6eefaf6d0">armnn::UnaryOperation::Exp</a>);</div><div class="line"><a name="l04453"></a><span class="lineno"> 4453</span> }</div><div class="line"><a name="l04454"></a><span class="lineno"> 4454</span> </div><div class="line"><a name="l04455"></a><span class="lineno"> 4455</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseLog(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04456"></a><span class="lineno"> 4456</span> {</div><div class="line"><a name="l04457"></a><span class="lineno"> 4457</span>  ParseElementwiseUnary(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8ace0be71e33226e4c1db2bcea5959f16b">armnn::UnaryOperation::Log</a>);</div><div class="line"><a name="l04458"></a><span class="lineno"> 4458</span> }</div><div class="line"><a name="l04459"></a><span class="lineno"> 4459</span> </div><div class="line"><a name="l04460"></a><span class="lineno"> 4460</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseLogicalNot(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04461"></a><span class="lineno"> 4461</span> {</div><div class="line"><a name="l04462"></a><span class="lineno"> 4462</span>  ParseElementwiseUnary(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8a2a25ebd8c909241e3f7818389b804ecc">armnn::UnaryOperation::LogicalNot</a>);</div><div class="line"><a name="l04463"></a><span class="lineno"> 4463</span> }</div><div class="line"><a name="l04464"></a><span class="lineno"> 4464</span> </div><div class="line"><a name="l04465"></a><span class="lineno"> 4465</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseNeg(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04466"></a><span class="lineno"> 4466</span> {</div><div class="line"><a name="l04467"></a><span class="lineno"> 4467</span>  ParseElementwiseUnary(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8afb278fa5defd7e699fcbc930c3e76ccd">armnn::UnaryOperation::Neg</a>);</div><div class="line"><a name="l04468"></a><span class="lineno"> 4468</span> }</div><div class="line"><a name="l04469"></a><span class="lineno"> 4469</span> </div><div class="line"><a name="l04470"></a><span class="lineno"> 4470</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseRsqrt(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04471"></a><span class="lineno"> 4471</span> {</div><div class="line"><a name="l04472"></a><span class="lineno"> 4472</span>  ParseElementwiseUnary(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8a09bb7f6e2118c85a6a55bd4bf2beeca4">armnn::UnaryOperation::Rsqrt</a>);</div><div class="line"><a name="l04473"></a><span class="lineno"> 4473</span> }</div><div class="line"><a name="l04474"></a><span class="lineno"> 4474</span> </div><div class="line"><a name="l04475"></a><span class="lineno"> 4475</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseSin(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04476"></a><span class="lineno"> 4476</span> {</div><div class="line"><a name="l04477"></a><span class="lineno"> 4477</span>  ParseElementwiseUnary(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8a0986d137604183312e6d3599578bc6cd">armnn::UnaryOperation::Sin</a>);</div><div class="line"><a name="l04478"></a><span class="lineno"> 4478</span> }</div><div class="line"><a name="l04479"></a><span class="lineno"> 4479</span> </div><div class="line"><a name="l04480"></a><span class="lineno"> 4480</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseSqrt(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04481"></a><span class="lineno"> 4481</span> {</div><div class="line"><a name="l04482"></a><span class="lineno"> 4482</span>  ParseElementwiseUnary(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaae77f3ad25595e35b327334d89410054">armnn::UnaryOperation::Sqrt</a>);</div><div class="line"><a name="l04483"></a><span class="lineno"> 4483</span> }</div><div class="line"><a name="l04484"></a><span class="lineno"> 4484</span> </div><div class="line"><a name="l04485"></a><span class="lineno"> 4485</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseElementwiseUnary(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex, <a class="code" href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8">UnaryOperation</a> unaryOperation)</div><div class="line"><a name="l04486"></a><span class="lineno"> 4486</span> {</div><div class="line"><a name="l04487"></a><span class="lineno"> 4487</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04488"></a><span class="lineno"> 4488</span> </div><div class="line"><a name="l04489"></a><span class="lineno"> 4489</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04490"></a><span class="lineno"> 4490</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l04491"></a><span class="lineno"> 4491</span> </div><div class="line"><a name="l04492"></a><span class="lineno"> 4492</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04493"></a><span class="lineno"> 4493</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l04494"></a><span class="lineno"> 4494</span> </div><div class="line"><a name="l04495"></a><span class="lineno"> 4495</span>  std::string layerName = std::string(<a class="code" href="namespacearmnn.xhtml#a6dac966f265381903c8ef4f392becced">GetUnaryOperationAsCString</a>(unaryOperation)) + <span class="stringliteral">":{}:{}"</span>;</div><div class="line"><a name="l04496"></a><span class="lineno"> 4496</span>  std::string layerNameFormatted = fmt::format(layerName, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04497"></a><span class="lineno"> 4497</span> </div><div class="line"><a name="l04498"></a><span class="lineno"> 4498</span>  <a class="code" href="structarmnn_1_1_elementwise_unary_descriptor.xhtml">ElementwiseUnaryDescriptor</a> desc;</div><div class="line"><a name="l04499"></a><span class="lineno"> 4499</span>  desc.<a class="code" href="structarmnn_1_1_elementwise_unary_descriptor.xhtml#afe768be66897eb3d73284424e3239b23">m_Operation</a> = unaryOperation;</div><div class="line"><a name="l04500"></a><span class="lineno"> 4500</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddElementwiseUnaryLayer(desc, layerNameFormatted.c_str());</div><div class="line"><a name="l04501"></a><span class="lineno"> 4501</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l04502"></a><span class="lineno"> 4502</span> </div><div class="line"><a name="l04503"></a><span class="lineno"> 4503</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});</div><div class="line"><a name="l04504"></a><span class="lineno"> 4504</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l04505"></a><span class="lineno"> 4505</span> </div><div class="line"><a name="l04506"></a><span class="lineno"> 4506</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04507"></a><span class="lineno"> 4507</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l04508"></a><span class="lineno"> 4508</span> </div><div class="line"><a name="l04509"></a><span class="lineno"> 4509</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04510"></a><span class="lineno"> 4510</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</div><div class="line"><a name="l04511"></a><span class="lineno"> 4511</span> }</div><div class="line"><a name="l04512"></a><span class="lineno"> 4512</span> </div><div class="line"><a name="l04513"></a><span class="lineno"> 4513</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseEqual(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04514"></a><span class="lineno"> 4514</span> {</div><div class="line"><a name="l04515"></a><span class="lineno"> 4515</span>  ParseComparison(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58caf5f286e73bda105e538310b3190f75c5">armnn::ComparisonOperation::Equal</a>);</div><div class="line"><a name="l04516"></a><span class="lineno"> 4516</span> }</div><div class="line"><a name="l04517"></a><span class="lineno"> 4517</span> </div><div class="line"><a name="l04518"></a><span class="lineno"> 4518</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseNotEqual(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04519"></a><span class="lineno"> 4519</span> {</div><div class="line"><a name="l04520"></a><span class="lineno"> 4520</span>  ParseComparison(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca19bb0af2c3c530538cb41aff7f235b96">armnn::ComparisonOperation::NotEqual</a>);</div><div class="line"><a name="l04521"></a><span class="lineno"> 4521</span> }</div><div class="line"><a name="l04522"></a><span class="lineno"> 4522</span> </div><div class="line"><a name="l04523"></a><span class="lineno"> 4523</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseGreater(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04524"></a><span class="lineno"> 4524</span> {</div><div class="line"><a name="l04525"></a><span class="lineno"> 4525</span>  ParseComparison(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca8768a6821cd735aea4f5b0df88c1fc6a">armnn::ComparisonOperation::Greater</a>);</div><div class="line"><a name="l04526"></a><span class="lineno"> 4526</span> }</div><div class="line"><a name="l04527"></a><span class="lineno"> 4527</span> </div><div class="line"><a name="l04528"></a><span class="lineno"> 4528</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseGreaterOrEqual(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04529"></a><span class="lineno"> 4529</span> {</div><div class="line"><a name="l04530"></a><span class="lineno"> 4530</span>  ParseComparison(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca6f2f0aefb3d22da0f3839453add5f937">armnn::ComparisonOperation::GreaterOrEqual</a>);</div><div class="line"><a name="l04531"></a><span class="lineno"> 4531</span> }</div><div class="line"><a name="l04532"></a><span class="lineno"> 4532</span> </div><div class="line"><a name="l04533"></a><span class="lineno"> 4533</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseLess(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04534"></a><span class="lineno"> 4534</span> {</div><div class="line"><a name="l04535"></a><span class="lineno"> 4535</span>  ParseComparison(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca1cfdf0e8d0c87a228c1f40d9bee7888b">armnn::ComparisonOperation::Less</a>);</div><div class="line"><a name="l04536"></a><span class="lineno"> 4536</span> }</div><div class="line"><a name="l04537"></a><span class="lineno"> 4537</span> </div><div class="line"><a name="l04538"></a><span class="lineno"> 4538</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseLessOrEqual(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04539"></a><span class="lineno"> 4539</span> {</div><div class="line"><a name="l04540"></a><span class="lineno"> 4540</span>  ParseComparison(subgraphIndex, operatorIndex, <a class="code" href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58caa4cbdbb6070a5abb35fc95ecf1e22c14">armnn::ComparisonOperation::LessOrEqual</a>);</div><div class="line"><a name="l04541"></a><span class="lineno"> 4541</span> }</div><div class="line"><a name="l04542"></a><span class="lineno"> 4542</span> </div><div class="line"><a name="l04543"></a><span class="lineno"> 4543</span> <span class="keywordtype">void</span> TfLiteParserImpl::ParseComparison(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l04544"></a><span class="lineno"> 4544</span>  <a class="code" href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58c">ComparisonOperation</a> comparisonOperation)</div><div class="line"><a name="l04545"></a><span class="lineno"> 4545</span> {</div><div class="line"><a name="l04546"></a><span class="lineno"> 4546</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04547"></a><span class="lineno"> 4547</span> </div><div class="line"><a name="l04548"></a><span class="lineno"> 4548</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04549"></a><span class="lineno"> 4549</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l04550"></a><span class="lineno"> 4550</span> </div><div class="line"><a name="l04551"></a><span class="lineno"> 4551</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04552"></a><span class="lineno"> 4552</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l04553"></a><span class="lineno"> 4553</span> </div><div class="line"><a name="l04554"></a><span class="lineno"> 4554</span>  <span class="keyword">auto</span> layerName = std::string(<a class="code" href="namespacearmnn.xhtml#aabb76a77e95921785f576bb29b495cd8">GetComparisonOperationAsCString</a>(comparisonOperation)) + <span class="stringliteral">":{}:{}"</span>;</div><div class="line"><a name="l04555"></a><span class="lineno"> 4555</span>  std::string layerNameFormatted = fmt::format(layerName, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04556"></a><span class="lineno"> 4556</span> </div><div class="line"><a name="l04557"></a><span class="lineno"> 4557</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);</div><div class="line"><a name="l04558"></a><span class="lineno"> 4558</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 1);</div><div class="line"><a name="l04559"></a><span class="lineno"> 4559</span>  CheckMatchingQuantization(inputTensorInfo, input1TensorInfo, layerNameFormatted, <span class="stringliteral">"Input 0"</span>, <span class="stringliteral">"Input 1"</span>);</div><div class="line"><a name="l04560"></a><span class="lineno"> 4560</span> </div><div class="line"><a name="l04561"></a><span class="lineno"> 4561</span>  <a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml">ComparisonDescriptor</a> desc;</div><div class="line"><a name="l04562"></a><span class="lineno"> 4562</span>  desc.<a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml#a865dc4f43cb0ff01a1dcf78036912fd1">m_Operation</a> = comparisonOperation;</div><div class="line"><a name="l04563"></a><span class="lineno"> 4563</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddComparisonLayer(desc, layerNameFormatted.c_str());</div><div class="line"><a name="l04564"></a><span class="lineno"> 4564</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l04565"></a><span class="lineno"> 4565</span> </div><div class="line"><a name="l04566"></a><span class="lineno"> 4566</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 1});</div><div class="line"><a name="l04567"></a><span class="lineno"> 4567</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l04568"></a><span class="lineno"> 4568</span> </div><div class="line"><a name="l04569"></a><span class="lineno"> 4569</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04570"></a><span class="lineno"> 4570</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l04571"></a><span class="lineno"> 4571</span> </div><div class="line"><a name="l04572"></a><span class="lineno"> 4572</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l04573"></a><span class="lineno"> 4573</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l04574"></a><span class="lineno"> 4574</span> }</div><div class="line"><a name="l04575"></a><span class="lineno"> 4575</span> </div><div class="line"><a name="l04576"></a><span class="lineno"> 4576</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* TfLiteParserImpl::AddReshapeLayer(<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l04577"></a><span class="lineno"> 4577</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSlot,</div><div class="line"><a name="l04578"></a><span class="lineno"> 4578</span>  std::string reshapeLayerName,</div><div class="line"><a name="l04579"></a><span class="lineno"> 4579</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputShape)</div><div class="line"><a name="l04580"></a><span class="lineno"> 4580</span> {</div><div class="line"><a name="l04581"></a><span class="lineno"> 4581</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">ReshapeDescriptor</a> desc;</div><div class="line"><a name="l04582"></a><span class="lineno"> 4582</span>  desc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = outputShape.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l04583"></a><span class="lineno"> 4583</span> </div><div class="line"><a name="l04584"></a><span class="lineno"> 4584</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* reshapeLayer =</div><div class="line"><a name="l04585"></a><span class="lineno"> 4585</span>  m_Network->AddReshapeLayer(desc, reshapeLayerName.c_str());</div><div class="line"><a name="l04586"></a><span class="lineno"> 4586</span> </div><div class="line"><a name="l04587"></a><span class="lineno"> 4587</span>  <span class="keyword">auto</span> & prevOutputSlot = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(outputSlot);</div><div class="line"><a name="l04588"></a><span class="lineno"> 4588</span>  prevOutputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(reshapeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l04589"></a><span class="lineno"> 4589</span>  reshapeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputShape);</div><div class="line"><a name="l04590"></a><span class="lineno"> 4590</span>  <span class="keywordflow">return</span> reshapeLayer;</div><div class="line"><a name="l04591"></a><span class="lineno"> 4591</span> }</div><div class="line"><a name="l04592"></a><span class="lineno"> 4592</span> </div><div class="line"><a name="l04593"></a><span class="lineno"> 4593</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* TfLiteParserImpl::AddFusedActivationLayer(<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* prevLayer,</div><div class="line"><a name="l04594"></a><span class="lineno"> 4594</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSlot,</div><div class="line"><a name="l04595"></a><span class="lineno"> 4595</span>  tflite::ActivationFunctionType activationType)</div><div class="line"><a name="l04596"></a><span class="lineno"> 4596</span> {</div><div class="line"><a name="l04597"></a><span class="lineno"> 4597</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDesc;</div><div class="line"><a name="l04598"></a><span class="lineno"> 4598</span>  std::string layerName = prevLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>();</div><div class="line"><a name="l04599"></a><span class="lineno"> 4599</span> </div><div class="line"><a name="l04600"></a><span class="lineno"> 4600</span>  <span class="keywordflow">switch</span>(activationType)</div><div class="line"><a name="l04601"></a><span class="lineno"> 4601</span>  {</div><div class="line"><a name="l04602"></a><span class="lineno"> 4602</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_NONE:</div><div class="line"><a name="l04603"></a><span class="lineno"> 4603</span>  {</div><div class="line"><a name="l04604"></a><span class="lineno"> 4604</span>  <span class="comment">// this is a no-op: return previous layer</span></div><div class="line"><a name="l04605"></a><span class="lineno"> 4605</span>  <span class="keywordflow">return</span> prevLayer;</div><div class="line"><a name="l04606"></a><span class="lineno"> 4606</span>  }</div><div class="line"><a name="l04607"></a><span class="lineno"> 4607</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_RELU:</div><div class="line"><a name="l04608"></a><span class="lineno"> 4608</span>  {</div><div class="line"><a name="l04609"></a><span class="lineno"> 4609</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::ReLu;</div><div class="line"><a name="l04610"></a><span class="lineno"> 4610</span>  layerName += <span class="stringliteral">":RELU"</span>;</div><div class="line"><a name="l04611"></a><span class="lineno"> 4611</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l04612"></a><span class="lineno"> 4612</span>  }</div><div class="line"><a name="l04613"></a><span class="lineno"> 4613</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_RELU6:</div><div class="line"><a name="l04614"></a><span class="lineno"> 4614</span>  {</div><div class="line"><a name="l04615"></a><span class="lineno"> 4615</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::BoundedReLu;</div><div class="line"><a name="l04616"></a><span class="lineno"> 4616</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 6.0f;</div><div class="line"><a name="l04617"></a><span class="lineno"> 4617</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = 0.0f;</div><div class="line"><a name="l04618"></a><span class="lineno"> 4618</span>  layerName += <span class="stringliteral">":RELU6"</span>;</div><div class="line"><a name="l04619"></a><span class="lineno"> 4619</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l04620"></a><span class="lineno"> 4620</span>  }</div><div class="line"><a name="l04621"></a><span class="lineno"> 4621</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_TANH:</div><div class="line"><a name="l04622"></a><span class="lineno"> 4622</span>  {</div><div class="line"><a name="l04623"></a><span class="lineno"> 4623</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::TanH;</div><div class="line"><a name="l04624"></a><span class="lineno"> 4624</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l04625"></a><span class="lineno"> 4625</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = 1.0f;</div><div class="line"><a name="l04626"></a><span class="lineno"> 4626</span>  layerName += <span class="stringliteral">":TANH"</span>;</div><div class="line"><a name="l04627"></a><span class="lineno"> 4627</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l04628"></a><span class="lineno"> 4628</span>  }</div><div class="line"><a name="l04629"></a><span class="lineno"> 4629</span> </div><div class="line"><a name="l04630"></a><span class="lineno"> 4630</span>  <span class="comment">// I only put these here as a reminder what others we could support</span></div><div class="line"><a name="l04631"></a><span class="lineno"> 4631</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_RELU_N1_TO_1:</div><div class="line"><a name="l04632"></a><span class="lineno"> 4632</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_SIGN_BIT:</div><div class="line"><a name="l04633"></a><span class="lineno"> 4633</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l04634"></a><span class="lineno"> 4634</span>  {</div><div class="line"><a name="l04635"></a><span class="lineno"> 4635</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l04636"></a><span class="lineno"> 4636</span>  fmt::format(<span class="stringliteral">"TfLite parser doesn't support fused activation: "</span></div><div class="line"><a name="l04637"></a><span class="lineno"> 4637</span>  <span class="stringliteral">"{}/{} {} "</span>,</div><div class="line"><a name="l04638"></a><span class="lineno"> 4638</span>  activationType,</div><div class="line"><a name="l04639"></a><span class="lineno"> 4639</span>  tflite::EnumNameActivationFunctionType(activationType),</div><div class="line"><a name="l04640"></a><span class="lineno"> 4640</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l04641"></a><span class="lineno"> 4641</span> </div><div class="line"><a name="l04642"></a><span class="lineno"> 4642</span>  }</div><div class="line"><a name="l04643"></a><span class="lineno"> 4643</span>  }</div><div class="line"><a name="l04644"></a><span class="lineno"> 4644</span> </div><div class="line"><a name="l04645"></a><span class="lineno"> 4645</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activationLayer =</div><div class="line"><a name="l04646"></a><span class="lineno"> 4646</span>  m_Network->AddActivationLayer(activationDesc, layerName.c_str());</div><div class="line"><a name="l04647"></a><span class="lineno"> 4647</span> </div><div class="line"><a name="l04648"></a><span class="lineno"> 4648</span>  <span class="keyword">auto</span> & prevOutputSlot = prevLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(outputSlot);</div><div class="line"><a name="l04649"></a><span class="lineno"> 4649</span>  prevOutputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(activationLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l04650"></a><span class="lineno"> 4650</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>(prevOutputSlot.GetTensorInfo());</div><div class="line"><a name="l04651"></a><span class="lineno"> 4651</span>  <span class="keywordflow">return</span> activationLayer;</div><div class="line"><a name="l04652"></a><span class="lineno"> 4652</span> }</div><div class="line"><a name="l04653"></a><span class="lineno"> 4653</span> </div><div class="line"><a name="l04654"></a><span class="lineno"> 4654</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* TfLiteParserImpl::AddFusedFloorLayer(<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* prevLayer,</div><div class="line"><a name="l04655"></a><span class="lineno"> 4655</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSlot)</div><div class="line"><a name="l04656"></a><span class="lineno"> 4656</span> {</div><div class="line"><a name="l04657"></a><span class="lineno"> 4657</span> </div><div class="line"><a name="l04658"></a><span class="lineno"> 4658</span>  <span class="keyword">auto</span>& prevOutputSlot = prevLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(outputSlot);</div><div class="line"><a name="l04659"></a><span class="lineno"> 4659</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dataType = prevOutputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l04660"></a><span class="lineno"> 4660</span> </div><div class="line"><a name="l04661"></a><span class="lineno"> 4661</span>  <span class="keywordflow">if</span> (dataType == DataType::Signed32)</div><div class="line"><a name="l04662"></a><span class="lineno"> 4662</span>  {</div><div class="line"><a name="l04663"></a><span class="lineno"> 4663</span>  <span class="keywordflow">return</span> prevLayer;</div><div class="line"><a name="l04664"></a><span class="lineno"> 4664</span>  }</div><div class="line"><a name="l04665"></a><span class="lineno"> 4665</span> </div><div class="line"><a name="l04666"></a><span class="lineno"> 4666</span>  std::string layerName = prevLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>();</div><div class="line"><a name="l04667"></a><span class="lineno"> 4667</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* floorLayer = m_Network->AddFloorLayer(layerName.c_str());</div><div class="line"><a name="l04668"></a><span class="lineno"> 4668</span> </div><div class="line"><a name="l04669"></a><span class="lineno"> 4669</span>  prevOutputSlot.Connect(floorLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l04670"></a><span class="lineno"> 4670</span>  floorLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(prevOutputSlot.GetTensorInfo());</div><div class="line"><a name="l04671"></a><span class="lineno"> 4671</span> </div><div class="line"><a name="l04672"></a><span class="lineno"> 4672</span>  <span class="keywordflow">return</span> floorLayer;</div><div class="line"><a name="l04673"></a><span class="lineno"> 4673</span> }</div><div class="line"><a name="l04674"></a><span class="lineno"> 4674</span> </div><div class="line"><a name="l04675"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#af9590c2ae8d68b5da999d3d6ab21dd3b"> 4675</a></span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abfc86471394295357a23b3addd0b5b1c">TfLiteParserImpl::ModelPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#af9590c2ae8d68b5da999d3d6ab21dd3b">TfLiteParserImpl::LoadModelFromFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* fileName)</div><div class="line"><a name="l04676"></a><span class="lineno"> 4676</span> {</div><div class="line"><a name="l04677"></a><span class="lineno"> 4677</span>  <span class="keywordflow">if</span> (fileName == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l04678"></a><span class="lineno"> 4678</span>  {</div><div class="line"><a name="l04679"></a><span class="lineno"> 4679</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">"Invalid (null) file name {}"</span>,</div><div class="line"><a name="l04680"></a><span class="lineno"> 4680</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l04681"></a><span class="lineno"> 4681</span>  }</div><div class="line"><a name="l04682"></a><span class="lineno"> 4682</span>  std::error_code errorCode;</div><div class="line"><a name="l04683"></a><span class="lineno"> 4683</span>  fs::path pathToFile(fileName);</div><div class="line"><a name="l04684"></a><span class="lineno"> 4684</span>  <span class="keywordflow">if</span> (!fs::exists(pathToFile, errorCode))</div><div class="line"><a name="l04685"></a><span class="lineno"> 4685</span>  {</div><div class="line"><a name="l04686"></a><span class="lineno"> 4686</span>  <span class="comment">//fmt::format() could not be used here (format error)</span></div><div class="line"><a name="l04687"></a><span class="lineno"> 4687</span>  std::stringstream msg;</div><div class="line"><a name="l04688"></a><span class="lineno"> 4688</span>  msg << <span class="stringliteral">"Cannot find the file ("</span> << fileName << <span class="stringliteral">") errorCode: "</span> << errorCode</div><div class="line"><a name="l04689"></a><span class="lineno"> 4689</span>  << <span class="stringliteral">" "</span> << <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString();</div><div class="line"><a name="l04690"></a><span class="lineno"> 4690</span> </div><div class="line"><a name="l04691"></a><span class="lineno"> 4691</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_file_not_found_exception.xhtml">FileNotFoundException</a>(msg.str());</div><div class="line"><a name="l04692"></a><span class="lineno"> 4692</span>  }</div><div class="line"><a name="l04693"></a><span class="lineno"> 4693</span>  std::ifstream file(fileName, std::ios::binary);</div><div class="line"><a name="l04694"></a><span class="lineno"> 4694</span>  std::string fileContent((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());</div><div class="line"><a name="l04695"></a><span class="lineno"> 4695</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a7f3fc56b7306b444f06f45cdc4032bbf">LoadModelFromBinary</a>(reinterpret_cast<const uint8_t *>(fileContent.c_str()),</div><div class="line"><a name="l04696"></a><span class="lineno"> 4696</span>  fileContent.size());</div><div class="line"><a name="l04697"></a><span class="lineno"> 4697</span> }</div><div class="line"><a name="l04698"></a><span class="lineno"> 4698</span> </div><div class="line"><a name="l04699"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a7f3fc56b7306b444f06f45cdc4032bbf"> 4699</a></span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abfc86471394295357a23b3addd0b5b1c">TfLiteParserImpl::ModelPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a7f3fc56b7306b444f06f45cdc4032bbf">TfLiteParserImpl::LoadModelFromBinary</a>(<span class="keyword">const</span> uint8_t* binaryContent, <span class="keywordtype">size_t</span> len)</div><div class="line"><a name="l04700"></a><span class="lineno"> 4700</span> {</div><div class="line"><a name="l04701"></a><span class="lineno"> 4701</span>  <span class="keywordflow">if</span> (binaryContent == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l04702"></a><span class="lineno"> 4702</span>  {</div><div class="line"><a name="l04703"></a><span class="lineno"> 4703</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">"Invalid (null) binary content {}"</span>,</div><div class="line"><a name="l04704"></a><span class="lineno"> 4704</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l04705"></a><span class="lineno"> 4705</span>  }</div><div class="line"><a name="l04706"></a><span class="lineno"> 4706</span>  flatbuffers::Verifier verifier(binaryContent, len);</div><div class="line"><a name="l04707"></a><span class="lineno"> 4707</span>  <span class="keywordflow">if</span> (verifier.VerifyBuffer<<a class="code" href="namespacearmnn__driver.xhtml#a923b39ec5f83672f50289cbaf5de6798">tflite::Model</a>>() == <span class="keyword">false</span>)</div><div class="line"><a name="l04708"></a><span class="lineno"> 4708</span>  {</div><div class="line"><a name="l04709"></a><span class="lineno"> 4709</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l04710"></a><span class="lineno"> 4710</span>  fmt::format(<span class="stringliteral">"Buffer doesn't conform to the expected Tensorflow Lite "</span></div><div class="line"><a name="l04711"></a><span class="lineno"> 4711</span>  <span class="stringliteral">"flatbuffers format. size:{} {}"</span>,</div><div class="line"><a name="l04712"></a><span class="lineno"> 4712</span>  len,</div><div class="line"><a name="l04713"></a><span class="lineno"> 4713</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l04714"></a><span class="lineno"> 4714</span>  }</div><div class="line"><a name="l04715"></a><span class="lineno"> 4715</span>  <span class="keywordflow">return</span> tflite::UnPackModel(binaryContent);</div><div class="line"><a name="l04716"></a><span class="lineno"> 4716</span> }</div><div class="line"><a name="l04717"></a><span class="lineno"> 4717</span> </div><div class="line"><a name="l04718"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb"> 4718</a></span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParserImpl::TensorRawPtrVector</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">TfLiteParserImpl::GetInputs</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a>& model,</div><div class="line"><a name="l04719"></a><span class="lineno"> 4719</span>  <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l04720"></a><span class="lineno"> 4720</span>  <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04721"></a><span class="lineno"> 4721</span> {</div><div class="line"><a name="l04722"></a><span class="lineno"> 4722</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04723"></a><span class="lineno"> 4723</span> </div><div class="line"><a name="l04724"></a><span class="lineno"> 4724</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& subgraphPtr = model->subgraphs[subgraphIndex];</div><div class="line"><a name="l04725"></a><span class="lineno"> 4725</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = subgraphPtr->operators[operatorIndex];</div><div class="line"><a name="l04726"></a><span class="lineno"> 4726</span> </div><div class="line"><a name="l04727"></a><span class="lineno"> 4727</span>  <span class="keywordtype">size_t</span> inputCount = operatorPtr->inputs.size();</div><div class="line"><a name="l04728"></a><span class="lineno"> 4728</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TensorRawPtrVector</a> result;</div><div class="line"><a name="l04729"></a><span class="lineno"> 4729</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < inputCount; ++i)</div><div class="line"><a name="l04730"></a><span class="lineno"> 4730</span>  {</div><div class="line"><a name="l04731"></a><span class="lineno"> 4731</span>  <span class="comment">// If the input location is -1 then assume input is turned off.</span></div><div class="line"><a name="l04732"></a><span class="lineno"> 4732</span>  <span class="keywordflow">if</span> (operatorPtr->inputs[i] == -1)</div><div class="line"><a name="l04733"></a><span class="lineno"> 4733</span>  {</div><div class="line"><a name="l04734"></a><span class="lineno"> 4734</span>  <span class="keywordflow">continue</span>;</div><div class="line"><a name="l04735"></a><span class="lineno"> 4735</span>  }</div><div class="line"><a name="l04736"></a><span class="lineno"> 4736</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l04737"></a><span class="lineno"> 4737</span>  {</div><div class="line"><a name="l04738"></a><span class="lineno"> 4738</span>  uint32_t inputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(operatorPtr->inputs[i]);</div><div class="line"><a name="l04739"></a><span class="lineno"> 4739</span>  result.push_back(subgraphPtr->tensors[inputId].get());</div><div class="line"><a name="l04740"></a><span class="lineno"> 4740</span>  }</div><div class="line"><a name="l04741"></a><span class="lineno"> 4741</span>  }</div><div class="line"><a name="l04742"></a><span class="lineno"> 4742</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l04743"></a><span class="lineno"> 4743</span> }</div><div class="line"><a name="l04744"></a><span class="lineno"> 4744</span> </div><div class="line"><a name="l04745"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488"> 4745</a></span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParserImpl::TensorRawPtrVector</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">TfLiteParserImpl::GetOutputs</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a>& model,</div><div class="line"><a name="l04746"></a><span class="lineno"> 4746</span>  <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l04747"></a><span class="lineno"> 4747</span>  <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04748"></a><span class="lineno"> 4748</span> {</div><div class="line"><a name="l04749"></a><span class="lineno"> 4749</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04750"></a><span class="lineno"> 4750</span> </div><div class="line"><a name="l04751"></a><span class="lineno"> 4751</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& subgraphPtr = model->subgraphs[subgraphIndex];</div><div class="line"><a name="l04752"></a><span class="lineno"> 4752</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = subgraphPtr->operators[operatorIndex];</div><div class="line"><a name="l04753"></a><span class="lineno"> 4753</span> </div><div class="line"><a name="l04754"></a><span class="lineno"> 4754</span>  <span class="keywordtype">size_t</span> outputCount = operatorPtr->outputs.size();</div><div class="line"><a name="l04755"></a><span class="lineno"> 4755</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TensorRawPtrVector</a> result(outputCount);</div><div class="line"><a name="l04756"></a><span class="lineno"> 4756</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < outputCount; ++i)</div><div class="line"><a name="l04757"></a><span class="lineno"> 4757</span>  {</div><div class="line"><a name="l04758"></a><span class="lineno"> 4758</span>  uint32_t outputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(operatorPtr->outputs[i]);</div><div class="line"><a name="l04759"></a><span class="lineno"> 4759</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#aa1664dc13adbc85ac12fb584b76bfdae">CHECK_TENSOR</a>(model, subgraphIndex, outputId);</div><div class="line"><a name="l04760"></a><span class="lineno"> 4760</span>  result[i] = subgraphPtr->tensors[outputId].get();</div><div class="line"><a name="l04761"></a><span class="lineno"> 4761</span>  }</div><div class="line"><a name="l04762"></a><span class="lineno"> 4762</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l04763"></a><span class="lineno"> 4763</span> }</div><div class="line"><a name="l04764"></a><span class="lineno"> 4764</span> </div><div class="line"><a name="l04765"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a20518e431cb501b172371b850e6c3ec7"> 4765</a></span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a86428e0c674542488c7292dfbe2ce381">TfLiteParserImpl::TensorIdRawPtrVector</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a20518e431cb501b172371b850e6c3ec7">TfLiteParserImpl::GetSubgraphInputs</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a>& model,</div><div class="line"><a name="l04766"></a><span class="lineno"> 4766</span>  <span class="keywordtype">size_t</span> subgraphIndex)</div><div class="line"><a name="l04767"></a><span class="lineno"> 4767</span> {</div><div class="line"><a name="l04768"></a><span class="lineno"> 4768</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(model, subgraphIndex);</div><div class="line"><a name="l04769"></a><span class="lineno"> 4769</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& subgraphPtr = model->subgraphs[subgraphIndex];</div><div class="line"><a name="l04770"></a><span class="lineno"> 4770</span> </div><div class="line"><a name="l04771"></a><span class="lineno"> 4771</span>  <span class="keywordtype">size_t</span> inputCount = subgraphPtr->inputs.size();</div><div class="line"><a name="l04772"></a><span class="lineno"> 4772</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a86428e0c674542488c7292dfbe2ce381">TensorIdRawPtrVector</a> result(inputCount);</div><div class="line"><a name="l04773"></a><span class="lineno"> 4773</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < inputCount; ++i)</div><div class="line"><a name="l04774"></a><span class="lineno"> 4774</span>  {</div><div class="line"><a name="l04775"></a><span class="lineno"> 4775</span>  uint32_t inputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(subgraphPtr->inputs[i]);</div><div class="line"><a name="l04776"></a><span class="lineno"> 4776</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#aa1664dc13adbc85ac12fb584b76bfdae">CHECK_TENSOR</a>(model, subgraphIndex, inputId);</div><div class="line"><a name="l04777"></a><span class="lineno"> 4777</span>  result[i] = std::make_pair(inputId, subgraphPtr->tensors[inputId].get());</div><div class="line"><a name="l04778"></a><span class="lineno"> 4778</span>  }</div><div class="line"><a name="l04779"></a><span class="lineno"> 4779</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l04780"></a><span class="lineno"> 4780</span> }</div><div class="line"><a name="l04781"></a><span class="lineno"> 4781</span> </div><div class="line"><a name="l04782"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a1bd7b663eb19e2355b6aa5c36232d6fc"> 4782</a></span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a86428e0c674542488c7292dfbe2ce381">TfLiteParserImpl::TensorIdRawPtrVector</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a1bd7b663eb19e2355b6aa5c36232d6fc">TfLiteParserImpl::GetSubgraphOutputs</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a>& model,</div><div class="line"><a name="l04783"></a><span class="lineno"> 4783</span>  <span class="keywordtype">size_t</span> subgraphIndex)</div><div class="line"><a name="l04784"></a><span class="lineno"> 4784</span> {</div><div class="line"><a name="l04785"></a><span class="lineno"> 4785</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(model, subgraphIndex);</div><div class="line"><a name="l04786"></a><span class="lineno"> 4786</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& subgraphPtr = model->subgraphs[subgraphIndex];</div><div class="line"><a name="l04787"></a><span class="lineno"> 4787</span> </div><div class="line"><a name="l04788"></a><span class="lineno"> 4788</span>  <span class="keywordtype">size_t</span> outputCount = subgraphPtr->outputs.size();</div><div class="line"><a name="l04789"></a><span class="lineno"> 4789</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a86428e0c674542488c7292dfbe2ce381">TensorIdRawPtrVector</a> result(outputCount);</div><div class="line"><a name="l04790"></a><span class="lineno"> 4790</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < outputCount; ++i)</div><div class="line"><a name="l04791"></a><span class="lineno"> 4791</span>  {</div><div class="line"><a name="l04792"></a><span class="lineno"> 4792</span>  uint32_t outputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(subgraphPtr->outputs[i]);</div><div class="line"><a name="l04793"></a><span class="lineno"> 4793</span>  result[i] = std::make_pair(outputId, subgraphPtr->tensors[outputId].get());</div><div class="line"><a name="l04794"></a><span class="lineno"> 4794</span>  }</div><div class="line"><a name="l04795"></a><span class="lineno"> 4795</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l04796"></a><span class="lineno"> 4796</span> }</div><div class="line"><a name="l04797"></a><span class="lineno"> 4797</span> </div><div class="line"><a name="l04798"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575"> 4798</a></span> std::vector<int32_t>& <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">TfLiteParserImpl::GetInputTensorIds</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a>& model,</div><div class="line"><a name="l04799"></a><span class="lineno"> 4799</span>  <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l04800"></a><span class="lineno"> 4800</span>  <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04801"></a><span class="lineno"> 4801</span> {</div><div class="line"><a name="l04802"></a><span class="lineno"> 4802</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04803"></a><span class="lineno"> 4803</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& subgraphPtr = model->subgraphs[subgraphIndex];</div><div class="line"><a name="l04804"></a><span class="lineno"> 4804</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = subgraphPtr->operators[operatorIndex];</div><div class="line"><a name="l04805"></a><span class="lineno"> 4805</span>  <span class="keywordflow">return</span> operatorPtr->inputs;</div><div class="line"><a name="l04806"></a><span class="lineno"> 4806</span> }</div><div class="line"><a name="l04807"></a><span class="lineno"> 4807</span> </div><div class="line"><a name="l04808"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f"> 4808</a></span> std::vector<int32_t>& <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">TfLiteParserImpl::GetOutputTensorIds</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a>& model,</div><div class="line"><a name="l04809"></a><span class="lineno"> 4809</span>  <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l04810"></a><span class="lineno"> 4810</span>  <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l04811"></a><span class="lineno"> 4811</span> {</div><div class="line"><a name="l04812"></a><span class="lineno"> 4812</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04813"></a><span class="lineno"> 4813</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& subgraphPtr = model->subgraphs[subgraphIndex];</div><div class="line"><a name="l04814"></a><span class="lineno"> 4814</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = subgraphPtr->operators[operatorIndex];</div><div class="line"><a name="l04815"></a><span class="lineno"> 4815</span>  <span class="keywordflow">return</span> operatorPtr->outputs;</div><div class="line"><a name="l04816"></a><span class="lineno"> 4816</span> }</div><div class="line"><a name="l04817"></a><span class="lineno"> 4817</span> </div><div class="line"><a name="l04818"></a><span class="lineno"> 4818</span> <span class="keywordtype">void</span> TfLiteParserImpl::RegisterInputSlots(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l04819"></a><span class="lineno"> 4819</span>  <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l04820"></a><span class="lineno"> 4820</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer,</div><div class="line"><a name="l04821"></a><span class="lineno"> 4821</span>  <span class="keyword">const</span> std::vector<unsigned int>& tensorIndexes,</div><div class="line"><a name="l04822"></a><span class="lineno"> 4822</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> startingSlotIndex)</div><div class="line"><a name="l04823"></a><span class="lineno"> 4823</span> {</div><div class="line"><a name="l04824"></a><span class="lineno"> 4824</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04825"></a><span class="lineno"> 4825</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l04826"></a><span class="lineno"> 4826</span> </div><div class="line"><a name="l04827"></a><span class="lineno"> 4827</span>  <span class="keywordflow">if</span> (tensorIndexes.size() + startingSlotIndex != layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>())</div><div class="line"><a name="l04828"></a><span class="lineno"> 4828</span>  {</div><div class="line"><a name="l04829"></a><span class="lineno"> 4829</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l04830"></a><span class="lineno"> 4830</span>  fmt::format(<span class="stringliteral">"The number of tensor inputs ({}) does not match the number expected ({})"</span></div><div class="line"><a name="l04831"></a><span class="lineno"> 4831</span>  <span class="stringliteral">" for subgraph:{} operator index:{} {}"</span>,</div><div class="line"><a name="l04832"></a><span class="lineno"> 4832</span>  tensorIndexes.size(),</div><div class="line"><a name="l04833"></a><span class="lineno"> 4833</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>(),</div><div class="line"><a name="l04834"></a><span class="lineno"> 4834</span>  subgraphIndex,</div><div class="line"><a name="l04835"></a><span class="lineno"> 4835</span>  operatorIndex,</div><div class="line"><a name="l04836"></a><span class="lineno"> 4836</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l04837"></a><span class="lineno"> 4837</span>  }</div><div class="line"><a name="l04838"></a><span class="lineno"> 4838</span> </div><div class="line"><a name="l04839"></a><span class="lineno"> 4839</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> index = 0; index < tensorIndexes.size() ; ++index)</div><div class="line"><a name="l04840"></a><span class="lineno"> 4840</span>  {</div><div class="line"><a name="l04841"></a><span class="lineno"> 4841</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tensorIndex = tensorIndexes[index];</div><div class="line"><a name="l04842"></a><span class="lineno"> 4842</span>  <a class="code" href="classarmnn_1_1_i_input_slot.xhtml">armnn::IInputSlot</a>* slot = &(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(startingSlotIndex + index));</div><div class="line"><a name="l04843"></a><span class="lineno"> 4843</span>  RegisterConsumerOfTensor(subgraphIndex, tensorIndex, slot);</div><div class="line"><a name="l04844"></a><span class="lineno"> 4844</span>  }</div><div class="line"><a name="l04845"></a><span class="lineno"> 4845</span> }</div><div class="line"><a name="l04846"></a><span class="lineno"> 4846</span> </div><div class="line"><a name="l04847"></a><span class="lineno"> 4847</span> <span class="keywordtype">void</span> TfLiteParserImpl::RegisterOutputSlots(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l04848"></a><span class="lineno"> 4848</span>  <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l04849"></a><span class="lineno"> 4849</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer,</div><div class="line"><a name="l04850"></a><span class="lineno"> 4850</span>  <span class="keyword">const</span> std::vector<unsigned int>& tensorIndexes)</div><div class="line"><a name="l04851"></a><span class="lineno"> 4851</span> {</div><div class="line"><a name="l04852"></a><span class="lineno"> 4852</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l04853"></a><span class="lineno"> 4853</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l04854"></a><span class="lineno"> 4854</span>  <span class="keywordflow">if</span> (tensorIndexes.size() != layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>())</div><div class="line"><a name="l04855"></a><span class="lineno"> 4855</span>  {</div><div class="line"><a name="l04856"></a><span class="lineno"> 4856</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l04857"></a><span class="lineno"> 4857</span>  fmt::format(<span class="stringliteral">"The number of tensor outputs ({}) does not match the number expected ({})"</span></div><div class="line"><a name="l04858"></a><span class="lineno"> 4858</span>  <span class="stringliteral">" for subgraph:{} operator index:{} {}"</span>,</div><div class="line"><a name="l04859"></a><span class="lineno"> 4859</span>  tensorIndexes.size(),</div><div class="line"><a name="l04860"></a><span class="lineno"> 4860</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(),</div><div class="line"><a name="l04861"></a><span class="lineno"> 4861</span>  subgraphIndex,</div><div class="line"><a name="l04862"></a><span class="lineno"> 4862</span>  operatorIndex,</div><div class="line"><a name="l04863"></a><span class="lineno"> 4863</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l04864"></a><span class="lineno"> 4864</span>  }</div><div class="line"><a name="l04865"></a><span class="lineno"> 4865</span> </div><div class="line"><a name="l04866"></a><span class="lineno"> 4866</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> slotIndex = 0; slotIndex < layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(); ++slotIndex)</div><div class="line"><a name="l04867"></a><span class="lineno"> 4867</span>  {</div><div class="line"><a name="l04868"></a><span class="lineno"> 4868</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tensorIndex = tensorIndexes[slotIndex];</div><div class="line"><a name="l04869"></a><span class="lineno"> 4869</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>* slot = &(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(slotIndex));</div><div class="line"><a name="l04870"></a><span class="lineno"> 4870</span>  RegisterProducerOfTensor(subgraphIndex, tensorIndex, slot);</div><div class="line"><a name="l04871"></a><span class="lineno"> 4871</span>  }</div><div class="line"><a name="l04872"></a><span class="lineno"> 4872</span> }</div><div class="line"><a name="l04873"></a><span class="lineno"> 4873</span> </div><div class="line"><a name="l04874"></a><span class="lineno"> 4874</span> <span class="keywordtype">void</span> TfLiteParserImpl::SetupInputLayerTensorInfos(<span class="keywordtype">size_t</span> subgraphIndex)</div><div class="line"><a name="l04875"></a><span class="lineno"> 4875</span> {</div><div class="line"><a name="l04876"></a><span class="lineno"> 4876</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphIndex);</div><div class="line"><a name="l04877"></a><span class="lineno"> 4877</span> </div><div class="line"><a name="l04878"></a><span class="lineno"> 4878</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a20518e431cb501b172371b850e6c3ec7">GetSubgraphInputs</a>(m_Model, subgraphIndex);</div><div class="line"><a name="l04879"></a><span class="lineno"> 4879</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span>& tensorIdAndPtr : inputs)</div><div class="line"><a name="l04880"></a><span class="lineno"> 4880</span>  {</div><div class="line"><a name="l04881"></a><span class="lineno"> 4881</span>  <span class="keyword">auto</span> tensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(tensorIdAndPtr.second);</div><div class="line"><a name="l04882"></a><span class="lineno"> 4882</span>  m_TensorInfos.insert({tensorIdAndPtr.first, tensorInfo});</div><div class="line"><a name="l04883"></a><span class="lineno"> 4883</span>  }</div><div class="line"><a name="l04884"></a><span class="lineno"> 4884</span> }</div><div class="line"><a name="l04885"></a><span class="lineno"> 4885</span> </div><div class="line"><a name="l04886"></a><span class="lineno"> 4886</span> <span class="keywordtype">void</span> TfLiteParserImpl::SetupInputLayers(<span class="keywordtype">size_t</span> subgraphIndex)</div><div class="line"><a name="l04887"></a><span class="lineno"> 4887</span> {</div><div class="line"><a name="l04888"></a><span class="lineno"> 4888</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphIndex);</div><div class="line"><a name="l04889"></a><span class="lineno"> 4889</span> </div><div class="line"><a name="l04890"></a><span class="lineno"> 4890</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a20518e431cb501b172371b850e6c3ec7">GetSubgraphInputs</a>(m_Model, subgraphIndex);</div><div class="line"><a name="l04891"></a><span class="lineno"> 4891</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span>& tensorIdAndPtr : inputs)</div><div class="line"><a name="l04892"></a><span class="lineno"> 4892</span>  {</div><div class="line"><a name="l04893"></a><span class="lineno"> 4893</span>  <span class="keyword">auto</span> bindingId = GenerateLayerBindingId(subgraphIndex, tensorIdAndPtr.first);</div><div class="line"><a name="l04894"></a><span class="lineno"> 4894</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer =</div><div class="line"><a name="l04895"></a><span class="lineno"> 4895</span>  m_Network->AddInputLayer(bindingId, tensorIdAndPtr.second->name.c_str());</div><div class="line"><a name="l04896"></a><span class="lineno"> 4896</span> </div><div class="line"><a name="l04897"></a><span class="lineno"> 4897</span>  <span class="keyword">auto</span> tensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(tensorIdAndPtr.second);</div><div class="line"><a name="l04898"></a><span class="lineno"> 4898</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l04899"></a><span class="lineno"> 4899</span> </div><div class="line"><a name="l04900"></a><span class="lineno"> 4900</span>  RegisterOutputSlots(subgraphIndex,</div><div class="line"><a name="l04901"></a><span class="lineno"> 4901</span>  VIRTUAL_OPERATOR_ID,</div><div class="line"><a name="l04902"></a><span class="lineno"> 4902</span>  layer,</div><div class="line"><a name="l04903"></a><span class="lineno"> 4903</span>  { <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(tensorIdAndPtr.first) });</div><div class="line"><a name="l04904"></a><span class="lineno"> 4904</span>  }</div><div class="line"><a name="l04905"></a><span class="lineno"> 4905</span> }</div><div class="line"><a name="l04906"></a><span class="lineno"> 4906</span> </div><div class="line"><a name="l04907"></a><span class="lineno"> 4907</span> <span class="keywordtype">void</span> TfLiteParserImpl::SetupOutputLayers(<span class="keywordtype">size_t</span> subgraphIndex)</div><div class="line"><a name="l04908"></a><span class="lineno"> 4908</span> {</div><div class="line"><a name="l04909"></a><span class="lineno"> 4909</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphIndex);</div><div class="line"><a name="l04910"></a><span class="lineno"> 4910</span> </div><div class="line"><a name="l04911"></a><span class="lineno"> 4911</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a1bd7b663eb19e2355b6aa5c36232d6fc">GetSubgraphOutputs</a>(m_Model, subgraphIndex);</div><div class="line"><a name="l04912"></a><span class="lineno"> 4912</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span>& tensorIdAndPtr : outputs)</div><div class="line"><a name="l04913"></a><span class="lineno"> 4913</span>  {</div><div class="line"><a name="l04914"></a><span class="lineno"> 4914</span>  <span class="keyword">auto</span> bindingId = GenerateLayerBindingId(subgraphIndex, tensorIdAndPtr.first);</div><div class="line"><a name="l04915"></a><span class="lineno"> 4915</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer =</div><div class="line"><a name="l04916"></a><span class="lineno"> 4916</span>  m_Network->AddOutputLayer(bindingId, tensorIdAndPtr.second->name.c_str());</div><div class="line"><a name="l04917"></a><span class="lineno"> 4917</span> </div><div class="line"><a name="l04918"></a><span class="lineno"> 4918</span>  RegisterInputSlots(subgraphIndex,</div><div class="line"><a name="l04919"></a><span class="lineno"> 4919</span>  VIRTUAL_OPERATOR_ID,</div><div class="line"><a name="l04920"></a><span class="lineno"> 4920</span>  layer,</div><div class="line"><a name="l04921"></a><span class="lineno"> 4921</span>  { <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(tensorIdAndPtr.first) });</div><div class="line"><a name="l04922"></a><span class="lineno"> 4922</span>  }</div><div class="line"><a name="l04923"></a><span class="lineno"> 4923</span> }</div><div class="line"><a name="l04924"></a><span class="lineno"> 4924</span> </div><div class="line"><a name="l04925"></a><span class="lineno"> 4925</span> <span class="keywordtype">void</span> TfLiteParserImpl::SetupConstantLayerTensorInfos(<span class="keywordtype">size_t</span> subgraph)</div><div class="line"><a name="l04926"></a><span class="lineno"> 4926</span> {</div><div class="line"><a name="l04927"></a><span class="lineno"> 4927</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraph);</div><div class="line"><a name="l04928"></a><span class="lineno"> 4928</span> </div><div class="line"><a name="l04929"></a><span class="lineno"> 4929</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & subgraphPtr = m_Model->subgraphs[subgraph];</div><div class="line"><a name="l04930"></a><span class="lineno"> 4930</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> subgraphIndex = 0; subgraphIndex < m_SubgraphConnections.size(); ++subgraphIndex)</div><div class="line"><a name="l04931"></a><span class="lineno"> 4931</span>  {</div><div class="line"><a name="l04932"></a><span class="lineno"> 4932</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tensorIndex = 0; tensorIndex < m_SubgraphConnections[subgraphIndex].size(); ++tensorIndex)</div><div class="line"><a name="l04933"></a><span class="lineno"> 4933</span>  {</div><div class="line"><a name="l04934"></a><span class="lineno"> 4934</span>  <span class="keywordflow">if</span> (m_SubgraphConnections[subgraphIndex][tensorIndex].outputSlot == <span class="keyword">nullptr</span> &&</div><div class="line"><a name="l04935"></a><span class="lineno"> 4935</span>  m_SubgraphConnections[subgraphIndex][tensorIndex].inputSlots.size() > 0)</div><div class="line"><a name="l04936"></a><span class="lineno"> 4936</span>  {</div><div class="line"><a name="l04937"></a><span class="lineno"> 4937</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TensorRawPtr</a> tensorPtr = subgraphPtr->tensors[tensorIndex].get();</div><div class="line"><a name="l04938"></a><span class="lineno"> 4938</span> </div><div class="line"><a name="l04939"></a><span class="lineno"> 4939</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(tensorPtr);</div><div class="line"><a name="l04940"></a><span class="lineno"> 4940</span> </div><div class="line"><a name="l04941"></a><span class="lineno"> 4941</span>  m_TensorInfos.insert({tensorIndex, tensorInfo});</div><div class="line"><a name="l04942"></a><span class="lineno"> 4942</span>  }</div><div class="line"><a name="l04943"></a><span class="lineno"> 4943</span>  }</div><div class="line"><a name="l04944"></a><span class="lineno"> 4944</span>  }</div><div class="line"><a name="l04945"></a><span class="lineno"> 4945</span> }</div><div class="line"><a name="l04946"></a><span class="lineno"> 4946</span> </div><div class="line"><a name="l04947"></a><span class="lineno"> 4947</span> <span class="keywordtype">void</span> TfLiteParserImpl::SetupConstantLayers(<span class="keywordtype">size_t</span> subgraph)</div><div class="line"><a name="l04948"></a><span class="lineno"> 4948</span> {</div><div class="line"><a name="l04949"></a><span class="lineno"> 4949</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraph);</div><div class="line"><a name="l04950"></a><span class="lineno"> 4950</span> </div><div class="line"><a name="l04951"></a><span class="lineno"> 4951</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & subgraphPtr = m_Model->subgraphs[subgraph];</div><div class="line"><a name="l04952"></a><span class="lineno"> 4952</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> subgraphIndex = 0; subgraphIndex < m_SubgraphConnections.size(); ++subgraphIndex)</div><div class="line"><a name="l04953"></a><span class="lineno"> 4953</span>  {</div><div class="line"><a name="l04954"></a><span class="lineno"> 4954</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tensorIndex = 0; tensorIndex < m_SubgraphConnections[subgraphIndex].size(); ++tensorIndex)</div><div class="line"><a name="l04955"></a><span class="lineno"> 4955</span>  {</div><div class="line"><a name="l04956"></a><span class="lineno"> 4956</span>  <span class="keywordflow">if</span> (m_SubgraphConnections[subgraphIndex][tensorIndex].outputSlot == <span class="keyword">nullptr</span> &&</div><div class="line"><a name="l04957"></a><span class="lineno"> 4957</span>  m_SubgraphConnections[subgraphIndex][tensorIndex].inputSlots.size() > 0)</div><div class="line"><a name="l04958"></a><span class="lineno"> 4958</span>  {</div><div class="line"><a name="l04959"></a><span class="lineno"> 4959</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TensorRawPtr</a> tensorPtr = subgraphPtr->tensors[tensorIndex].get();</div><div class="line"><a name="l04960"></a><span class="lineno"> 4960</span> </div><div class="line"><a name="l04961"></a><span class="lineno"> 4961</span>  <span class="keywordflow">if</span> (IsConstTensor(tensorPtr))</div><div class="line"><a name="l04962"></a><span class="lineno"> 4962</span>  {</div><div class="line"><a name="l04963"></a><span class="lineno"> 4963</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(tensorPtr);</div><div class="line"><a name="l04964"></a><span class="lineno"> 4964</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> dataType = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l04965"></a><span class="lineno"> 4965</span> </div><div class="line"><a name="l04966"></a><span class="lineno"> 4966</span>  <span class="keywordflow">if</span> (std::find(m_ConstantsToDequantize.begin(), m_ConstantsToDequantize.end(), tensorPtr->buffer)</div><div class="line"><a name="l04967"></a><span class="lineno"> 4967</span>  != m_ConstantsToDequantize.end())</div><div class="line"><a name="l04968"></a><span class="lineno"> 4968</span>  {</div><div class="line"><a name="l04969"></a><span class="lineno"> 4969</span>  dataType = DataType::Float32;</div><div class="line"><a name="l04970"></a><span class="lineno"> 4970</span>  }</div><div class="line"><a name="l04971"></a><span class="lineno"> 4971</span>  <span class="keyword">auto</span> tensorAndData = CreateConstTensorNonPermuted(tensorPtr, tensorInfo, dataType);</div><div class="line"><a name="l04972"></a><span class="lineno"> 4972</span> </div><div class="line"><a name="l04973"></a><span class="lineno"> 4973</span>  std::string layerName = fmt::format(<span class="stringliteral">"Constant:{}"</span>, tensorPtr->name);</div><div class="line"><a name="l04974"></a><span class="lineno"> 4974</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a> *layer = m_Network->AddConstantLayer(tensorAndData.first, layerName.c_str());</div><div class="line"><a name="l04975"></a><span class="lineno"> 4975</span> </div><div class="line"><a name="l04976"></a><span class="lineno"> 4976</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorAndData.first.GetInfo());</div><div class="line"><a name="l04977"></a><span class="lineno"> 4977</span>  RegisterOutputSlots(subgraphIndex,</div><div class="line"><a name="l04978"></a><span class="lineno"> 4978</span>  VIRTUAL_OPERATOR_ID,</div><div class="line"><a name="l04979"></a><span class="lineno"> 4979</span>  layer,</div><div class="line"><a name="l04980"></a><span class="lineno"> 4980</span>  { tensorIndex });</div><div class="line"><a name="l04981"></a><span class="lineno"> 4981</span>  }</div><div class="line"><a name="l04982"></a><span class="lineno"> 4982</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (ShouldConstantTensorBeCreated(tensorIndex))</div><div class="line"><a name="l04983"></a><span class="lineno"> 4983</span>  {</div><div class="line"><a name="l04984"></a><span class="lineno"> 4984</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(tensorPtr);</div><div class="line"><a name="l04985"></a><span class="lineno"> 4985</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> dataType = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l04986"></a><span class="lineno"> 4986</span> </div><div class="line"><a name="l04987"></a><span class="lineno"> 4987</span>  <span class="keywordflow">if</span> (std::find(m_ConstantsToDequantize.begin(), m_ConstantsToDequantize.end(), tensorPtr->buffer)</div><div class="line"><a name="l04988"></a><span class="lineno"> 4988</span>  != m_ConstantsToDequantize.end())</div><div class="line"><a name="l04989"></a><span class="lineno"> 4989</span>  {</div><div class="line"><a name="l04990"></a><span class="lineno"> 4990</span>  dataType = DataType::Float32;</div><div class="line"><a name="l04991"></a><span class="lineno"> 4991</span>  }</div><div class="line"><a name="l04992"></a><span class="lineno"> 4992</span>  <span class="comment">// Make sure isConstant flag is set.</span></div><div class="line"><a name="l04993"></a><span class="lineno"> 4993</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>();</div><div class="line"><a name="l04994"></a><span class="lineno"> 4994</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a71975fcec1464d639f1a78f73164d1bd">SetDataType</a>(dataType);</div><div class="line"><a name="l04995"></a><span class="lineno"> 4995</span> </div><div class="line"><a name="l04996"></a><span class="lineno"> 4996</span>  <span class="keyword">auto</span> tensorAndData = <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(tensorInfo, std::vector<uint8_t>(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>()));</div><div class="line"><a name="l04997"></a><span class="lineno"> 4997</span> </div><div class="line"><a name="l04998"></a><span class="lineno"> 4998</span>  std::string layerName = fmt::format(<span class="stringliteral">"Constant:{}"</span>, tensorPtr->name);</div><div class="line"><a name="l04999"></a><span class="lineno"> 4999</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddConstantLayer(tensorAndData, layerName.c_str());</div><div class="line"><a name="l05000"></a><span class="lineno"> 5000</span> </div><div class="line"><a name="l05001"></a><span class="lineno"> 5001</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l05002"></a><span class="lineno"> 5002</span>  RegisterOutputSlots(subgraphIndex,</div><div class="line"><a name="l05003"></a><span class="lineno"> 5003</span>  VIRTUAL_OPERATOR_ID,</div><div class="line"><a name="l05004"></a><span class="lineno"> 5004</span>  layer,</div><div class="line"><a name="l05005"></a><span class="lineno"> 5005</span>  {tensorIndex});</div><div class="line"><a name="l05006"></a><span class="lineno"> 5006</span>  }</div><div class="line"><a name="l05007"></a><span class="lineno"> 5007</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l05008"></a><span class="lineno"> 5008</span>  {</div><div class="line"><a name="l05009"></a><span class="lineno"> 5009</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l05010"></a><span class="lineno"> 5010</span>  fmt::format(<span class="stringliteral">"Invalid Tensor: Tensor should be constant. {}"</span>,</div><div class="line"><a name="l05011"></a><span class="lineno"> 5011</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l05012"></a><span class="lineno"> 5012</span>  }</div><div class="line"><a name="l05013"></a><span class="lineno"> 5013</span>  }</div><div class="line"><a name="l05014"></a><span class="lineno"> 5014</span>  }</div><div class="line"><a name="l05015"></a><span class="lineno"> 5015</span>  }</div><div class="line"><a name="l05016"></a><span class="lineno"> 5016</span> }</div><div class="line"><a name="l05017"></a><span class="lineno"> 5017</span> </div><div class="line"><a name="l05018"></a><span class="lineno"> 5018</span> <span class="comment">// example usage: BufferRawPtr bufferPtr = GetBuffer(m_Model, inputs[0]->buffer);</span></div><div class="line"><a name="l05019"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538"> 5019</a></span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">TfLiteParserImpl::BufferRawPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">TfLiteParserImpl::GetBuffer</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a>& model, <span class="keywordtype">size_t</span> bufferIndex)</div><div class="line"><a name="l05020"></a><span class="lineno"> 5020</span> {</div><div class="line"><a name="l05021"></a><span class="lineno"> 5021</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a7c88d54e3f895030c70330a4c9d76a7a">CHECK_BUFFER</a>(model, bufferIndex);</div><div class="line"><a name="l05022"></a><span class="lineno"> 5022</span>  <span class="keywordflow">return</span> model->buffers[bufferIndex].get();</div><div class="line"><a name="l05023"></a><span class="lineno"> 5023</span> }</div><div class="line"><a name="l05024"></a><span class="lineno"> 5024</span> </div><div class="line"><a name="l05025"></a><span class="lineno"> 5025</span> <span class="keyword">template</span><<span class="keyword">typename</span> T></div><div class="line"><a name="l05026"></a><span class="lineno"> 5026</span> std::pair<armnn::ConstTensor, TfLiteParserImpl::SupportedDataStorage></div><div class="line"><a name="l05027"></a><span class="lineno"> 5027</span> TfLiteParserImpl::CreateConstTensorAndStoreData(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">TfLiteParserImpl::BufferRawPtr</a> bufferPtr,</div><div class="line"><a name="l05028"></a><span class="lineno"> 5028</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TfLiteParserImpl::TensorRawPtr</a> tensorPtr,</div><div class="line"><a name="l05029"></a><span class="lineno"> 5029</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& tensorInfo,</div><div class="line"><a name="l05030"></a><span class="lineno"> 5030</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::PermutationVector&></a> permutationVector)</div><div class="line"><a name="l05031"></a><span class="lineno"> 5031</span> {</div><div class="line"><a name="l05032"></a><span class="lineno"> 5032</span>  <span class="comment">// Make sure isConstant flag is set.</span></div><div class="line"><a name="l05033"></a><span class="lineno"> 5033</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>();</div><div class="line"><a name="l05034"></a><span class="lineno"> 5034</span> </div><div class="line"><a name="l05035"></a><span class="lineno"> 5035</span>  <span class="keyword">auto</span> constData = CreateConstTensorImpl<T>(bufferPtr,</div><div class="line"><a name="l05036"></a><span class="lineno"> 5036</span>  tensorPtr,</div><div class="line"><a name="l05037"></a><span class="lineno"> 5037</span>  tensorInfo,</div><div class="line"><a name="l05038"></a><span class="lineno"> 5038</span>  permutationVector);</div><div class="line"><a name="l05039"></a><span class="lineno"> 5039</span>  TfLiteParserImpl::SupportedDataStorage storage(std::move(constData.second));</div><div class="line"><a name="l05040"></a><span class="lineno"> 5040</span>  <span class="keywordflow">return</span> std::make_pair(constData.first, std::move(storage));</div><div class="line"><a name="l05041"></a><span class="lineno"> 5041</span> }</div><div class="line"><a name="l05042"></a><span class="lineno"> 5042</span> </div><div class="line"><a name="l05043"></a><span class="lineno"> 5043</span> <span class="keywordtype">bool</span> TfLiteParserImpl::ShouldConstantTensorBeCreated(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tensorIndex)</div><div class="line"><a name="l05044"></a><span class="lineno"> 5044</span> {</div><div class="line"><a name="l05045"></a><span class="lineno"> 5045</span>  <span class="comment">// If the TensorIndex appears in the list of ConstantsToBeCreated then return true</span></div><div class="line"><a name="l05046"></a><span class="lineno"> 5046</span>  <span class="keywordflow">return</span> (std::find(m_ConstantsToBeCreated.begin(), m_ConstantsToBeCreated.end(), tensorIndex)</div><div class="line"><a name="l05047"></a><span class="lineno"> 5047</span>  != m_ConstantsToBeCreated.end());</div><div class="line"><a name="l05048"></a><span class="lineno"> 5048</span> }</div><div class="line"><a name="l05049"></a><span class="lineno"> 5049</span> </div><div class="line"><a name="l05050"></a><span class="lineno"> 5050</span> <span class="keywordtype">bool</span> TfLiteParserImpl::IsConstTensor(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TensorRawPtr</a> tensorPtr)</div><div class="line"><a name="l05051"></a><span class="lineno"> 5051</span> {</div><div class="line"><a name="l05052"></a><span class="lineno"> 5052</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ae38d96fe05581ea025713b3e781c5a43">CHECK_TENSOR_PTR</a>(tensorPtr);</div><div class="line"><a name="l05053"></a><span class="lineno"> 5053</span>  <span class="keywordtype">bool</span> isConst = <span class="keyword">true</span>;</div><div class="line"><a name="l05054"></a><span class="lineno"> 5054</span> </div><div class="line"><a name="l05055"></a><span class="lineno"> 5055</span>  <span class="keyword">auto</span> buffer = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, tensorPtr->buffer);</div><div class="line"><a name="l05056"></a><span class="lineno"> 5056</span>  <span class="keywordflow">if</span> (buffer->data.size() == 0)</div><div class="line"><a name="l05057"></a><span class="lineno"> 5057</span>  {</div><div class="line"><a name="l05058"></a><span class="lineno"> 5058</span>  isConst = <span class="keyword">false</span>;</div><div class="line"><a name="l05059"></a><span class="lineno"> 5059</span>  }</div><div class="line"><a name="l05060"></a><span class="lineno"> 5060</span> </div><div class="line"><a name="l05061"></a><span class="lineno"> 5061</span>  <span class="keywordflow">return</span> isConst;</div><div class="line"><a name="l05062"></a><span class="lineno"> 5062</span> }</div><div class="line"><a name="l05063"></a><span class="lineno"> 5063</span> </div><div class="line"><a name="l05064"></a><span class="lineno"> 5064</span> std::pair<armnn::ConstTensor, TfLiteParserImpl::SupportedDataStorage></div><div class="line"><a name="l05065"></a><span class="lineno"> 5065</span> TfLiteParserImpl::CreateConstTensorPermuted(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TensorRawPtr</a> tensorPtr,</div><div class="line"><a name="l05066"></a><span class="lineno"> 5066</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& tensorInfo,</div><div class="line"><a name="l05067"></a><span class="lineno"> 5067</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::PermutationVector&></a> permutationVector)</div><div class="line"><a name="l05068"></a><span class="lineno"> 5068</span> {</div><div class="line"><a name="l05069"></a><span class="lineno"> 5069</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ae38d96fe05581ea025713b3e781c5a43">CHECK_TENSOR_PTR</a>(tensorPtr);</div><div class="line"><a name="l05070"></a><span class="lineno"> 5070</span>  <span class="keyword">auto</span> bufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, tensorPtr->buffer);</div><div class="line"><a name="l05071"></a><span class="lineno"> 5071</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ae6440b8ea95cf981cd7bbffa52c22fe1">CHECK_BUFFER_SIZE</a>(bufferPtr, tensorInfo, tensorPtr->buffer);</div><div class="line"><a name="l05072"></a><span class="lineno"> 5072</span> </div><div class="line"><a name="l05073"></a><span class="lineno"> 5073</span>  <span class="comment">// Make sure isConstant flag is set.</span></div><div class="line"><a name="l05074"></a><span class="lineno"> 5074</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>();</div><div class="line"><a name="l05075"></a><span class="lineno"> 5075</span> </div><div class="line"><a name="l05076"></a><span class="lineno"> 5076</span>  <span class="keywordflow">switch</span> (tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>())</div><div class="line"><a name="l05077"></a><span class="lineno"> 5077</span>  {</div><div class="line"><a name="l05078"></a><span class="lineno"> 5078</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</div><div class="line"><a name="l05079"></a><span class="lineno"> 5079</span>  <span class="keywordflow">return</span> CreateConstTensorAndStoreData<float>(bufferPtr,</div><div class="line"><a name="l05080"></a><span class="lineno"> 5080</span>  tensorPtr,</div><div class="line"><a name="l05081"></a><span class="lineno"> 5081</span>  tensorInfo,</div><div class="line"><a name="l05082"></a><span class="lineno"> 5082</span>  permutationVector);</div><div class="line"><a name="l05083"></a><span class="lineno"> 5083</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</div><div class="line"><a name="l05084"></a><span class="lineno"> 5084</span>  <span class="keywordflow">return</span> CreateConstTensorAndStoreData<uint8_t>(bufferPtr,</div><div class="line"><a name="l05085"></a><span class="lineno"> 5085</span>  tensorPtr,</div><div class="line"><a name="l05086"></a><span class="lineno"> 5086</span>  tensorInfo,</div><div class="line"><a name="l05087"></a><span class="lineno"> 5087</span>  permutationVector);</div><div class="line"><a name="l05088"></a><span class="lineno"> 5088</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>:</div><div class="line"><a name="l05089"></a><span class="lineno"> 5089</span>  <span class="keywordflow">return</span> CreateConstTensorAndStoreData<int8_t>(bufferPtr,</div><div class="line"><a name="l05090"></a><span class="lineno"> 5090</span>  tensorPtr,</div><div class="line"><a name="l05091"></a><span class="lineno"> 5091</span>  tensorInfo,</div><div class="line"><a name="l05092"></a><span class="lineno"> 5092</span>  permutationVector);</div><div class="line"><a name="l05093"></a><span class="lineno"> 5093</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>:</div><div class="line"><a name="l05094"></a><span class="lineno"> 5094</span>  <span class="keywordflow">return</span> CreateConstTensorAndStoreData<int8_t>(bufferPtr,</div><div class="line"><a name="l05095"></a><span class="lineno"> 5095</span>  tensorPtr,</div><div class="line"><a name="l05096"></a><span class="lineno"> 5096</span>  tensorInfo,</div><div class="line"><a name="l05097"></a><span class="lineno"> 5097</span>  permutationVector);</div><div class="line"><a name="l05098"></a><span class="lineno"> 5098</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>:</div><div class="line"><a name="l05099"></a><span class="lineno"> 5099</span>  <span class="keywordflow">return</span> CreateConstTensorAndStoreData<int32_t>(bufferPtr,</div><div class="line"><a name="l05100"></a><span class="lineno"> 5100</span>  tensorPtr,</div><div class="line"><a name="l05101"></a><span class="lineno"> 5101</span>  tensorInfo,</div><div class="line"><a name="l05102"></a><span class="lineno"> 5102</span>  permutationVector);</div><div class="line"><a name="l05103"></a><span class="lineno"> 5103</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l05104"></a><span class="lineno"> 5104</span>  {</div><div class="line"><a name="l05105"></a><span class="lineno"> 5105</span>  std::stringstream errString;</div><div class="line"><a name="l05106"></a><span class="lineno"> 5106</span>  errString << <span class="stringliteral">"Unexpected datatype when creating const tensor: "</span></div><div class="line"><a name="l05107"></a><span class="lineno"> 5107</span>  << <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">armnn::GetDataTypeName</a>(tensorInfo.GetDataType())</div><div class="line"><a name="l05108"></a><span class="lineno"> 5108</span>  << <span class="stringliteral">" shape:"</span> << tensorInfo.GetShape()</div><div class="line"><a name="l05109"></a><span class="lineno"> 5109</span>  << <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString();</div><div class="line"><a name="l05110"></a><span class="lineno"> 5110</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(errString.str());</div><div class="line"><a name="l05111"></a><span class="lineno"> 5111</span>  }</div><div class="line"><a name="l05112"></a><span class="lineno"> 5112</span>  }</div><div class="line"><a name="l05113"></a><span class="lineno"> 5113</span> }</div><div class="line"><a name="l05114"></a><span class="lineno"> 5114</span> </div><div class="line"><a name="l05115"></a><span class="lineno"> 5115</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> TfLiteParserImpl::CreateConstTensorNonPermuted(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TensorRawPtr</a> tensorPtr,</div><div class="line"><a name="l05116"></a><span class="lineno"> 5116</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& tensorInfo)</div><div class="line"><a name="l05117"></a><span class="lineno"> 5117</span> {</div><div class="line"><a name="l05118"></a><span class="lineno"> 5118</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ae38d96fe05581ea025713b3e781c5a43">CHECK_TENSOR_PTR</a>(tensorPtr);</div><div class="line"><a name="l05119"></a><span class="lineno"> 5119</span>  <span class="keyword">auto</span> bufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, tensorPtr->buffer);</div><div class="line"><a name="l05120"></a><span class="lineno"> 5120</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ae6440b8ea95cf981cd7bbffa52c22fe1">CHECK_BUFFER_SIZE</a>(bufferPtr, tensorInfo, tensorPtr->buffer);</div><div class="line"><a name="l05121"></a><span class="lineno"> 5121</span> </div><div class="line"><a name="l05122"></a><span class="lineno"> 5122</span>  <span class="comment">// Make sure isConstant flag is set.</span></div><div class="line"><a name="l05123"></a><span class="lineno"> 5123</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>();</div><div class="line"><a name="l05124"></a><span class="lineno"> 5124</span> </div><div class="line"><a name="l05125"></a><span class="lineno"> 5125</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(tensorInfo, bufferPtr->data.data());</div><div class="line"><a name="l05126"></a><span class="lineno"> 5126</span> }</div><div class="line"><a name="l05127"></a><span class="lineno"> 5127</span> </div><div class="line"><a name="l05128"></a><span class="lineno"> 5128</span> std::pair<armnn::ConstTensor, std::unique_ptr<float[]>></div><div class="line"><a name="l05129"></a><span class="lineno"> 5129</span> TfLiteParserImpl::CreateConstTensorNonPermuted(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TensorRawPtr</a> tensorPtr,</div><div class="line"><a name="l05130"></a><span class="lineno"> 5130</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& tensorInfo,</div><div class="line"><a name="l05131"></a><span class="lineno"> 5131</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> inputDataType)</div><div class="line"><a name="l05132"></a><span class="lineno"> 5132</span> {</div><div class="line"><a name="l05133"></a><span class="lineno"> 5133</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ae38d96fe05581ea025713b3e781c5a43">CHECK_TENSOR_PTR</a>(tensorPtr);</div><div class="line"><a name="l05134"></a><span class="lineno"> 5134</span>  <span class="keyword">auto</span> bufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, tensorPtr->buffer);</div><div class="line"><a name="l05135"></a><span class="lineno"> 5135</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ae6440b8ea95cf981cd7bbffa52c22fe1">CHECK_BUFFER_SIZE</a>(bufferPtr, tensorInfo, tensorPtr->buffer);</div><div class="line"><a name="l05136"></a><span class="lineno"> 5136</span> </div><div class="line"><a name="l05137"></a><span class="lineno"> 5137</span>  <span class="comment">// Make sure isConstant flag is set.</span></div><div class="line"><a name="l05138"></a><span class="lineno"> 5138</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>();</div><div class="line"><a name="l05139"></a><span class="lineno"> 5139</span> </div><div class="line"><a name="l05140"></a><span class="lineno"> 5140</span>  <span class="keywordflow">if</span> (inputDataType == DataType::Float32 && tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != DataType::Float32)</div><div class="line"><a name="l05141"></a><span class="lineno"> 5141</span>  {</div><div class="line"><a name="l05142"></a><span class="lineno"> 5142</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l05143"></a><span class="lineno"> 5143</span>  {</div><div class="line"><a name="l05144"></a><span class="lineno"> 5144</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> constTensorInfo(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), DataType::Float32, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l05145"></a><span class="lineno"> 5145</span>  std::unique_ptr<float[]> data = <a class="code" href="namespacearmnn_utils.xhtml#a573c6170ca8451709e031b6243bc187d">armnnUtils::ToFloatArray</a>(bufferPtr->data, tensorInfo);</div><div class="line"><a name="l05146"></a><span class="lineno"> 5146</span>  <span class="keywordflow">return</span> std::make_pair(<a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(constTensorInfo, data.get()), std::move(data));</div><div class="line"><a name="l05147"></a><span class="lineno"> 5147</span>  }</div><div class="line"><a name="l05148"></a><span class="lineno"> 5148</span>  <span class="keywordflow">catch</span> (<a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>&)</div><div class="line"><a name="l05149"></a><span class="lineno"> 5149</span>  {</div><div class="line"><a name="l05150"></a><span class="lineno"> 5150</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l05151"></a><span class="lineno"> 5151</span>  fmt::format(<span class="stringliteral">"Unsupported input/weights combination: Input {} not supported with Weights {}"</span>,</div><div class="line"><a name="l05152"></a><span class="lineno"> 5152</span>  <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(DataType::Float32),</div><div class="line"><a name="l05153"></a><span class="lineno"> 5153</span>  <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()),</div><div class="line"><a name="l05154"></a><span class="lineno"> 5154</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l05155"></a><span class="lineno"> 5155</span>  }</div><div class="line"><a name="l05156"></a><span class="lineno"> 5156</span>  }</div><div class="line"><a name="l05157"></a><span class="lineno"> 5157</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l05158"></a><span class="lineno"> 5158</span>  {</div><div class="line"><a name="l05159"></a><span class="lineno"> 5159</span>  <span class="keywordflow">return</span> std::make_pair(<a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(tensorInfo, bufferPtr->data.data()), std::unique_ptr<<span class="keywordtype">float</span>[]>());</div><div class="line"><a name="l05160"></a><span class="lineno"> 5160</span>  }</div><div class="line"><a name="l05161"></a><span class="lineno"> 5161</span> }</div><div class="line"><a name="l05162"></a><span class="lineno"> 5162</span> </div><div class="line"><a name="l05163"></a><span class="lineno"> 5163</span> std::pair<armnn::ConstTensor*, std::unique_ptr<float[]>></div><div class="line"><a name="l05164"></a><span class="lineno"> 5164</span> TfLiteParserImpl::CreateConstTensorPtr(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TensorRawPtr</a> tensorPtr, <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& inputTensorInfo)</div><div class="line"><a name="l05165"></a><span class="lineno"> 5165</span> {</div><div class="line"><a name="l05166"></a><span class="lineno"> 5166</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ae38d96fe05581ea025713b3e781c5a43">CHECK_TENSOR_PTR</a>(tensorPtr);</div><div class="line"><a name="l05167"></a><span class="lineno"> 5167</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(tensorPtr);</div><div class="line"><a name="l05168"></a><span class="lineno"> 5168</span>  <span class="keyword">auto</span> bufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">GetBuffer</a>(m_Model, tensorPtr->buffer);</div><div class="line"><a name="l05169"></a><span class="lineno"> 5169</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ae6440b8ea95cf981cd7bbffa52c22fe1">CHECK_BUFFER_SIZE</a>(bufferPtr, tensorInfo, tensorPtr->buffer);</div><div class="line"><a name="l05170"></a><span class="lineno"> 5170</span> </div><div class="line"><a name="l05171"></a><span class="lineno"> 5171</span>  <span class="comment">// Make sure isConstant flag is set.</span></div><div class="line"><a name="l05172"></a><span class="lineno"> 5172</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>();</div><div class="line"><a name="l05173"></a><span class="lineno"> 5173</span> </div><div class="line"><a name="l05174"></a><span class="lineno"> 5174</span>  <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == DataType::Float32 && tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != DataType::Float32)</div><div class="line"><a name="l05175"></a><span class="lineno"> 5175</span>  {</div><div class="line"><a name="l05176"></a><span class="lineno"> 5176</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l05177"></a><span class="lineno"> 5177</span>  {</div><div class="line"><a name="l05178"></a><span class="lineno"> 5178</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> constTensorInfo(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), DataType::Float32, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l05179"></a><span class="lineno"> 5179</span>  std::unique_ptr<float[]> data = <a class="code" href="namespacearmnn_utils.xhtml#a573c6170ca8451709e031b6243bc187d">armnnUtils::ToFloatArray</a>(bufferPtr->data, tensorInfo);</div><div class="line"><a name="l05180"></a><span class="lineno"> 5180</span>  <span class="keywordflow">return</span> std::make_pair(<span class="keyword">new</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(constTensorInfo, data.get()), std::move(data));</div><div class="line"><a name="l05181"></a><span class="lineno"> 5181</span>  }</div><div class="line"><a name="l05182"></a><span class="lineno"> 5182</span>  <span class="keywordflow">catch</span> (<a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>&)</div><div class="line"><a name="l05183"></a><span class="lineno"> 5183</span>  {</div><div class="line"><a name="l05184"></a><span class="lineno"> 5184</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l05185"></a><span class="lineno"> 5185</span>  fmt::format(<span class="stringliteral">"Unsupported input/weights combination: Input {} not supported with Weights {}"</span>,</div><div class="line"><a name="l05186"></a><span class="lineno"> 5186</span>  <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(DataType::Float32),</div><div class="line"><a name="l05187"></a><span class="lineno"> 5187</span>  <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()),</div><div class="line"><a name="l05188"></a><span class="lineno"> 5188</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l05189"></a><span class="lineno"> 5189</span>  }</div><div class="line"><a name="l05190"></a><span class="lineno"> 5190</span>  }</div><div class="line"><a name="l05191"></a><span class="lineno"> 5191</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l05192"></a><span class="lineno"> 5192</span>  {</div><div class="line"><a name="l05193"></a><span class="lineno"> 5193</span>  <span class="keywordflow">return</span> std::make_pair(<span class="keyword">new</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(tensorInfo, bufferPtr->data.data()), std::unique_ptr<<span class="keywordtype">float</span>[]>());</div><div class="line"><a name="l05194"></a><span class="lineno"> 5194</span>  }</div><div class="line"><a name="l05195"></a><span class="lineno"> 5195</span> }</div><div class="line"><a name="l05196"></a><span class="lineno"> 5196</span> </div><div class="line"><a name="l05197"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a913d452e7766210b59a69d3c658b2862"> 5197</a></span> <a class="code" href="namespacearmnn_tf_lite_parser.xhtml#a9084adbf804022c874039ad40d1939e9">BindingPointInfo</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a913d452e7766210b59a69d3c658b2862">TfLiteParserImpl::GetNetworkInputBindingInfo</a>(<span class="keywordtype">size_t</span> subgraphId,</div><div class="line"><a name="l05198"></a><span class="lineno"> 5198</span>  <span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div><div class="line"><a name="l05199"></a><span class="lineno"> 5199</span> <span class="keyword"></span>{</div><div class="line"><a name="l05200"></a><span class="lineno"> 5200</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphId);</div><div class="line"><a name="l05201"></a><span class="lineno"> 5201</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a20518e431cb501b172371b850e6c3ec7">GetSubgraphInputs</a>(m_Model, subgraphId);</div><div class="line"><a name="l05202"></a><span class="lineno"> 5202</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span>& input : inputs)</div><div class="line"><a name="l05203"></a><span class="lineno"> 5203</span>  {</div><div class="line"><a name="l05204"></a><span class="lineno"> 5204</span>  <span class="keywordflow">if</span> (input.second->name == name)</div><div class="line"><a name="l05205"></a><span class="lineno"> 5205</span>  {</div><div class="line"><a name="l05206"></a><span class="lineno"> 5206</span>  <span class="keyword">auto</span> bindingId = GenerateLayerBindingId(subgraphId, input.first);</div><div class="line"><a name="l05207"></a><span class="lineno"> 5207</span>  <span class="keyword">auto</span> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(input.second);</div><div class="line"><a name="l05208"></a><span class="lineno"> 5208</span>  <span class="comment">// Input tensors are always treated as constant tensors during network execution.</span></div><div class="line"><a name="l05209"></a><span class="lineno"> 5209</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l05210"></a><span class="lineno"> 5210</span>  <span class="keywordflow">return</span> std::make_pair(bindingId, inputTensorInfo);</div><div class="line"><a name="l05211"></a><span class="lineno"> 5211</span>  }</div><div class="line"><a name="l05212"></a><span class="lineno"> 5212</span>  }</div><div class="line"><a name="l05213"></a><span class="lineno"> 5213</span> </div><div class="line"><a name="l05214"></a><span class="lineno"> 5214</span>  std::stringstream bindings;</div><div class="line"><a name="l05215"></a><span class="lineno"> 5215</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span>& input : inputs)</div><div class="line"><a name="l05216"></a><span class="lineno"> 5216</span>  {</div><div class="line"><a name="l05217"></a><span class="lineno"> 5217</span>  bindings << <span class="stringliteral">"'"</span> << input.second->name << <span class="stringliteral">"' "</span>;</div><div class="line"><a name="l05218"></a><span class="lineno"> 5218</span>  }</div><div class="line"><a name="l05219"></a><span class="lineno"> 5219</span> </div><div class="line"><a name="l05220"></a><span class="lineno"> 5220</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l05221"></a><span class="lineno"> 5221</span>  fmt::format(<span class="stringliteral">"No input binding found for subgraph:{} and name:{}. "</span></div><div class="line"><a name="l05222"></a><span class="lineno"> 5222</span>  <span class="stringliteral">"Possible inputs are: [{}] {}"</span>,</div><div class="line"><a name="l05223"></a><span class="lineno"> 5223</span>  subgraphId,</div><div class="line"><a name="l05224"></a><span class="lineno"> 5224</span>  name,</div><div class="line"><a name="l05225"></a><span class="lineno"> 5225</span>  bindings.str(),</div><div class="line"><a name="l05226"></a><span class="lineno"> 5226</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l05227"></a><span class="lineno"> 5227</span> }</div><div class="line"><a name="l05228"></a><span class="lineno"> 5228</span> </div><div class="line"><a name="l05229"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a59139faede3a07a43c4c8f86bb03dc43"> 5229</a></span> <a class="code" href="namespacearmnn_tf_lite_parser.xhtml#a9084adbf804022c874039ad40d1939e9">BindingPointInfo</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a59139faede3a07a43c4c8f86bb03dc43">TfLiteParserImpl::GetNetworkOutputBindingInfo</a>(<span class="keywordtype">size_t</span> subgraphId,</div><div class="line"><a name="l05230"></a><span class="lineno"> 5230</span>  <span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div><div class="line"><a name="l05231"></a><span class="lineno"> 5231</span> <span class="keyword"></span>{</div><div class="line"><a name="l05232"></a><span class="lineno"> 5232</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphId);</div><div class="line"><a name="l05233"></a><span class="lineno"> 5233</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a1bd7b663eb19e2355b6aa5c36232d6fc">GetSubgraphOutputs</a>(m_Model, subgraphId);</div><div class="line"><a name="l05234"></a><span class="lineno"> 5234</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < outputs.size(); ++i)</div><div class="line"><a name="l05235"></a><span class="lineno"> 5235</span>  {</div><div class="line"><a name="l05236"></a><span class="lineno"> 5236</span>  <span class="keyword">auto</span> <span class="keyword">const</span> output = outputs[i];</div><div class="line"><a name="l05237"></a><span class="lineno"> 5237</span>  <span class="keywordflow">if</span> (output.second->name == name)</div><div class="line"><a name="l05238"></a><span class="lineno"> 5238</span>  {</div><div class="line"><a name="l05239"></a><span class="lineno"> 5239</span>  <span class="keyword">auto</span> bindingId = GenerateLayerBindingId(subgraphId, output.first);</div><div class="line"><a name="l05240"></a><span class="lineno"> 5240</span>  std::vector<unsigned int> shape = m_OverriddenOutputShapes.size() > 0 ?</div><div class="line"><a name="l05241"></a><span class="lineno"> 5241</span>  m_OverriddenOutputShapes[i] : AsUnsignedVector(output.second->shape);</div><div class="line"><a name="l05242"></a><span class="lineno"> 5242</span>  <span class="keywordflow">return</span> std::make_pair(bindingId, <a class="code" href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">ToTensorInfo</a>(output.second, shape));</div><div class="line"><a name="l05243"></a><span class="lineno"> 5243</span>  }</div><div class="line"><a name="l05244"></a><span class="lineno"> 5244</span>  }</div><div class="line"><a name="l05245"></a><span class="lineno"> 5245</span> </div><div class="line"><a name="l05246"></a><span class="lineno"> 5246</span>  std::stringstream bindings;</div><div class="line"><a name="l05247"></a><span class="lineno"> 5247</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span>& output : outputs)</div><div class="line"><a name="l05248"></a><span class="lineno"> 5248</span>  {</div><div class="line"><a name="l05249"></a><span class="lineno"> 5249</span>  bindings << <span class="stringliteral">"'"</span> << output.second->name << <span class="stringliteral">"' "</span>;</div><div class="line"><a name="l05250"></a><span class="lineno"> 5250</span>  }</div><div class="line"><a name="l05251"></a><span class="lineno"> 5251</span> </div><div class="line"><a name="l05252"></a><span class="lineno"> 5252</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l05253"></a><span class="lineno"> 5253</span>  fmt::format(<span class="stringliteral">"No output binding found for subgraph:{} and name:{}. "</span></div><div class="line"><a name="l05254"></a><span class="lineno"> 5254</span>  <span class="stringliteral">"Possible outputs are: [{}] {}"</span>,</div><div class="line"><a name="l05255"></a><span class="lineno"> 5255</span>  subgraphId,</div><div class="line"><a name="l05256"></a><span class="lineno"> 5256</span>  name,</div><div class="line"><a name="l05257"></a><span class="lineno"> 5257</span>  bindings.str(),</div><div class="line"><a name="l05258"></a><span class="lineno"> 5258</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l05259"></a><span class="lineno"> 5259</span> }</div><div class="line"><a name="l05260"></a><span class="lineno"> 5260</span> </div><div class="line"><a name="l05261"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a1b7aed8466cc5792966d271a65285d0e"> 5261</a></span> <span class="keywordtype">size_t</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a1b7aed8466cc5792966d271a65285d0e">TfLiteParserImpl::GetSubgraphCount</a>()<span class="keyword"> const</span></div><div class="line"><a name="l05262"></a><span class="lineno"> 5262</span> <span class="keyword"></span>{</div><div class="line"><a name="l05263"></a><span class="lineno"> 5263</span>  <span class="keywordflow">return</span> m_Model->subgraphs.size();</div><div class="line"><a name="l05264"></a><span class="lineno"> 5264</span> }</div><div class="line"><a name="l05265"></a><span class="lineno"> 5265</span> </div><div class="line"><a name="l05266"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe1ea028d8fcb9942c204d4415ff3319"> 5266</a></span> std::vector<std::string> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe1ea028d8fcb9942c204d4415ff3319">TfLiteParserImpl::GetSubgraphInputTensorNames</a>(<span class="keywordtype">size_t</span> subgraphId)<span class="keyword"> const</span></div><div class="line"><a name="l05267"></a><span class="lineno"> 5267</span> <span class="keyword"></span>{</div><div class="line"><a name="l05268"></a><span class="lineno"> 5268</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphId);</div><div class="line"><a name="l05269"></a><span class="lineno"> 5269</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a20518e431cb501b172371b850e6c3ec7">GetSubgraphInputs</a>(m_Model, subgraphId);</div><div class="line"><a name="l05270"></a><span class="lineno"> 5270</span>  std::vector<std::string> result;</div><div class="line"><a name="l05271"></a><span class="lineno"> 5271</span>  result.reserve(inputs.size());</div><div class="line"><a name="l05272"></a><span class="lineno"> 5272</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span>& input : inputs)</div><div class="line"><a name="l05273"></a><span class="lineno"> 5273</span>  {</div><div class="line"><a name="l05274"></a><span class="lineno"> 5274</span>  result.push_back(input.second->name);</div><div class="line"><a name="l05275"></a><span class="lineno"> 5275</span>  }</div><div class="line"><a name="l05276"></a><span class="lineno"> 5276</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l05277"></a><span class="lineno"> 5277</span> }</div><div class="line"><a name="l05278"></a><span class="lineno"> 5278</span> </div><div class="line"><a name="l05279"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a949070abe70fabe18cc1eb29d5853f47"> 5279</a></span> std::vector<std::string> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a949070abe70fabe18cc1eb29d5853f47">TfLiteParserImpl::GetSubgraphOutputTensorNames</a>(<span class="keywordtype">size_t</span> subgraphId)<span class="keyword"> const</span></div><div class="line"><a name="l05280"></a><span class="lineno"> 5280</span> <span class="keyword"></span>{</div><div class="line"><a name="l05281"></a><span class="lineno"> 5281</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphId);</div><div class="line"><a name="l05282"></a><span class="lineno"> 5282</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a1bd7b663eb19e2355b6aa5c36232d6fc">GetSubgraphOutputs</a>(m_Model, subgraphId);</div><div class="line"><a name="l05283"></a><span class="lineno"> 5283</span>  std::vector<std::string> result;</div><div class="line"><a name="l05284"></a><span class="lineno"> 5284</span>  result.reserve(outputs.size());</div><div class="line"><a name="l05285"></a><span class="lineno"> 5285</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span>& output : outputs)</div><div class="line"><a name="l05286"></a><span class="lineno"> 5286</span>  {</div><div class="line"><a name="l05287"></a><span class="lineno"> 5287</span>  result.push_back(output.second->name);</div><div class="line"><a name="l05288"></a><span class="lineno"> 5288</span>  }</div><div class="line"><a name="l05289"></a><span class="lineno"> 5289</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l05290"></a><span class="lineno"> 5290</span> }</div><div class="line"><a name="l05291"></a><span class="lineno"> 5291</span> </div><div class="line"><a name="l05292"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aa09a8bb02eed50715082d8b7fccd2f8d"> 5292</a></span> <span class="keyword">const</span> std::string <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aa09a8bb02eed50715082d8b7fccd2f8d">TfLiteParserImpl::GetVersion</a>()</div><div class="line"><a name="l05293"></a><span class="lineno"> 5293</span> {</div><div class="line"><a name="l05294"></a><span class="lineno"> 5294</span>  <span class="keywordflow">return</span> <a class="code" href="include_2armnn_tf_lite_parser_2_version_8hpp.xhtml#a88c7ccef0cfc85702b48557ec804ef9a">TFLITE_PARSER_VERSION</a>;</div><div class="line"><a name="l05295"></a><span class="lineno"> 5295</span> }</div><div class="line"><a name="l05296"></a><span class="lineno"> 5296</span> </div><div class="line"><a name="l05297"></a><span class="lineno"> 5297</span> TfLiteParserImpl::SupportedDataStorage::SupportedDataStorage(std::unique_ptr<<span class="keywordtype">float</span>[]>&& data)</div><div class="line"><a name="l05298"></a><span class="lineno"> 5298</span> : m_FloatData(std::move(data))</div><div class="line"><a name="l05299"></a><span class="lineno"> 5299</span> , m_Uint8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l05300"></a><span class="lineno"> 5300</span> , m_Int8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l05301"></a><span class="lineno"> 5301</span> , m_Int32Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l05302"></a><span class="lineno"> 5302</span> {</div><div class="line"><a name="l05303"></a><span class="lineno"> 5303</span> }</div><div class="line"><a name="l05304"></a><span class="lineno"> 5304</span> </div><div class="line"><a name="l05305"></a><span class="lineno"> 5305</span> TfLiteParserImpl::SupportedDataStorage::SupportedDataStorage(std::unique_ptr<uint8_t[]>&& data)</div><div class="line"><a name="l05306"></a><span class="lineno"> 5306</span> : m_FloatData(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l05307"></a><span class="lineno"> 5307</span> , m_Uint8Data(std::move(data))</div><div class="line"><a name="l05308"></a><span class="lineno"> 5308</span> , m_Int8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l05309"></a><span class="lineno"> 5309</span> , m_Int32Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l05310"></a><span class="lineno"> 5310</span> {</div><div class="line"><a name="l05311"></a><span class="lineno"> 5311</span> }</div><div class="line"><a name="l05312"></a><span class="lineno"> 5312</span> </div><div class="line"><a name="l05313"></a><span class="lineno"> 5313</span> TfLiteParserImpl::SupportedDataStorage::SupportedDataStorage(std::unique_ptr<int8_t[]>&& data)</div><div class="line"><a name="l05314"></a><span class="lineno"> 5314</span> : m_FloatData(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l05315"></a><span class="lineno"> 5315</span> , m_Uint8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l05316"></a><span class="lineno"> 5316</span> , m_Int8Data(std::move(data))</div><div class="line"><a name="l05317"></a><span class="lineno"> 5317</span> , m_Int32Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l05318"></a><span class="lineno"> 5318</span> {</div><div class="line"><a name="l05319"></a><span class="lineno"> 5319</span> }</div><div class="line"><a name="l05320"></a><span class="lineno"> 5320</span> </div><div class="line"><a name="l05321"></a><span class="lineno"> 5321</span> TfLiteParserImpl::SupportedDataStorage::SupportedDataStorage(std::unique_ptr<int32_t[]>&& data)</div><div class="line"><a name="l05322"></a><span class="lineno"> 5322</span> : m_FloatData(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l05323"></a><span class="lineno"> 5323</span> , m_Uint8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l05324"></a><span class="lineno"> 5324</span> , m_Int8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l05325"></a><span class="lineno"> 5325</span> , m_Int32Data(std::move(data))</div><div class="line"><a name="l05326"></a><span class="lineno"> 5326</span> {</div><div class="line"><a name="l05327"></a><span class="lineno"> 5327</span> }</div><div class="line"><a name="l05328"></a><span class="lineno"> 5328</span> </div><div class="line"><a name="l05329"></a><span class="lineno"> 5329</span> } <span class="comment">// armnnTfLiteParser</span></div><div class="ttc" id="namespacetflite_xhtml"><div class="ttname"><a href="namespacetflite.xhtml">tflite</a></div><div class="ttdef"><b>Definition:</b> <a href="armnn__external__delegate_8cpp_source.xhtml#l00012">armnn_external_delegate.cpp:12</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#l00550">Descriptors.hpp:550</a></div></div> +<div class="ttc" id="_tf_lite_parser_8cpp_xhtml_afbe702264a4e175da37c4941c0894bdb"><div class="ttname"><a href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a></div><div class="ttdeci">#define CHECK_MODEL(MODEL, SUBGRAPH_INDEX, OPERATOR_INDEX)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00183">TfLiteParser.cpp:183</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_abfc86471394295357a23b3addd0b5b1c"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abfc86471394295357a23b3addd0b5b1c">armnnTfLiteParser::TfLiteParserImpl::ModelPtr</a></div><div class="ttdeci">std::unique_ptr< tflite::ModelT > ModelPtr</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00030">TfLiteParser.hpp:30</a></div></div> +<div class="ttc" id="_ignore_unused_8hpp_xhtml"><div class="ttname"><a href="_ignore_unused_8hpp.xhtml">IgnoreUnused.hpp</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_a1bd7b663eb19e2355b6aa5c36232d6fc"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a1bd7b663eb19e2355b6aa5c36232d6fc">armnnTfLiteParser::TfLiteParserImpl::GetSubgraphOutputs</a></div><div class="ttdeci">static TensorIdRawPtrVector GetSubgraphOutputs(const ModelPtr &model, size_t subgraphIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l04782">TfLiteParser.cpp:4782</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a2d299363c9fc33334c571fa29ca4f58ca8768a6821cd735aea4f5b0df88c1fc6a"><div class="ttname"><a href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca8768a6821cd735aea4f5b0df88c1fc6a">armnn::ComparisonOperation::Greater</a></div></div> +<div class="ttc" id="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="structarmnn_1_1_lstm_input_params_xhtml_ab03e6e1514f74427916c892f473fe04c"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">armnn::LstmInputParams::m_ProjectionWeights</a></div><div class="ttdeci">const ConstTensor * m_ProjectionWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00055">LstmParams.hpp:55</a></div></div> +<div class="ttc" id="structarmnn_1_1_elementwise_unary_descriptor_xhtml_afe768be66897eb3d73284424e3239b23"><div class="ttname"><a href="structarmnn_1_1_elementwise_unary_descriptor.xhtml#afe768be66897eb3d73284424e3239b23">armnn::ElementwiseUnaryDescriptor::m_Operation</a></div><div class="ttdeci">UnaryOperation m_Operation</div><div class="ttdoc">Specifies the elementwiseUnary operation to execute. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00125">Descriptors.hpp:125</a></div></div> +<div class="ttc" id="structarmnn_1_1_stack_descriptor_xhtml_ab218de7805899c8412d75d1fd1d846d2"><div class="ttname"><a href="structarmnn_1_1_stack_descriptor.xhtml#ab218de7805899c8412d75d1fd1d846d2">armnn::StackDescriptor::m_Axis</a></div><div class="ttdeci">uint32_t m_Axis</div><div class="ttdoc">0-based axis along which to stack the input tensors. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01220">Descriptors.hpp:1220</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#l00224">Descriptors.hpp:224</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#l00068">INetwork.hpp:68</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_ab509802c659de19929f18bad14a35c58"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ab509802c659de19929f18bad14a35c58">armnn::DetectionPostProcessDescriptor::m_ScaleW</a></div><div class="ttdeci">float m_ScaleW</div><div class="ttdoc">Center size encoding scale weight. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00731">Descriptors.hpp:731</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a22f377fc4e10dc1773a3f979061e85f1"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a22f377fc4e10dc1773a3f979061e85f1">armnn::TensorInfo::IsTypeSpaceMatch</a></div><div class="ttdeci">bool IsTypeSpaceMatch(const TensorInfo &other) const</div><div class="ttdoc">Check that the types are the same and, if quantize, that the quantization parameters are the same...</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00432">Tensor.cpp:432</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a4a9d678146f572808a361dbdc5489f38"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">armnn::LstmInputParams::m_CellBias</a></div><div class="ttdeci">const ConstTensor * m_CellBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00053">LstmParams.hpp:53</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#l00381">Descriptors.hpp:381</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::DepthwiseConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00676">Descriptors.hpp:676</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a9c2cba04b6d7ace4fc2a2436b82a5a63"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">armnn::IConnectableLayer::GetNumInputSlots</a></div><div class="ttdeci">virtual unsigned int GetNumInputSlots() const =0</div><div class="ttdoc">Returns the number of connectable input slots. </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#l00771">Descriptors.hpp:771</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#l01387">Descriptors.hpp:1387</a></div></div> +<div class="ttc" id="_tf_lite_parser_8cpp_xhtml_a5fc762bb40634f39ff6ff4e23005a3a9"><div class="ttname"><a href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a></div><div class="ttdeci">#define ARMNN_THROW_PARSE_EXCEPTION(msg)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00042">TfLiteParser.cpp:42</a></div></div> +<div class="ttc" id="_tensor_8hpp_xhtml"><div class="ttname"><a href="_tensor_8hpp.xhtml">Tensor.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00191">Tensor.hpp:191</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::DepthwiseConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00666">Descriptors.hpp:666</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#l00375">Descriptors.hpp:375</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a1cfaa710db2a54673b21d2ea2da757c8ace0be71e33226e4c1db2bcea5959f16b"><div class="ttname"><a href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8ace0be71e33226e4c1db2bcea5959f16b">armnn::UnaryOperation::Log</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_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_ac3486e6c1a291aa67efd8b280ffb83cc"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">armnnTfLiteParser::TfLiteParserImpl::TensorRawPtr</a></div><div class="ttdeci">const tflite::TensorT * TensorRawPtr</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00035">TfLiteParser.hpp:35</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a2d299363c9fc33334c571fa29ca4f58caf5f286e73bda105e538310b3190f75c5"><div class="ttname"><a href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58caf5f286e73bda105e538310b3190f75c5">armnn::ComparisonOperation::Equal</a></div></div> +<div class="ttc" id="structarmnn_1_1_check_location_xhtml_a5e3562cda960da001597e7dd5679b140"><div class="ttname"><a href="structarmnn_1_1_check_location.xhtml#a5e3562cda960da001597e7dd5679b140">armnn::CheckLocation::AsString</a></div><div class="ttdeci">std::string AsString() const</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00029">Exceptions.hpp:29</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_a6d0384878432cfc9652b7ae8bc59506f"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a6d0384878432cfc9652b7ae8bc59506f">armnn::StridedSliceDescriptor::m_ShrinkAxisMask</a></div><div class="ttdeci">int32_t m_ShrinkAxisMask</div><div class="ttdoc">Shrink axis mask value. If set, the nth specification shrinks the dimensionality by 1...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01302">Descriptors.hpp:1302</a></div></div> +<div class="ttc" id="structarmnn_1_1_reshape_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_reshape_descriptor.xhtml">armnn::ReshapeDescriptor</a></div><div class="ttdoc">A ReshapeDescriptor for the ReshapeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00970">Descriptors.hpp:970</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml_acccb75bd1d68a81f6ddd61687f51c5a1"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml#acccb75bd1d68a81f6ddd61687f51c5a1">armnn::TensorShape::AreAllDimensionsSpecified</a></div><div class="ttdeci">bool AreAllDimensionsSpecified() const</div><div class="ttdoc">Checks if there is at least one dimension not specified. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00241">Tensor.cpp:241</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_a118fe06b7c2599da60398ee311ede923"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a118fe06b7c2599da60398ee311ede923">armnn::StridedSliceDescriptor::m_Begin</a></div><div class="ttdeci">std::vector< int > m_Begin</div><div class="ttdoc">Begin values for the input that will be sliced. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01289">Descriptors.hpp:1289</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_afe204ca375b74e9a72640c9227918d0a"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">armnn::LstmInputParams::m_CellToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_CellToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00050">LstmParams.hpp:50</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_aaafbda6e6816876b3d7963cfe64dd2f8"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">armnnTfLiteParser::TfLiteParserImpl::BufferRawPtr</a></div><div class="ttdeci">const tflite::BufferT * BufferRawPtr</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00040">TfLiteParser.hpp:40</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_a11d5c25face9b54e90f79ee8bdc1d0fb"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#a11d5c25face9b54e90f79ee8bdc1d0fb">armnn::Convolution3dDescriptor::m_PadBack</a></div><div class="ttdeci">uint32_t m_PadBack</div><div class="ttdoc">Padding back value in the depth dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00607">Descriptors.hpp:607</a></div></div> +<div class="ttc" id="structarmnn_1_1_pad_descriptor_xhtml_a410fa919f78af0f0f100bd1594eca4ab"><div class="ttname"><a href="structarmnn_1_1_pad_descriptor.xhtml#a410fa919f78af0f0f100bd1594eca4ab">armnn::PadDescriptor::m_PadValue</a></div><div class="ttdeci">float m_PadValue</div><div class="ttdoc">Optional value to use for padding, defaults to 0. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01168">Descriptors.hpp:1168</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::DepthwiseConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00678">Descriptors.hpp:678</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::StridedSliceDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01310">Descriptors.hpp:1310</a></div></div> +<div class="ttc" id="structarmnn_1_1_comparison_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_comparison_descriptor.xhtml">armnn::ComparisonDescriptor</a></div><div class="ttdoc">A ComparisonDescriptor for the ComparisonLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00089">Descriptors.hpp:89</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_ae64523937ea910030ad66fee6fddd51f"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae64523937ea910030ad66fee6fddd51f">armnn::DetectionPostProcessDescriptor::m_ScaleX</a></div><div class="ttdeci">float m_ScaleX</div><div class="ttdoc">Center size encoding scale x. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00727">Descriptors.hpp:727</a></div></div> +<div class="ttc" id="structarmnn_1_1_stack_descriptor_xhtml_a2bea87b470268bb0b73457c3733dbc04"><div class="ttname"><a href="structarmnn_1_1_stack_descriptor.xhtml#a2bea87b470268bb0b73457c3733dbc04">armnn::StackDescriptor::m_InputShape</a></div><div class="ttdeci">TensorShape m_InputShape</div><div class="ttdoc">Required shape of all input tensors. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01224">Descriptors.hpp:1224</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#l00496">Descriptors.hpp:496</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#l00383">Descriptors.hpp:383</a></div></div> +<div class="ttc" id="_tf_lite_parser_8hpp_xhtml"><div class="ttname"><a href="_tf_lite_parser_8hpp.xhtml">TfLiteParser.hpp</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaaae77f3ad25595e35b327334d89410054"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaae77f3ad25595e35b327334d89410054">armnn::ActivationFunction::Sqrt</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution3dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00609">Descriptors.hpp:609</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#l00502">Descriptors.hpp:502</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#l00767">Descriptors.hpp:767</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a2d299363c9fc33334c571fa29ca4f58ca1cfdf0e8d0c87a228c1f40d9bee7888b"><div class="ttname"><a href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca1cfdf0e8d0c87a228c1f40d9bee7888b">armnn::ComparisonOperation::Less</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#l00660">Descriptors.hpp:660</a></div></div> +<div class="ttc" id="structarmnn_1_1_reduce_descriptor_xhtml_a28e0548abfc4e79c48f29a3d11a062e9"><div class="ttname"><a href="structarmnn_1_1_reduce_descriptor.xhtml#a28e0548abfc4e79c48f29a3d11a062e9">armnn::ReduceDescriptor::m_KeepDims</a></div><div class="ttdeci">bool m_KeepDims</div><div class="ttdoc">if true then output shape has no change. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01501">Descriptors.hpp:1501</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#l01428">Descriptors.hpp:1428</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a484bafa2f8453a7c5a4a00b92a61b006"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">armnn::LstmInputParams::m_CellToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_CellToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00048">LstmParams.hpp:48</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a78f5b213a603b55d0fabea389e89f203"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a78f5b213a603b55d0fabea389e89f203">armnn::TransposeConvolution2dDescriptor::m_OutputShape</a></div><div class="ttdeci">std::vector< unsigned int > m_OutputShape</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01433">Descriptors.hpp:1433</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_abcbdfb544ece4c31d0b37715ad0f3be0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">armnn::TensorInfo::GetNumBytes</a></div><div class="ttdeci">unsigned int GetNumBytes() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00427">Tensor.cpp:427</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a869254cb56968986a78a79e1d6d4a86b"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">armnn::ResizeDescriptor::m_Method</a></div><div class="ttdeci">ResizeMethod m_Method</div><div class="ttdoc">The Interpolation method to use (Bilinear, NearestNeighbor). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00959">Descriptors.hpp:959</a></div></div> +<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml_a8275d51ef9a584feb95726ea0522f6e5"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">armnn::SoftmaxDescriptor::m_Beta</a></div><div class="ttdeci">float m_Beta</div><div class="ttdoc">Exponentiation value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00170">Descriptors.hpp:170</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser_xhtml_a6bf5861864c8828e59df24a7868c5439"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a6bf5861864c8828e59df24a7868c5439">armnnTfLiteParser::ITfLiteParser::CreateNetworkFromBinaryFile</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromBinaryFile(const char *graphFile)</div><div class="ttdoc">Create the network from a flatbuffers binary file on disk. </div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00074">TfLiteParser.cpp:74</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a9e081a9b94defb30d1558dc912507e0e"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">armnn::LstmInputParams::m_InputGateBias</a></div><div class="ttdeci">const ConstTensor * m_InputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00051">LstmParams.hpp:51</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#l00393">Descriptors.hpp:393</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_a59139faede3a07a43c4c8f86bb03dc43"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a59139faede3a07a43c4c8f86bb03dc43">armnnTfLiteParser::TfLiteParserImpl::GetNetworkOutputBindingInfo</a></div><div class="ttdeci">BindingPointInfo GetNetworkOutputBindingInfo(size_t subgraphId, const std::string &name) const</div><div class="ttdoc">Retrieve binding info (layer id and tensor info) for the network output identified by the given layer...</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l05229">TfLiteParser.cpp:5229</a></div></div> +<div class="ttc" id="structarmnn_1_1_arg_min_max_descriptor_xhtml_ab1ae6f520bb1a4da191a0ae907477f23"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_descriptor.xhtml#ab1ae6f520bb1a4da191a0ae907477f23">armnn::ArgMinMaxDescriptor::m_Function</a></div><div class="ttdeci">ArgMinMaxFunction m_Function</div><div class="ttdoc">Specify if the function is to find Min or Max. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00081">Descriptors.hpp:81</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a7e2f87544b8bc7e497e1dec8d3ca4055"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7e2f87544b8bc7e497e1dec8d3ca4055">armnn::DetectionPostProcessDescriptor::m_DetectionsPerClass</a></div><div class="ttdeci">uint32_t m_DetectionsPerClass</div><div class="ttdoc">Detections per classes, used in Regular NMS. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00717">Descriptors.hpp:717</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a9bc46a2ad56319786bad30be9ab2dc6a"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a9bc46a2ad56319786bad30be9ab2dc6a">armnn::TransposeConvolution2dDescriptor::m_OutputShapeEnabled</a></div><div class="ttdeci">bool m_OutputShapeEnabled</div><div class="ttdoc">Output shape if it has been specified. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01432">Descriptors.hpp:1432</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::BatchToSpaceNdDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00870">Descriptors.hpp:870</a></div></div> +<div class="ttc" id="_tf_lite_parser_8cpp_xhtml_a7c88d54e3f895030c70330a4c9d76a7a"><div class="ttname"><a href="_tf_lite_parser_8cpp.xhtml#a7c88d54e3f895030c70330a4c9d76a7a">CHECK_BUFFER</a></div><div class="ttdeci">#define CHECK_BUFFER(MODEL, BUFFER_INDEX)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00260">TfLiteParser.cpp:260</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser_xhtml"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml">armnnTfLiteParser::ITfLiteParser</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_tf_lite_parser_8hpp_source.xhtml#l00026">ITfLiteParser.hpp:26</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa1e34af023adeb7d5f484f8eb4b9826b6"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa1e34af023adeb7d5f484f8eb4b9826b6">armnn::ActivationFunction::Abs</a></div></div> +<div class="ttc" id="classarmnn_1_1_exception_xhtml_abf843cbb29dec939d0731e491bab6f70"><div class="ttname"><a href="classarmnn_1_1_exception.xhtml#abf843cbb29dec939d0731e491bab6f70">armnn::Exception::what</a></div><div class="ttdeci">virtual const char * what() const noexcept override</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8cpp_source.xhtml#l00032">Exceptions.cpp:32</a></div></div> +<div class="ttc" id="_logging_8hpp_xhtml_a7b6ad073975f437ec38ca7d20154727f"><div class="ttname"><a href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a></div><div class="ttdeci">#define ARMNN_LOG(severity)</div><div class="ttdef"><b>Definition:</b> <a href="_logging_8hpp_source.xhtml#l00212">Logging.hpp:212</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="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#l00379">Descriptors.hpp:379</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a1759754ccb88ecc9af44f3aae6e244ee"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">armnn::LstmInputParams::m_RecurrentToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00046">LstmParams.hpp:46</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a4de71c3661093e5c4ae7775114f43413"><div class="ttname"><a href="namespacearmnn.xhtml#a4de71c3661093e5c4ae7775114f43413">armnn::NetworkOptions</a></div><div class="ttdeci">std::vector< BackendOptions > NetworkOptions</div><div class="ttdef"><b>Definition:</b> <a href="_backend_options_8hpp_source.xhtml#l00016">BackendOptions.hpp:16</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Convolution3dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00603">Descriptors.hpp:603</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_a949070abe70fabe18cc1eb29d5853f47"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a949070abe70fabe18cc1eb29d5853f47">armnnTfLiteParser::TfLiteParserImpl::GetSubgraphOutputTensorNames</a></div><div class="ttdeci">std::vector< std::string > GetSubgraphOutputTensorNames(size_t subgraphId) const</div><div class="ttdoc">Return the output tensor names for a given subgraph. </div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l05279">TfLiteParser.cpp:5279</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution3dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00621">Descriptors.hpp:621</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_a523deabeb7d0a884028b35eebfd1cb6c"><div class="ttname"><a href="namespacearmnn_utils.xhtml#a523deabeb7d0a884028b35eebfd1cb6c">armnnUtils::ProcessConcatInputTensorInfo</a></div><div class="ttdeci">void ProcessConcatInputTensorInfo(armnn::TensorInfo &inputTensorInfo, armnn::OriginsDescriptor &concatDescriptor, const unsigned int &concatAxis, unsigned int inputIndex, unsigned int &mergeDimOrigin)</div><div class="ttdef"><b>Definition:</b> <a href="_parser_helper_8cpp_source.xhtml#l00019">ParserHelper.cpp:19</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::DataType::Signed64</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::NormalizationDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00773">Descriptors.hpp:773</a></div></div> +<div class="ttc" id="structarmnn_1_1_pad_descriptor_xhtml_a85f98c94e11f65a6b73f831735c040f3"><div class="ttname"><a href="structarmnn_1_1_pad_descriptor.xhtml#a85f98c94e11f65a6b73f831735c040f3">armnn::PadDescriptor::m_PadList</a></div><div class="ttdeci">std::vector< std::pair< unsigned int, unsigned int > > m_PadList</div><div class="ttdoc">Specifies the padding for input dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01165">Descriptors.hpp:1165</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_af0f796fba1a2be9c56b4c9ee534577ee"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">armnn::LstmInputParams::m_ForgetLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_ForgetLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00058">LstmParams.hpp:58</a></div></div> +<div class="ttc" id="structarmnn_1_1_reduce_descriptor_xhtml_aa57c67b1da0011b1abb30170146e870f"><div class="ttname"><a href="structarmnn_1_1_reduce_descriptor.xhtml#aa57c67b1da0011b1abb30170146e870f">armnn::ReduceDescriptor::m_ReduceOperation</a></div><div class="ttdeci">ReduceOperation m_ReduceOperation</div><div class="ttdoc">Specifies the reduction operation to execute. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01505">Descriptors.hpp:1505</a></div></div> +<div class="ttc" id="namespacearmnn_tf_lite_parser_xhtml_af69bedce3c37be895f75146016ba8a17"><div class="ttname"><a href="namespacearmnn_tf_lite_parser.xhtml#af69bedce3c37be895f75146016ba8a17">armnnTfLiteParser::ITfLiteParserPtr</a></div><div class="ttdeci">std::unique_ptr< ITfLiteParser, void(*)(ITfLiteParser *parser)> ITfLiteParserPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_tf_lite_parser_8hpp_source.xhtml#l00024">ITfLiteParser.hpp:24</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a35b112e30c3eb153806a2a8c16d178e3"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">armnn::LstmInputParams::m_CellToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_CellToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00049">LstmParams.hpp:49</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_aadad81a95152fe5aad839db352d4012c"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aadad81a95152fe5aad839db352d4012c">armnnTfLiteParser::TfLiteParserImpl::OperatorPtr</a></div><div class="ttdeci">std::unique_ptr< tflite::OperatorT > OperatorPtr</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00032">TfLiteParser.hpp:32</a></div></div> +<div class="ttc" id="namespacearmnn_tf_lite_parser_xhtml_a85c693ec44ec126eb56462857fe634fc"><div class="ttname"><a href="namespacearmnn_tf_lite_parser.xhtml#a85c693ec44ec126eb56462857fe634fc">armnnTfLiteParser::ComputeWrappedIndex</a></div><div class="ttdeci">unsigned int ComputeWrappedIndex(int idx, unsigned int numDimsIn)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l03957">TfLiteParser.cpp:3957</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__quick__start_8dox_source.xhtml#l00006">01_00_quick_start.dox:6</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml">armnnTfLiteParser::TfLiteParserImpl</a></div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00026">TfLiteParser.hpp:26</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &&...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div> +<div class="ttc" id="_lstm_params_8hpp_xhtml"><div class="ttname"><a href="_lstm_params_8hpp.xhtml">LstmParams.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::TransposeConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01422">Descriptors.hpp:1422</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_a61081be1483984e33db452c75d569f51"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a61081be1483984e33db452c75d569f51">armnn::StridedSliceDescriptor::m_BeginMask</a></div><div class="ttdeci">int32_t m_BeginMask</div><div class="ttdoc">Begin mask value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01297">Descriptors.hpp:1297</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a1cfaa710db2a54673b21d2ea2da757c8afb278fa5defd7e699fcbc930c3e76ccd"><div class="ttname"><a href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8afb278fa5defd7e699fcbc930c3e76ccd">armnn::UnaryOperation::Neg</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_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_aaebfa9a01a0bb8a0935114ff0140cc45"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aaebfa9a01a0bb8a0935114ff0140cc45">armnnTfLiteParser::TfLiteParserImpl::OutputShapeOfReshape</a></div><div class="ttdeci">static armnn::TensorInfo OutputShapeOfReshape(const armnn::TensorInfo &inputTensorInfo, const std::vector< int32_t > &targetDimsIn)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02882">TfLiteParser.cpp:2882</a></div></div> +<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml_a490ec6b59006d1fe1ec2ea30e69fb97c"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">armnn::PermutationVector::GetSize</a></div><div class="ttdeci">SizeType GetSize() const</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00338">Types.hpp:338</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_ac37e49c0d6e6e54f9d2015d0f11f8ee7"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#ac37e49c0d6e6e54f9d2015d0f11f8ee7">armnn::StridedSliceDescriptor::m_EndMask</a></div><div class="ttdeci">int32_t m_EndMask</div><div class="ttdoc">End mask value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01300">Descriptors.hpp:1300</a></div></div> +<div class="ttc" id="structarmnn_1_1_space_to_depth_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_space_to_depth_descriptor.xhtml">armnn::SpaceToDepthDescriptor</a></div><div class="ttdoc">A SpaceToDepthDescriptor for the SpaceToDepthLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01022">Descriptors.hpp:1022</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718">armnn::PoolingAlgorithm</a></div><div class="ttdeci">PoolingAlgorithm</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00136">Types.hpp:136</a></div></div> +<div class="ttc" id="classarmnn_1_1_optional_reference_switch_xhtml_a77c7d528ac063d870b8c8426ec81c1c3"><div class="ttname"><a href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">armnn::OptionalReferenceSwitch< std::is_reference< T >::value, T >::value</a></div><div class="ttdeci">const T & value() const</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00146">Optional.hpp:146</a></div></div> +<div class="ttc" id="structarmnn_1_1_space_to_batch_nd_descriptor_xhtml_a85f98c94e11f65a6b73f831735c040f3"><div class="ttname"><a href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml#a85f98c94e11f65a6b73f831735c040f3">armnn::SpaceToBatchNdDescriptor::m_PadList</a></div><div class="ttdeci">std::vector< std::pair< unsigned int, unsigned int > > m_PadList</div><div class="ttdoc">Specifies the padding values for the input dimension: heightPad{top, bottom} widthPad{left, right}. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01016">Descriptors.hpp:1016</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::Convolution3dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation along x axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00615">Descriptors.hpp:615</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::DepthwiseConvolution2dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation factor value for height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00674">Descriptors.hpp:674</a></div></div> +<div class="ttc" id="_filesystem_8hpp_xhtml"><div class="ttname"><a href="_filesystem_8hpp.xhtml">Filesystem.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">armnn::BatchToSpaceNdDescriptor</a></div><div class="ttdoc">A BatchToSpaceNdDescriptor for the BatchToSpaceNdLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00843">Descriptors.hpp:843</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">armnn::BoostLogSeverityMapping::error</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#l00387">Descriptors.hpp:387</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ab8cf8f9fb6792e654c2d8d8382f6f01b"><div class="ttname"><a href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a></div><div class="ttdeci">int LayerBindingId</div><div class="ttdoc">Type of identifiers for bindable layers (inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00290">Types.hpp:290</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a8c0f6d48705f40c5590dde09be262222"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">armnn::LstmInputParams::m_OutputGateBias</a></div><div class="ttdeci">const ConstTensor * m_OutputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00054">LstmParams.hpp:54</a></div></div> +<div class="ttc" id="include_2armnn_tf_lite_parser_2_version_8hpp_xhtml_a88c7ccef0cfc85702b48557ec804ef9a"><div class="ttname"><a href="include_2armnn_tf_lite_parser_2_version_8hpp.xhtml#a88c7ccef0cfc85702b48557ec804ef9a">TFLITE_PARSER_VERSION</a></div><div class="ttdeci">#define TFLITE_PARSER_VERSION</div><div class="ttdoc">TFLITE_PARSER_VERSION: "X.Y.Z" where: X = Major version number Y = Minor version number Z = Patch ver...</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_tf_lite_parser_2_version_8hpp_source.xhtml#l00025">Version.hpp:25</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a1cfaa710db2a54673b21d2ea2da757c8a2a25ebd8c909241e3f7818389b804ecc"><div class="ttname"><a href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8a2a25ebd8c909241e3f7818389b804ecc">armnn::UnaryOperation::LogicalNot</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_convolution3d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution3dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00611">Descriptors.hpp:611</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#l00763">Descriptors.hpp:763</a></div></div> +<div class="ttc" id="_tf_lite_parser_8cpp_xhtml_aa1664dc13adbc85ac12fb584b76bfdae"><div class="ttname"><a href="_tf_lite_parser_8cpp.xhtml#aa1664dc13adbc85ac12fb584b76bfdae">CHECK_TENSOR</a></div><div class="ttdeci">#define CHECK_TENSOR(MODEL, SUBGRAPH_INDEX, TENSOR_INDEX)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00212">TfLiteParser.cpp:212</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a81b5ff8545adad19a1c9d4ca076d552c"><div class="ttname"><a href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">armnn::GetDataTypeName</a></div><div class="ttdeci">constexpr const char * GetDataTypeName(DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00206">TypesUtils.hpp:206</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#l00193">Tensor.hpp:193</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_a32a96909bc8a8ee9076bd4d5c1028301"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a32a96909bc8a8ee9076bd4d5c1028301">armnnTfLiteParser::TfLiteParserImpl::CreateNetworkFromBinary</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromBinary(const std::vector< uint8_t > &binaryContent)</div><div class="ttdoc">Create the network from a flatbuffers binary. </div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00930">TfLiteParser.cpp:930</a></div></div> +<div class="ttc" id="_backend_options_8hpp_xhtml"><div class="ttname"><a href="_backend_options_8hpp.xhtml">BackendOptions.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml">armnn::ResizeDescriptor</a></div><div class="ttdoc">A ResizeDescriptor for the ResizeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00932">Descriptors.hpp:932</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_a720f0206e377dd1808acc94b3b12c538"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a720f0206e377dd1808acc94b3b12c538">armnnTfLiteParser::TfLiteParserImpl::GetBuffer</a></div><div class="ttdeci">static BufferRawPtr GetBuffer(const ModelPtr &model, size_t bufferIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l05019">TfLiteParser.cpp:5019</a></div></div> +<div class="ttc" id="structarmnn_1_1_l2_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::L2NormalizationDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00792">Descriptors.hpp:792</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a9ae2c9796692ebeafe19a4d3f09c8ea8"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a9ae2c9796692ebeafe19a4d3f09c8ea8">armnn::DetectionPostProcessDescriptor::m_MaxClassesPerDetection</a></div><div class="ttdeci">uint32_t m_MaxClassesPerDetection</div><div class="ttdoc">Maximum numbers of classes per detection, used in Fast NMS. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00715">Descriptors.hpp:715</a></div></div> +<div class="ttc" id="structarmnn_1_1_mean_descriptor_xhtml_a1f0d67b087c491248bd1cde3ff995a95"><div class="ttname"><a href="structarmnn_1_1_mean_descriptor.xhtml#a1f0d67b087c491248bd1cde3ff995a95">armnn::MeanDescriptor::m_Axis</a></div><div class="ttdeci">std::vector< unsigned int > m_Axis</div><div class="ttdoc">Values for the dimensions to reduce. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01137">Descriptors.hpp:1137</a></div></div> +<div class="ttc" id="structarmnn_1_1_stack_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_stack_descriptor.xhtml">armnn::StackDescriptor</a></div><div class="ttdoc">A StackDescriptor for the StackLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01198">Descriptors.hpp:1198</a></div></div> +<div class="ttc" id="structarmnn_1_1_space_to_batch_nd_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::SpaceToBatchNdDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01018">Descriptors.hpp:1018</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a1cfaa710db2a54673b21d2ea2da757c8a0986d137604183312e6d3599578bc6cd"><div class="ttname"><a href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8a0986d137604183312e6d3599578bc6cd">armnn::UnaryOperation::Sin</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a6dac966f265381903c8ef4f392becced"><div class="ttname"><a href="namespacearmnn.xhtml#a6dac966f265381903c8ef4f392becced">armnn::GetUnaryOperationAsCString</a></div><div class="ttdeci">constexpr char const * GetUnaryOperationAsCString(UnaryOperation operation)</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00075">TypesUtils.hpp:75</a></div></div> +<div class="ttc" id="structarmnn_1_1_reshape_descriptor_xhtml_a1178f4dafdda81f59c15145ec327f7d9"><div class="ttname"><a href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">armnn::ReshapeDescriptor::m_TargetShape</a></div><div class="ttdeci">TensorShape m_TargetShape</div><div class="ttdoc">Target shape value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00986">Descriptors.hpp:986</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_aed935c554e4f6a4e7b9dcde057d00877"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aed935c554e4f6a4e7b9dcde057d00877">armnnTfLiteParser::TfLiteParserImpl::CreateNetworkFromBinaryFile</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromBinaryFile(const char *graphFile)</div><div class="ttdoc">Create the network from a flatbuffers binary file on disk. </div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00923">TfLiteParser.cpp:923</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#l00385">Descriptors.hpp:385</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_ae72089bcab60ac175557f4241b16a014"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae72089bcab60ac175557f4241b16a014">armnn::DetectionPostProcessDescriptor::m_MaxDetections</a></div><div class="ttdeci">uint32_t m_MaxDetections</div><div class="ttdoc">Maximum numbers of detections. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00713">Descriptors.hpp:713</a></div></div> +<div class="ttc" id="structarmnn_1_1_pad_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a></div><div class="ttdoc">A PadDescriptor for the PadLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01143">Descriptors.hpp:1143</a></div></div> +<div class="ttc" id="namespacearmnn_onnx_parser_xhtml_a503ae4f55dae1486e53978657083b35d"><div class="ttname"><a href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">armnnOnnxParser::ModelPtr</a></div><div class="ttdeci">std::unique_ptr< onnx::ModelProto > ModelPtr</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8hpp_source.xhtml#l00023">OnnxParser.hpp:23</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a0cd848f65ec31778d708852f0042fe37"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">armnn::LstmInputParams::m_InputLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_InputLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00057">LstmParams.hpp:57</a></div></div> +<div class="ttc" id="_tf_lite_parser_8cpp_xhtml_a315ccf3e3cb207b1fbd10a2ad3e6333a"><div class="ttname"><a href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a></div><div class="ttdeci">#define CHECK_SUBGRAPH(MODEL, SUBGRAPH_INDEX)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00141">TfLiteParser.cpp:141</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a2d299363c9fc33334c571fa29ca4f58c"><div class="ttname"><a href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58c">armnn::ComparisonOperation</a></div><div class="ttdeci">ComparisonOperation</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00108">Types.hpp:108</a></div></div> +<div class="ttc" id="_types_utils_8hpp_xhtml"><div class="ttname"><a href="_types_utils_8hpp.xhtml">TypesUtils.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml">armnn::LstmInputParamsInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00063">LstmParams.hpp:63</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::DepthwiseConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00668">Descriptors.hpp:668</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="namespacearmnn_xhtml_abc05539fc6e7907f32ef0fb242e3b3b0"><div class="ttname"><a href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0">armnn::ReduceOperation</a></div><div class="ttdeci">ReduceOperation</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00143">Types.hpp:143</a></div></div> +<div class="ttc" id="_permute_8hpp_xhtml"><div class="ttname"><a href="_permute_8hpp.xhtml">Permute.hpp</a></div></div> +<div class="ttc" id="namespacearmnn__driver_xhtml_a923b39ec5f83672f50289cbaf5de6798"><div class="ttname"><a href="namespacearmnn__driver.xhtml#a923b39ec5f83672f50289cbaf5de6798">armnn_driver::Model</a></div><div class="ttdeci">::android::nn::Model Model</div><div class="ttdoc">Helper classes. </div><div class="ttdef"><b>Definition:</b> <a href="_conversion_utils_8hpp_source.xhtml#l00043">ConversionUtils.hpp:43</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_a913d452e7766210b59a69d3c658b2862"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a913d452e7766210b59a69d3c658b2862">armnnTfLiteParser::TfLiteParserImpl::GetNetworkInputBindingInfo</a></div><div class="ttdeci">BindingPointInfo GetNetworkInputBindingInfo(size_t subgraphId, const std::string &name) const</div><div class="ttdoc">Retrieve 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="_tf_lite_parser_8cpp_source.xhtml#l05197">TfLiteParser.cpp:5197</a></div></div> +<div class="ttc" id="namespacearmnn_deserializer_xhtml_a97b50f22cd99f0e09e6e48d20a35f6b2"><div class="ttname"><a href="namespacearmnn_deserializer.xhtml#a97b50f22cd99f0e09e6e48d20a35f6b2">armnnDeserializer::CheckShape</a></div><div class="ttdeci">bool CheckShape(const armnn::TensorShape &actual, const std::vector< uint32_t > &expected)</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8cpp_source.xhtml#l00188">Deserializer.cpp:188</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_a7f3fc56b7306b444f06f45cdc4032bbf"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a7f3fc56b7306b444f06f45cdc4032bbf">armnnTfLiteParser::TfLiteParserImpl::LoadModelFromBinary</a></div><div class="ttdeci">static ModelPtr LoadModelFromBinary(const uint8_t *binaryContent, size_t len)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l04699">TfLiteParser.cpp:4699</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00048">Types.hpp:48</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a53c8a7f33a40e1e240256bcfcf41b101"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a53c8a7f33a40e1e240256bcfcf41b101">armnn::DetectionPostProcessDescriptor::m_NmsIouThreshold</a></div><div class="ttdeci">float m_NmsIouThreshold</div><div class="ttdoc">Intersection over union threshold. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00721">Descriptors.hpp:721</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a56b81ca8ba4b4937e0787e4951f043fc"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">armnn::LstmInputParams::m_RecurrentToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00047">LstmParams.hpp:47</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a></div><div class="ttdoc">An LstmDescriptor for the LstmLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01049">Descriptors.hpp:1049</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#l00377">Descriptors.hpp:377</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_a86428e0c674542488c7292dfbe2ce381"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a86428e0c674542488c7292dfbe2ce381">armnnTfLiteParser::TfLiteParserImpl::TensorIdRawPtrVector</a></div><div class="ttdeci">std::vector< TensorIdRawPtr > TensorIdRawPtrVector</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00038">TfLiteParser.hpp:38</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::DepthwiseConvolution2dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation factor value for width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00672">Descriptors.hpp:672</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::DepthwiseConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00664">Descriptors.hpp:664</a></div></div> +<div class="ttc" id="structarmnn_1_1_check_location_xhtml_ac1184445a1323e07e0da084a54aec535"><div class="ttname"><a href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">armnn::CheckLocation::FileLine</a></div><div class="ttdeci">std::string FileLine() const</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00037">Exceptions.hpp:37</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="_assert_8hpp_xhtml_a91c4dfde57907d7698c7531785690a7f"><div class="ttname"><a href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a></div><div class="ttdeci">#define ARMNN_ASSERT_MSG(COND, MSG)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00015">Assert.hpp:15</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_a7c91eda2b331d607bae92cd8ebf50bb9"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a7c91eda2b331d607bae92cd8ebf50bb9">armnn::StridedSliceDescriptor::m_NewAxisMask</a></div><div class="ttdeci">int32_t m_NewAxisMask</div><div class="ttdoc">New axis mask value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01307">Descriptors.hpp:1307</a></div></div> +<div class="ttc" id="structarmnn_1_1_mean_descriptor_xhtml_a28e0548abfc4e79c48f29a3d11a062e9"><div class="ttname"><a href="structarmnn_1_1_mean_descriptor.xhtml#a28e0548abfc4e79c48f29a3d11a062e9">armnn::MeanDescriptor::m_KeepDims</a></div><div class="ttdeci">bool m_KeepDims</div><div class="ttdoc">Enable/disable keep dimensions. If true, then the reduced dimensions that are of length 1 are kept...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01139">Descriptors.hpp:1139</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_a8b4b0b88a5e79a88b8b60db76398f575"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">armnnTfLiteParser::TfLiteParserImpl::GetInputTensorIds</a></div><div class="ttdeci">static std::vector< int32_t > & GetInputTensorIds(const ModelPtr &model, size_t subgraphIndex, size_t operatorIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l04798">TfLiteParser.cpp:4798</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml_a02e143524aefddd40b485fcf7dea6696"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">armnn::BatchToSpaceNdDescriptor::m_BlockShape</a></div><div class="ttdeci">std::vector< unsigned int > m_BlockShape</div><div class="ttdoc">Block shape values. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00866">Descriptors.hpp:866</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#l00041">INetwork.hpp:41</a></div></div> +<div class="ttc" id="structarmnn_1_1_l2_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_descriptor.xhtml">armnn::L2NormalizationDescriptor</a></div><div class="ttdoc">A L2NormalizationDescriptor for the L2NormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00777">Descriptors.hpp:777</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a44b0e6b16708df7f0d2bbab141688aaa"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">armnn::LstmInputParams::m_ProjectionBias</a></div><div class="ttdeci">const ConstTensor * m_ProjectionBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00056">LstmParams.hpp:56</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">armnn::TensorInfo::GetQuantizationOffset</a></div><div class="ttdeci">int32_t GetQuantizationOffset() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00478">Tensor.cpp:478</a></div></div> +<div class="ttc" id="structarmnn_1_1_arg_min_max_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_descriptor.xhtml">armnn::ArgMinMaxDescriptor</a></div><div class="ttdoc">An ArgMinMaxDescriptor for ArgMinMaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00067">Descriptors.hpp:67</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_aa09a8bb02eed50715082d8b7fccd2f8d"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#aa09a8bb02eed50715082d8b7fccd2f8d">armnnTfLiteParser::TfLiteParserImpl::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="_tf_lite_parser_8cpp_source.xhtml#l05292">TfLiteParser.cpp:5292</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00461">Tensor.cpp:461</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00198">Tensor.hpp:198</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#l00181">Descriptors.hpp:181</a></div></div> +<div class="ttc" id="structarmnn_1_1_reduce_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_reduce_descriptor.xhtml">armnn::ReduceDescriptor</a></div><div class="ttdoc">A ReduceDescriptor for the REDUCE operators. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01485">Descriptors.hpp:1485</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div> +<div class="ttc" id="classarmnn_1_1_optional_base_xhtml_a86b749ce2c4bc627fa8a1fcfaf0e314f"><div class="ttname"><a href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">armnn::OptionalBase::has_value</a></div><div class="ttdeci">bool has_value() const noexcept</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00053">Optional.hpp:53</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#l00475">Descriptors.hpp:475</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_af996d82c47e43a16f4c8faa6c6b3e030"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#af996d82c47e43a16f4c8faa6c6b3e030">armnn::StridedSliceDescriptor::m_EllipsisMask</a></div><div class="ttdeci">int32_t m_EllipsisMask</div><div class="ttdoc">Ellipsis mask value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01304">Descriptors.hpp:1304</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#l00494">Descriptors.hpp:494</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_af9590c2ae8d68b5da999d3d6ab21dd3b"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#af9590c2ae8d68b5da999d3d6ab21dd3b">armnnTfLiteParser::TfLiteParserImpl::LoadModelFromFile</a></div><div class="ttdeci">static ModelPtr LoadModelFromFile(const char *fileName)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l04675">TfLiteParser.cpp:4675</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_info_xhtml_a4b3f6e6f2268416ffd7a34fda95ffd0b"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params_info.xhtml#a4b3f6e6f2268416ffd7a34fda95ffd0b">armnn::LstmInputParamsInfo::m_InputToForgetWeights</a></div><div class="ttdeci">const TensorInfo * m_InputToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00090">LstmParams.hpp:90</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">armnn::LayerType::Permute</a></div></div> +<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00327">Tensor.hpp:327</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_ac93cb1365b4bcb67df2a3164606096c5"><div class="ttname"><a href="namespacearmnn_utils.xhtml#ac93cb1365b4bcb67df2a3164606096c5">armnnUtils::GetUnsignedAxis</a></div><div class="ttdeci">unsigned int GetUnsignedAxis(const unsigned int inputDimension, const int axis)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.xhtml#l00196">TensorUtils.cpp:196</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a></div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00013">LstmParams.hpp:13</a></div></div> +<div class="ttc" id="structarmnn_1_1_check_location_xhtml"><div class="ttname"><a href="structarmnn_1_1_check_location.xhtml">armnn::CheckLocation</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00014">Exceptions.hpp:14</a></div></div> +<div class="ttc" id="structarmnn_1_1_gather_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_gather_descriptor.xhtml">armnn::GatherDescriptor</a></div><div class="ttdoc">A GatherDescriptor for the GatherLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00912">Descriptors.hpp:912</a></div></div> +<div class="ttc" id="_verification_helpers_8hpp_xhtml_a479b2821a7a2cbb8fa8eb7f60a47065d"><div class="ttname"><a href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a></div><div class="ttdeci">#define CHECK_VALID_SIZE(ACTUAL,...)</div><div class="ttdef"><b>Definition:</b> <a href="_verification_helpers_8hpp_source.xhtml#l00032">VerificationHelpers.hpp:32</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a3a04b0ccee4bb2f21721ee5045e83df4"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a3a04b0ccee4bb2f21721ee5045e83df4">armnn::DetectionPostProcessDescriptor::m_NumClasses</a></div><div class="ttdeci">uint32_t m_NumClasses</div><div class="ttdoc">Number of classes. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00723">Descriptors.hpp:723</a></div></div> +<div class="ttc" id="_verification_helpers_8hpp_xhtml_aaef93dc9a69f51b59f3cdd0ff0165927"><div class="ttname"><a href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a></div><div class="ttdeci">#define CHECKED_NON_NEGATIVE(VALUE)</div><div class="ttdef"><b>Definition:</b> <a href="_verification_helpers_8hpp_source.xhtml#l00035">VerificationHelpers.hpp:35</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a2d299363c9fc33334c571fa29ca4f58ca19bb0af2c3c530538cb41aff7f235b96"><div class="ttname"><a href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca19bb0af2c3c530538cb41aff7f235b96">armnn::ComparisonOperation::NotEqual</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_abd8bee7fb9b86485a60bc7ee05114270"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#abd8bee7fb9b86485a60bc7ee05114270">armnnTfLiteParser::TfLiteParserImpl::TensorRawPtrVector</a></div><div class="ttdeci">std::vector< TensorRawPtr > TensorRawPtrVector</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00036">TfLiteParser.hpp:36</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a22b3f11f56aa924c0c1b6ee4d6cc90e1"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a22b3f11f56aa924c0c1b6ee4d6cc90e1">armnn::IOutputSlot::GetOwningIConnectableLayer</a></div><div class="ttdeci">virtual const IConnectableLayer & GetOwningIConnectableLayer() const =0</div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_a1b7aed8466cc5792966d271a65285d0e"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a1b7aed8466cc5792966d271a65285d0e">armnnTfLiteParser::TfLiteParserImpl::GetSubgraphCount</a></div><div class="ttdeci">size_t GetSubgraphCount() const</div><div class="ttdoc">Return the number of subgraphs in the parsed model. </div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l05261">TfLiteParser.cpp:5261</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::TransposeConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01420">Descriptors.hpp:1420</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="structarmnn_1_1_stand_in_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_stand_in_descriptor.xhtml">armnn::StandInDescriptor</a></div><div class="ttdoc">A StandInDescriptor for the StandIn layer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01228">Descriptors.hpp:1228</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a7ed9bc7c26df67d274d5dd4cd83adf0f"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7ed9bc7c26df67d274d5dd4cd83adf0f">armnn::DetectionPostProcessDescriptor::m_UseRegularNms</a></div><div class="ttdeci">bool m_UseRegularNms</div><div class="ttdoc">Use Regular NMS. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00725">Descriptors.hpp:725</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_a83ca447892f460dabaa2f87d3dc3db61"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#a83ca447892f460dabaa2f87d3dc3db61">armnn::Convolution3dDescriptor::m_PadFront</a></div><div class="ttdeci">uint32_t m_PadFront</div><div class="ttdoc">Padding front value in the depth dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00605">Descriptors.hpp:605</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::TransposeConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01430">Descriptors.hpp:1430</a></div></div> +<div class="ttc" id="structarmnn_1_1_space_to_batch_nd_descriptor_xhtml_a02e143524aefddd40b485fcf7dea6696"><div class="ttname"><a href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">armnn::SpaceToBatchNdDescriptor::m_BlockShape</a></div><div class="ttdeci">std::vector< unsigned int > m_BlockShape</div><div class="ttdoc">Block shape value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01013">Descriptors.hpp:1013</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_a0d53caff836b84204adbd1c28752a201"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a0d53caff836b84204adbd1c28752a201">armnn::StridedSliceDescriptor::m_Stride</a></div><div class="ttdeci">std::vector< int > m_Stride</div><div class="ttdoc">Stride values for the input that will be sliced. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01293">Descriptors.hpp:1293</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a></div><div class="ttdoc">An ActivationDescriptor for the ActivationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00036">Descriptors.hpp:36</a></div></div> +<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_a8aeddebdcf02e1832b22203c08a6b678"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">armnn::BaseTensor::GetInfo</a></div><div class="ttdeci">const TensorInfo & GetInfo() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00295">Tensor.hpp:295</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#l00203">Exceptions.hpp:203</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a71975fcec1464d639f1a78f73164d1bd"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a71975fcec1464d639f1a78f73164d1bd">armnn::TensorInfo::SetDataType</a></div><div class="ttdeci">void SetDataType(DataType type)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00199">Tensor.hpp:199</a></div></div> +<div class="ttc" id="structarmnn_1_1_stack_descriptor_xhtml_aed6086070440ceb94129bef06f70173f"><div class="ttname"><a href="structarmnn_1_1_stack_descriptor.xhtml#aed6086070440ceb94129bef06f70173f">armnn::StackDescriptor::m_NumInputs</a></div><div class="ttdeci">uint32_t m_NumInputs</div><div class="ttdoc">Number of input tensors. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01222">Descriptors.hpp:1222</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Convolution3dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00597">Descriptors.hpp:597</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_ae1b07ed928036004bd257169e5aeeef4"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">armnn::LstmDescriptor::m_ActivationFunc</a></div><div class="ttdeci">uint32_t m_ActivationFunc</div><div class="ttdoc">The activation function to use. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01087">Descriptors.hpp:1087</a></div></div> +<div class="ttc" id="structarmnn_1_1_slice_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_slice_descriptor.xhtml">armnn::SliceDescriptor</a></div><div class="ttdoc">A SliceDescriptor for the SliceLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01175">Descriptors.hpp:1175</a></div></div> +<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00295">Types.hpp:295</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_a0da1e3b0281fa84f8b6f5ce3cd15b7c0"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a0da1e3b0281fa84f8b6f5ce3cd15b7c0">armnnTfLiteParser::TfLiteParserImpl::LoadModel</a></div><div class="ttdeci">armnn::INetworkPtr LoadModel(std::unique_ptr< tflite::ModelT > model)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00938">TfLiteParser.cpp:938</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml">armnn::Convolution3dDescriptor</a></div><div class="ttdoc">A Convolution3dDescriptor for the Convolution3dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00556">Descriptors.hpp:556</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_a988cb5e216eb87d854414c6a0282eeb4"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a988cb5e216eb87d854414c6a0282eeb4">armnnTfLiteParser::TfLiteParserImpl::SubgraphPtr</a></div><div class="ttdeci">std::unique_ptr< tflite::SubGraphT > SubgraphPtr</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00031">TfLiteParser.hpp:31</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Convolution3dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00599">Descriptors.hpp:599</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_adceb04ae84c524e4d01881e3754a4d59"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">armnn::IConnectableLayer::GetType</a></div><div class="ttdeci">virtual LayerType GetType() const =0</div><div class="ttdoc">Returns the armnn::LayerType of this layer. </div></div> +<div class="ttc" id="structarmnn_1_1_batch_mat_mul_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_mat_mul_descriptor.xhtml">armnn::BatchMatMulDescriptor</a></div><div class="ttdoc">A BatchMatMulDescriptor for the BatchMatMul operator. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01531">Descriptors.hpp:1531</a></div></div> +<div class="ttc" id="structarmnn_1_1_pad_descriptor_xhtml_a66f2c989f51ab6116de4380390250b69"><div class="ttname"><a href="structarmnn_1_1_pad_descriptor.xhtml#a66f2c989f51ab6116de4380390250b69">armnn::PadDescriptor::m_PaddingMode</a></div><div class="ttdeci">PaddingMode m_PaddingMode</div><div class="ttdoc">Specifies the Padding mode (Constant, Reflect or Symmetric) </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01171">Descriptors.hpp:1171</a></div></div> +<div class="ttc" id="_logging_8hpp_xhtml"><div class="ttname"><a href="_logging_8hpp.xhtml">Logging.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Pooling2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00395">Descriptors.hpp:395</a></div></div> +<div class="ttc" id="_tf_lite_parser_8cpp_xhtml_ae38d96fe05581ea025713b3e781c5a43"><div class="ttname"><a href="_tf_lite_parser_8cpp.xhtml#ae38d96fe05581ea025713b3e781c5a43">CHECK_TENSOR_PTR</a></div><div class="ttdeci">#define CHECK_TENSOR_PTR(TENSOR_PTR)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00225">TfLiteParser.cpp:225</a></div></div> +<div class="ttc" id="_parser_helper_8hpp_xhtml"><div class="ttname"><a href="_parser_helper_8hpp.xhtml">ParserHelper.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_reduce_descriptor_xhtml_aa1c6fc8c96404252f1072632fc5acb59"><div class="ttname"><a href="structarmnn_1_1_reduce_descriptor.xhtml#aa1c6fc8c96404252f1072632fc5acb59">armnn::ReduceDescriptor::m_vAxis</a></div><div class="ttdeci">std::vector< uint32_t > m_vAxis</div><div class="ttdoc">The indices of the dimensions to reduce. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01503">Descriptors.hpp:1503</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_aa61510cbd529870182e918ac6e8b9d72"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#aa61510cbd529870182e918ac6e8b9d72">armnn::DetectionPostProcessDescriptor::m_ScaleH</a></div><div class="ttdeci">float m_ScaleH</div><div class="ttdoc">Center size encoding scale height. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00733">Descriptors.hpp:733</a></div></div> +<div class="ttc" id="structarmnn_1_1_comparison_descriptor_xhtml_a865dc4f43cb0ff01a1dcf78036912fd1"><div class="ttname"><a href="structarmnn_1_1_comparison_descriptor.xhtml#a865dc4f43cb0ff01a1dcf78036912fd1">armnn::ComparisonDescriptor::m_Operation</a></div><div class="ttdeci">ComparisonOperation m_Operation</div><div class="ttdoc">Specifies the comparison operation to execute. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00105">Descriptors.hpp:105</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_aa68194dd6258ab5b04123005a066ea25"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#aa68194dd6258ab5b04123005a066ea25">armnn::StridedSliceDescriptor::m_End</a></div><div class="ttdeci">std::vector< int > m_End</div><div class="ttdoc">End values for the input that will be sliced. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01291">Descriptors.hpp:1291</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ad0b8c32bb5381f4cc999093ba3b98b43"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">armnn::LstmInputParams::m_CellLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_CellLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00059">LstmParams.hpp:59</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ace7a1f1f1041b412b7d8ef82b95ff352"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">armnn::LstmInputParams::m_ForgetGateBias</a></div><div class="ttdeci">const ConstTensor * m_ForgetGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00052">LstmParams.hpp:52</a></div></div> +<div class="ttc" id="structarmnn_1_1_space_to_batch_nd_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml">armnn::SpaceToBatchNdDescriptor</a></div><div class="ttdoc">A SpaceToBatchNdDescriptor for the SpaceToBatchNdLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00990">Descriptors.hpp:990</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a49e11acda22742cbaf6f1b259ead0d84"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">armnn::LstmInputParams::m_InputToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00042">LstmParams.hpp:42</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_a20518e431cb501b172371b850e6c3ec7"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a20518e431cb501b172371b850e6c3ec7">armnnTfLiteParser::TfLiteParserImpl::GetSubgraphInputs</a></div><div class="ttdeci">static TensorIdRawPtrVector GetSubgraphInputs(const ModelPtr &model, size_t subgraphIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l04765">TfLiteParser.cpp:4765</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Convolution3dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NDHWC, NCDHW). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00623">Descriptors.hpp:623</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a6e30c7b3451da3ea9cf4259fb602e6e6"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">armnn::LstmInputParams::m_InputToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00043">LstmParams.hpp:43</a></div></div> +<div class="ttc" id="structarmnn_1_1_backend_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_backend_options.xhtml">armnn::BackendOptions</a></div><div class="ttdoc">Struct for the users to pass backend specific options. </div><div class="ttdef"><b>Definition:</b> <a href="_backend_options_8hpp_source.xhtml#l00022">BackendOptions.hpp:22</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#l00761">Descriptors.hpp:761</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#l00061">Descriptors.hpp:61</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_a342a58ab95b91af13b24dd4bdc7e09bb"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a342a58ab95b91af13b24dd4bdc7e09bb">armnnTfLiteParser::TfLiteParserImpl::GetInputs</a></div><div class="ttdeci">static TensorRawPtrVector GetInputs(const ModelPtr &model, size_t subgraphIndex, size_t operatorIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l04718">TfLiteParser.cpp:4718</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="namespacearmnn_deserializer_xhtml_a80888061963ddd18e87105807a035d9a"><div class="ttname"><a href="namespacearmnn_deserializer.xhtml#a80888061963ddd18e87105807a035d9a">armnnDeserializer::TensorRawPtr</a></div><div class="ttdeci">const armnnSerializer::TensorInfo * TensorRawPtr</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8hpp_source.xhtml#l00020">Deserializer.hpp:20</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_a279d0891900c191d5a0139e5c11fe488"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a279d0891900c191d5a0139e5c11fe488">armnnTfLiteParser::TfLiteParserImpl::GetOutputs</a></div><div class="ttdeci">static TensorRawPtrVector GetOutputs(const ModelPtr &model, size_t subgraphIndex, size_t operatorIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l04745">TfLiteParser.cpp:4745</a></div></div> +<div class="ttc" id="namespacearmnn_onnx_parser_xhtml_ae89f792279f0d06b6c164a6f1c7529e1"><div class="ttname"><a href="namespacearmnn_onnx_parser.xhtml#ae89f792279f0d06b6c164a6f1c7529e1">armnnOnnxParser::CreateConstTensorImpl</a></div><div class="ttdeci">std::pair< armnn::ConstTensor, std::unique_ptr< T[]> > CreateConstTensorImpl(const T *bufferPtr, armnn::TensorInfo &tensorInfo, const armnn::Optional< armnn::PermutationVector &> permutationVector)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.xhtml#l00577">OnnxParser.cpp:577</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#l01416">Descriptors.hpp:1416</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="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#l01424">Descriptors.hpp:1424</a></div></div> +<div class="ttc" id="_layer_support_8hpp_xhtml"><div class="ttname"><a href="_layer_support_8hpp.xhtml">LayerSupport.hpp</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_afe6c475f92d02dd1eb12acd746e4736f"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe6c475f92d02dd1eb12acd746e4736f">armnnTfLiteParser::TfLiteParserImpl::GetOutputTensorIds</a></div><div class="ttdeci">static std::vector< int32_t > & GetOutputTensorIds(const ModelPtr &model, size_t subgraphIndex, size_t operatorIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l04808">TfLiteParser.cpp:4808</a></div></div> +<div class="ttc" id="_tf_lite_parser_8cpp_xhtml_ac578671d13bca92fee7f492110247cbf"><div class="ttname"><a href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a></div><div class="ttdeci">#define CHECK_SUPPORTED_FUSED_ACTIVATION(OPTION, SUBGRAPH_INDEX, OPERATOR_INDEX)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00352">TfLiteParser.cpp:352</a></div></div> +<div class="ttc" id="structarmnn_1_1_gather_descriptor_xhtml_a35d11c7d509d1adbae1ae01c58394a7f"><div class="ttname"><a href="structarmnn_1_1_gather_descriptor.xhtml#a35d11c7d509d1adbae1ae01c58394a7f">armnn::GatherDescriptor::m_Axis</a></div><div class="ttdeci">int32_t m_Axis</div><div class="ttdoc">The axis in params to gather indices from. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00928">Descriptors.hpp:928</a></div></div> +<div class="ttc" id="structarmnn_1_1_elementwise_unary_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_elementwise_unary_descriptor.xhtml">armnn::ElementwiseUnaryDescriptor</a></div><div class="ttdoc">A ElementwiseUnaryDescriptor for the ElementwiseUnaryLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00109">Descriptors.hpp:109</a></div></div> +<div class="ttc" id="_tensor_utils_8hpp_xhtml"><div class="ttname"><a href="_tensor_utils_8hpp.xhtml">TensorUtils.hpp</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#l00373">Descriptors.hpp:373</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_a573c6170ca8451709e031b6243bc187d"><div class="ttname"><a href="namespacearmnn_utils.xhtml#a573c6170ca8451709e031b6243bc187d">armnnUtils::ToFloatArray</a></div><div class="ttdeci">std::unique_ptr< float[]> ToFloatArray(const std::vector< PrimitiveType > &data, const armnn::TensorInfo &tensorInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.xhtml#l00248">TensorUtils.cpp:248</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ae83131e16df1cace69395a5f99bc5ecb"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">armnn::LstmInputParams::m_RecurrentToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00045">LstmParams.hpp:45</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::DepthwiseConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00670">Descriptors.hpp:670</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#l01426">Descriptors.hpp:1426</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml_a3941f674c071c9503e00d2b59e92e454"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a3941f674c071c9503e00d2b59e92e454">armnn::BatchToSpaceNdDescriptor::m_Crops</a></div><div class="ttdeci">std::vector< std::pair< unsigned int, unsigned int > > m_Crops</div><div class="ttdoc">The values to crop from the input dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00868">Descriptors.hpp:868</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div> +<div class="ttc" id="_descriptors_8hpp_xhtml"><div class="ttname"><a href="_descriptors_8hpp.xhtml">Descriptors.hpp</a></div></div> +<div class="ttc" id="_exceptions_8hpp_xhtml"><div class="ttname"><a href="_exceptions_8hpp.xhtml">Exceptions.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Convolution3dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00601">Descriptors.hpp:601</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorShape::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdoc">Function that returns the tensor rank. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00174">Tensor.cpp:174</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ae7e8cbf71db6a490789ca6dcaa8deeae"><div class="ttname"><a href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeae">armnn::ArgMinMaxFunction</a></div><div class="ttdeci">ArgMinMaxFunction</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00102">Types.hpp:102</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_abc05539fc6e7907f32ef0fb242e3b3b0a362a33c23b08e4a32a4ec53fbb82cccd"><div class="ttname"><a href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0a362a33c23b08e4a32a4ec53fbb82cccd">armnn::ReduceOperation::Prod</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#l00391">Descriptors.hpp:391</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_aabb76a77e95921785f576bb29b495cd8"><div class="ttname"><a href="namespacearmnn.xhtml#aabb76a77e95921785f576bb29b495cd8">armnn::GetComparisonOperationAsCString</a></div><div class="ttdeci">constexpr char const * GetComparisonOperationAsCString(ComparisonOperation operation)</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00061">TypesUtils.hpp:61</a></div></div> +<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml_a5b192c5fcd96a0f75542524cf646b355"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#a5b192c5fcd96a0f75542524cf646b355">armnn::OriginsDescriptor::SetConcatAxis</a></div><div class="ttdeci">void SetConcatAxis(unsigned int concatAxis)</div><div class="ttdoc">Set the concatenation axis value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00158">Descriptors.cpp:158</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="namespacearmnn_xhtml_a9a2af2f8c4af4f9efa8e79417d505ac4"><div class="ttname"><a href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4">armnn::ResizeMethod</a></div><div class="ttdeci">ResizeMethod</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00152">Types.hpp:152</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a435d3651482bbfcc11263b4e4e0c900f"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">armnn::LstmInputParams::m_RecurrentToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00044">LstmParams.hpp:44</a></div></div> +<div class="ttc" id="structarmnn_1_1_mean_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_mean_descriptor.xhtml">armnn::MeanDescriptor</a></div><div class="ttdoc">A MeanDescriptor for the MeanLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01119">Descriptors.hpp:1119</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8ffca1e21bdfa7f945617acd606aac91"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">armnn::TensorInfo::SetConstant</a></div><div class="ttdeci">void SetConstant(const bool IsConstant=true)</div><div class="ttdoc">Marks the data corresponding to this tensor info as constant. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00514">Tensor.cpp:514</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a1cfaa710db2a54673b21d2ea2da757c8"><div class="ttname"><a href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8">armnn::UnaryOperation</a></div><div class="ttdeci">UnaryOperation</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00124">Types.hpp:124</a></div></div> +<div class="ttc" id="namespacearmnn_tf_lite_parser_xhtml_a9084adbf804022c874039ad40d1939e9"><div class="ttname"><a href="namespacearmnn_tf_lite_parser.xhtml#a9084adbf804022c874039ad40d1939e9">armnnTfLiteParser::BindingPointInfo</a></div><div class="ttdeci">armnn::BindingPointInfo BindingPointInfo</div><div class="ttdef"><b>Definition:</b> <a href="_i_tf_lite_parser_8hpp_source.xhtml#l00020">ITfLiteParser.hpp:20</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_deserializer_xhtml_a948b8c615ff06defa3b80d2352259ff2"><div class="ttname"><a href="namespacearmnn_deserializer.xhtml#a948b8c615ff06defa3b80d2352259ff2">armnnDeserializer::ToTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo ToTensorInfo(TensorRawPtr tensorPtr)</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8cpp_source.xhtml#l00616">Deserializer.cpp:616</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#l01418">Descriptors.hpp:1418</a></div></div> +<div class="ttc" id="namespacearmnn_tf_lite_parser_xhtml"><div class="ttname"><a href="namespacearmnn_tf_lite_parser.xhtml">armnnTfLiteParser</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_tf_lite_parser_8hpp_source.xhtml#l00017">ITfLiteParser.hpp:17</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_descriptor.xhtml">armnn::TransposeDescriptor</a></div><div class="ttdoc">A TransposeDescriptor for the TransposeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01437">Descriptors.hpp:1437</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml">armnn::StridedSliceDescriptor</a></div><div class="ttdoc">A StridedSliceDescriptor for the StridedSliceLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01250">Descriptors.hpp:1250</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_output_slot_xhtml_a9943775a364fc4ab53b85ac88f311886"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">armnn::IOutputSlot::GetTensorInfo</a></div><div class="ttdeci">virtual const TensorInfo & GetTensorInfo() const =0</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="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#l00083">Descriptors.hpp:83</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="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_abc05539fc6e7907f32ef0fb242e3b3b0aa0ec87054b5e5b7847d0d8780a01a3d5"><div class="ttname"><a href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0aa0ec87054b5e5b7847d0d8780a01a3d5">armnn::ReduceOperation::Sum</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a7a2156ec7d9c012ce00bbcc6afcb9028"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7a2156ec7d9c012ce00bbcc6afcb9028">armnn::DetectionPostProcessDescriptor::m_ScaleY</a></div><div class="ttdeci">float m_ScaleY</div><div class="ttdoc">Center size encoding scale y. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00729">Descriptors.hpp:729</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a4392dd6b4862cc9cf95ae8f1001ba592"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a4392dd6b4862cc9cf95ae8f1001ba592">armnn::DetectionPostProcessDescriptor::m_NmsScoreThreshold</a></div><div class="ttdeci">float m_NmsScoreThreshold</div><div class="ttdoc">NMS score threshold. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00719">Descriptors.hpp:719</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#l00252">INetwork.hpp:252</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &destination)=0</div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_aa6e3c075c888e7c16942a468a3aae33c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#aa6e3c075c888e7c16942a468a3aae33c">armnn::IConnectableLayer::InferOutputShapes</a></div><div class="ttdeci">virtual std::vector< TensorShape > InferOutputShapes(const std::vector< TensorShape > &inputShapes) const =0</div><div class="ttdoc">Infer the shape of the output(s) based on the provided input shape(s) </div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d"><div class="ttname"><a href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d">armnn::NormalizationAlgorithmMethod::LocalBrightness</a></div><div class="ttdoc">Krichevsky 2012: Local Brightness Normalization. </div></div> +<div class="ttc" id="namespacearmnn_xhtml_a2d299363c9fc33334c571fa29ca4f58caa4cbdbb6070a5abb35fc95ecf1e22c14"><div class="ttname"><a href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58caa4cbdbb6070a5abb35fc95ecf1e22c14">armnn::ComparisonOperation::LessOrEqual</a></div></div> +<div class="ttc" id="structarmnn_1_1_check_location_xhtml_a46e3b4b140e2c550342337b5fcceb9c6"><div class="ttname"><a href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">armnn::CheckLocation::m_Function</a></div><div class="ttdeci">const char * m_Function</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00016">Exceptions.hpp:16</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_abc05539fc6e7907f32ef0fb242e3b3b0a6a061313d22e51e0f25b7cd4dc065233"><div class="ttname"><a href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0a6a061313d22e51e0f25b7cd4dc065233">armnn::ReduceOperation::Max</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#l00339">Descriptors.hpp:339</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc"><div class="ttname"><a href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc">armnn::NormalizationAlgorithmChannel::Across</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a1cfaa710db2a54673b21d2ea2da757c8acad39a154bffb61175f674d6eefaf6d0"><div class="ttname"><a href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8acad39a154bffb61175f674d6eefaf6d0">armnn::UnaryOperation::Exp</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a9b18daea2e9f42386055326fd016519a"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">armnn::LstmInputParams::m_OutputLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_OutputLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00060">LstmParams.hpp:60</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a1cfaa710db2a54673b21d2ea2da757c8a09bb7f6e2118c85a6a55bd4bf2beeca4"><div class="ttname"><a href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8a09bb7f6e2118c85a6a55bd4bf2beeca4">armnn::UnaryOperation::Rsqrt</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#l00737">Descriptors.hpp:737</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a2d299363c9fc33334c571fa29ca4f58ca6f2f0aefb3d22da0f3839453add5f937"><div class="ttname"><a href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca6f2f0aefb3d22da0f3839453add5f937">armnn::ComparisonOperation::GreaterOrEqual</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_a7d28f3a07c7ab30f66e7c70ded86eb49"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#a7d28f3a07c7ab30f66e7c70ded86eb49">armnnTfLiteParser::TfLiteParserImpl::OutputShapeOfSqueeze</a></div><div class="ttdeci">static armnn::TensorInfo OutputShapeOfSqueeze(std::vector< uint32_t > squeezeDims, const armnn::TensorInfo &inputTensorInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02137">TfLiteParser.cpp:2137</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml">armnn::DetectionPostProcessDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00681">Descriptors.hpp:681</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl_xhtml_afe1ea028d8fcb9942c204d4415ff3319"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser_impl.xhtml#afe1ea028d8fcb9942c204d4415ff3319">armnnTfLiteParser::TfLiteParserImpl::GetSubgraphInputTensorNames</a></div><div class="ttdeci">std::vector< std::string > GetSubgraphInputTensorNames(size_t subgraphId) const</div><div class="ttdoc">Return the input tensor names for a given subgraph. </div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l05266">TfLiteParser.cpp:5266</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a4dd0194b114cbf51da5b3a72569863ef"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a4dd0194b114cbf51da5b3a72569863ef">armnn::DataLayout::NDHWC</a></div></div> +<div class="ttc" id="classarmnn_1_1_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#l00195">Tensor.hpp:195</a></div></div> +<div class="ttc" id="_tf_lite_parser_8cpp_xhtml_ae6440b8ea95cf981cd7bbffa52c22fe1"><div class="ttname"><a href="_tf_lite_parser_8cpp.xhtml#ae6440b8ea95cf981cd7bbffa52c22fe1">CHECK_BUFFER_SIZE</a></div><div class="ttdeci">#define CHECK_BUFFER_SIZE(BUFFER_PTR, TENSOR_INFO, BUFFER_ID)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00331">TfLiteParser.cpp:331</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_a16543bce17aa2e4d6e81c88f74227192"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#a16543bce17aa2e4d6e81c88f74227192">armnn::Convolution3dDescriptor::m_DilationZ</a></div><div class="ttdeci">uint32_t m_DilationZ</div><div class="ttdoc">Dilation along z axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00619">Descriptors.hpp:619</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml_a28c4c9cb15f6be3499abbc46b356060b"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">armnn::ActivationDescriptor::m_B</a></div><div class="ttdeci">float m_B</div><div class="ttdoc">Beta lower bound value used by the activation functions. (BoundedReLu, Linear, TanH). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00063">Descriptors.hpp:63</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a7c00efeb540198b33b8558c76e5cc2dd"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a7c00efeb540198b33b8558c76e5cc2dd">armnn::TensorInfo::IsQuantized</a></div><div class="ttdeci">bool IsQuantized() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00504">Tensor.cpp:504</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_abeaf4f6785039866fd075f4569ba8e84"><div class="ttname"><a href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a></div><div class="ttdeci">armnn::TensorShape Permuted(const armnn::TensorShape &srcShape, const armnn::PermutationVector &mappings)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00098">Permute.cpp:98</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#l00157">Descriptors.hpp:157</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#l00769">Descriptors.hpp:769</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_a5164336f6a1b15be0d434a6bbf7289da"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#a5164336f6a1b15be0d434a6bbf7289da">armnn::Convolution3dDescriptor::m_StrideZ</a></div><div class="ttdeci">uint32_t m_StrideZ</div><div class="ttdoc">Stride value when proceeding through input for the depth dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00613">Descriptors.hpp:613</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a6e7dc8639c4b2f0a93031afff5182735"><div class="ttname"><a href="namespacearmnn.xhtml#a6e7dc8639c4b2f0a93031afff5182735">armnn::IsActivationSupported</a></div><div class="ttdeci">bool IsActivationSupported(const BackendId &backend, const TensorInfo &input, const TensorInfo &output, const ActivationDescriptor &descriptor, char *reasonIfUnsupported=nullptr, size_t reasonIfUnsupportedMaxLength=1024)</div><div class="ttdoc">Deprecated in favor of IBackend and ILayerSupport interfaces. </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#l00765">Descriptors.hpp:765</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="include_2armnn_tf_lite_parser_2_version_8hpp_xhtml"><div class="ttname"><a href="include_2armnn_tf_lite_parser_2_version_8hpp.xhtml">Version.hpp</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#l00059">Descriptors.hpp:59</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_input_slot_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_input_slot.xhtml">armnn::IInputSlot</a></div><div class="ttdoc">An input connection slot for a layer. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00025">INetwork.hpp:25</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#l00389">Descriptors.hpp:389</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#l00627">Descriptors.hpp:627</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_abdcd184ed3bd648bb31d385040cafd5d"><div class="ttname"><a href="namespacearmnn.xhtml#abdcd184ed3bd648bb31d385040cafd5d">armnn::MaxNumOfTensorDimensions</a></div><div class="ttdeci">constexpr unsigned int MaxNumOfTensorDimensions</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00031">Types.hpp:31</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution3d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_convolution3d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::Convolution3dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation along y axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00617">Descriptors.hpp:617</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorInfo::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00196">Tensor.hpp:196</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a31da1ead6794dd64571afdd0b6efc771"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">armnn::LstmInputParams::m_InputToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00041">LstmParams.hpp:41</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9ea"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9ea">armnn::ActivationFunction</a></div><div class="ttdeci">ActivationFunction</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00086">Types.hpp:86</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ae7e8cbf71db6a490789ca6dcaa8deeaea78d811e98514cd165dda532286610fd2"><div class="ttname"><a href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeaea78d811e98514cd165dda532286610fd2">armnn::ArgMinMaxFunction::Min</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::DepthwiseConvolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00662">Descriptors.hpp:662</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_a2d3dcfc10f90adedc995b64211dab6e9"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#a2d3dcfc10f90adedc995b64211dab6e9">armnn::FullyConnectedDescriptor::m_ConstantWeights</a></div><div class="ttdeci">bool m_ConstantWeights</div><div class="ttdoc">Enable/disable constant weights and biases. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00498">Descriptors.hpp:498</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_affcee5f4ab5994a21bee3b78b4e43de3"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">armnn::LstmInputParams::m_InputToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00040">LstmParams.hpp:40</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_510324e450b9df55f9aac2d01fae83d8.xhtml">armnnTfLiteParser</a></li><li class="navelem"><a class="el" href="_tf_lite_parser_8cpp.xhtml">TfLiteParser.cpp</a></li> + <li class="footer">Generated on Fri Feb 24 2023 10:24:25 for ArmNN by + <a 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