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<div class="title">TfLiteParser.cpp</div>  </div>
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<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>&#160;<span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;</div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_tf_lite_parser_8hpp.xhtml">TfLiteParser.hpp</a>&quot;</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;</div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_descriptors_8hpp.xhtml">armnn/Descriptors.hpp</a>&gt;</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_exceptions_8hpp.xhtml">armnn/Exceptions.hpp</a>&gt;</span></div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_logging_8hpp.xhtml">armnn/Logging.hpp</a>&gt;</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_types_utils_8hpp.xhtml">armnn/TypesUtils.hpp</a>&gt;</span></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_ignore_unused_8hpp.xhtml">armnn/utility/IgnoreUnused.hpp</a>&gt;</span></div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;</div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment">// armnnUtils:</span></div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_permute_8hpp.xhtml">armnnUtils/Permute.hpp</a>&gt;</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;</div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_parser_helper_8hpp.xhtml">ParserHelper.hpp</a>&gt;</span></div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_verification_helpers_8hpp.xhtml">VerificationHelpers.hpp</a>&gt;</span></div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;</div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="comment">// The generated code based on the Tf Lite schema:</span></div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="preprocessor">#include &lt;schema_generated.h&gt;</span></div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;</div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="preprocessor">#include &lt;flatbuffers/flexbuffers.h&gt;</span></div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;</div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;<span class="preprocessor">#include &lt;boost/assert.hpp&gt;</span></div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;<span class="preprocessor">#include &lt;boost/format.hpp&gt;</span></div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;<span class="preprocessor">#include &lt;boost/numeric/conversion/cast.hpp&gt;</span></div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;<span class="preprocessor">#include &lt;boost/filesystem.hpp&gt;</span></div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;</div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;<span class="preprocessor">#include &lt;fstream&gt;</span></div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;<span class="preprocessor">#include &lt;algorithm&gt;</span></div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;<span class="preprocessor">#include &lt;limits&gt;</span></div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;<span class="preprocessor">#include &lt;numeric&gt;</span></div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;<span class="preprocessor">#include &lt;sstream&gt;</span></div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;</div><div class="line"><a name="l00036"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">   36</a></span>&#160;<span class="preprocessor">#define ARMNN_THROW_PARSE_EXCEPTION(msg) \</span></div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;<span class="preprocessor">          { \</span></div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;<span class="preprocessor">            throw armnn::ParseException( static_cast&lt;const std::stringstream&amp;&gt;( std::stringstream() &lt;&lt; msg \</span></div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;<span class="preprocessor">               &lt;&lt; &quot;: &quot; \</span></div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;<span class="preprocessor">               &lt;&lt; CHECK_LOCATION().AsString()).str()); \</span></div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="preprocessor">          }</span></div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;</div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<span class="keyword">using</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">armnn::CheckLocation</a>;</div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearmnn_tf_lite_parser.xhtml">armnnTfLiteParser</a></div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;{</div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;<span class="keyword">namespace</span></div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;{</div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;<span class="keyword">const</span> uint32_t VIRTUAL_OPERATOR_ID = std::numeric_limits&lt;uint32_t&gt;::max();</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;<span class="keywordtype">void</span> CheckSubgraph(<span class="keyword">const</span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">TfLiteParser::ModelPtr</a> &amp; model,</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;                   <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;                   <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a> &amp; location)</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;{</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;    <span class="keywordflow">if</span> (model.get() == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;    {</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;            boost::str(</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;                boost::format(<span class="stringliteral">&quot;%1% was called with invalid (null) model. &quot;</span></div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;                              <span class="stringliteral">&quot;Possible reason is that the model is not yet loaded and Unpack(ed). &quot;</span></div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;                              <span class="stringliteral">&quot;subgraph:%2% at %3%&quot;</span>) %</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;                              location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;                              subgraphIndex %</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;                              location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;    }</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;    <span class="keywordflow">else</span> <span class="keywordflow">if</span> (subgraphIndex &gt;= model-&gt;subgraphs.size())</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;    {</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;            boost::str(</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;                boost::format(<span class="stringliteral">&quot;%1% was called with an invalid subgraph index. &quot;</span></div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;                              <span class="stringliteral">&quot;subgraph:%2% at %3%&quot;</span>) %</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;                              location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;                              subgraphIndex %</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;                              location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;    }</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;}</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;</div><div class="line"><a name="l00079"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">   79</a></span>&#160;<span class="preprocessor">#define CHECK_SUBGRAPH(MODEL, SUBGRAPH_INDEX) \</span></div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;<span class="preprocessor">    CheckSubgraph(MODEL, SUBGRAPH_INDEX, CHECK_LOCATION())</span></div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;<span class="keywordtype">void</span> CheckModel(<span class="keyword">const</span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">TfLiteParser::ModelPtr</a> &amp; model,</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;                <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;                <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;                <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a> &amp; location)</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;{</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;    <span class="keywordflow">if</span> (model.get() == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;    {</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;            boost::str(</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;                boost::format(<span class="stringliteral">&quot;%1% was called with invalid (null) model. &quot;</span></div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;                                <span class="stringliteral">&quot;Possible reason is that the model is not yet loaded and Unpack(ed). &quot;</span></div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;                                <span class="stringliteral">&quot;subgraph:%2% operator:%3% at %4%&quot;</span>) %</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;                                location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;                                subgraphIndex %</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;                                operatorIndex %</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;                                location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;    }</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    <span class="keywordflow">else</span> <span class="keywordflow">if</span> (subgraphIndex &gt;= model-&gt;subgraphs.size())</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    {</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;            boost::str(</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;                boost::format(<span class="stringliteral">&quot;%1% was called with an invalid subgraph index. &quot;</span></div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;                                <span class="stringliteral">&quot;subgraph:%2% operator:%3% at %4%&quot;</span>) %</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;                                location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;                                subgraphIndex %</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;                                operatorIndex %</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;                                location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;    }</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    <span class="keywordflow">else</span> <span class="keywordflow">if</span> (operatorIndex &gt;= model-&gt;subgraphs[subgraphIndex]-&gt;operators.size() &amp;&amp;</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;             operatorIndex != VIRTUAL_OPERATOR_ID)</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    {</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;            boost::str(</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;                boost::format(<span class="stringliteral">&quot;%1% was called with an invalid operator index. &quot;</span></div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;                                <span class="stringliteral">&quot;subgraph:%2% operator:%3% at %4%&quot;</span>) %</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;                                location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;                                subgraphIndex %</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;                                operatorIndex %</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;                                location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    }</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;}</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;</div><div class="line"><a name="l00124"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">  124</a></span>&#160;<span class="preprocessor">#define CHECK_MODEL(MODEL, SUBGRAPH_INDEX, OPERATOR_INDEX) \</span></div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;<span class="preprocessor">    CheckModel(MODEL, SUBGRAPH_INDEX, OPERATOR_INDEX, CHECK_LOCATION())</span></div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;<span class="keywordtype">void</span> <a class="code" href="_tensor_test_8cpp.xhtml#ad80e179ec400af9d2547f172f3ca05f3">CheckTensor</a>(<span class="keyword">const</span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">TfLiteParser::ModelPtr</a> &amp; model,</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;                 <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;                 <span class="keywordtype">size_t</span> tensorIndex,</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;                 <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a> &amp; location)</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;{</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;    <span class="comment">// not checking model, because I assume CHECK_MODEL already run</span></div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;    <span class="comment">// and checked that. An assert would do.</span></div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;    BOOST_ASSERT_MSG(model.get() != <span class="keyword">nullptr</span>, <span class="stringliteral">&quot;Expecting a valid model in this function&quot;</span>);</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;    <span class="comment">// also subgraph index should be checked by CHECK_MODEL so</span></div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;    <span class="comment">// I only add an assert here</span></div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;    BOOST_ASSERT_MSG(subgraphIndex &lt; model-&gt;subgraphs.size(), <span class="stringliteral">&quot;Expecting a valid subgraph index&quot;</span>);</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;    <span class="comment">// the tensor index is the only one to check here</span></div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;    <span class="keywordflow">if</span> (tensorIndex &gt;= model-&gt;subgraphs[subgraphIndex]-&gt;tensors.size())</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    {</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;            boost::str(</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;                boost::format(<span class="stringliteral">&quot;%1% was called with an invalid tensor index. &quot;</span></div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;                                <span class="stringliteral">&quot;subgraph:%2% tensor:%3% at %4%&quot;</span>) %</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;                                location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;                                subgraphIndex %</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;                                tensorIndex %</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;                                location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;    }</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;}</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;</div><div class="line"><a name="l00154"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#aa1664dc13adbc85ac12fb584b76bfdae">  154</a></span>&#160;<span class="preprocessor">#define CHECK_TENSOR(MODEL, SUBGRAPH_INDEX, TENSOR_INDEX) \</span></div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;<span class="preprocessor">    CheckTensor(MODEL, SUBGRAPH_INDEX, TENSOR_INDEX, CHECK_LOCATION())</span></div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;<span class="keywordtype">void</span> CheckTensorPtr(<a class="code" href="_parser_flatbuffers_serialize_fixture_8hpp.xhtml#a15c20a0693cd3fc4d85565e2f920d8ef">TfLiteParser::TensorRawPtr</a> rawPtr,</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;                    <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a> &amp; location)</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;{</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;    <span class="keywordflow">if</span> (rawPtr == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;    {</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;        <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>&#160;            boost::str(</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;                boost::format(<span class="stringliteral">&quot;%1% was called with a null tensor pointer. &quot;</span></div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;                              <span class="stringliteral">&quot;at %2%&quot;</span>) %</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;                              location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;                              location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;    }</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;}</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;</div><div class="line"><a name="l00172"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#ae38d96fe05581ea025713b3e781c5a43">  172</a></span>&#160;<span class="preprocessor">#define CHECK_TENSOR_PTR(TENSOR_PTR) \</span></div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;<span class="preprocessor">    CheckTensorPtr(TENSOR_PTR, CHECK_LOCATION())</span></div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;<span class="keywordtype">void</span> CheckBuffer(<span class="keyword">const</span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">TfLiteParser::ModelPtr</a> &amp; model,</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;                 <span class="keywordtype">size_t</span> bufferIndex,</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;                 <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a> &amp; location)</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;{</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    <span class="keywordflow">if</span> (model.get() == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;    {</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;            boost::str(</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;                boost::format(<span class="stringliteral">&quot;%1% was called with invalid (null) model. &quot;</span></div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;                              <span class="stringliteral">&quot;Possible reason is that the model is not yet loaded and Unpack(ed). &quot;</span></div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;                              <span class="stringliteral">&quot;buffer:%2% at %3%&quot;</span>) %</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;                              location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;                              bufferIndex %</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;                              location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;    }</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;    <span class="keywordflow">else</span> <span class="keywordflow">if</span> (bufferIndex &gt;= model-&gt;buffers.size())</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;    {</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;            boost::str(</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;                boost::format(<span class="stringliteral">&quot;%1% was called with an invalid buffer index. &quot;</span></div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;                              <span class="stringliteral">&quot;buffer index:%2% at %3%&quot;</span>) %</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;                              location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;                              bufferIndex %</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;                              location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;    }</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;    <span class="keywordflow">else</span> <span class="keywordflow">if</span> (model-&gt;buffers[bufferIndex].get() == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;    {</div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;        <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>&#160;            boost::str(</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;                boost::format(<span class="stringliteral">&quot;The buffer #%1% is null. %3%&quot;</span>) %</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;                              bufferIndex %</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;                              location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a5e3562cda960da001597e7dd5679b140">AsString</a>()));</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    }</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;}</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;</div><div class="line"><a name="l00210"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#a7c88d54e3f895030c70330a4c9d76a7a">  210</a></span>&#160;<span class="preprocessor">#define CHECK_BUFFER(MODEL, BUFFER_INDEX) \</span></div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;<span class="preprocessor">    CheckBuffer(MODEL, BUFFER_INDEX, CHECK_LOCATION())</span></div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;<span class="keywordtype">void</span> CheckBufferSize(TfLiteParser::BufferRawPtr bufferPtr,</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;                     <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> &amp; tensorInfo,</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;                     uint32_t bufferId,</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;                     <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a> &amp; location)</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;{</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;    <span class="keywordflow">if</span> (bufferPtr == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;    {</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;        <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>&#160;            boost::str(</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;                boost::format(<span class="stringliteral">&quot;BufferPtr is null for buffer:%1%. %2%&quot;</span>) %</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;                              bufferId %</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;                              location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a5e3562cda960da001597e7dd5679b140">AsString</a>()));</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    }</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;    <span class="keywordflow">else</span> <span class="keywordflow">if</span>(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() &gt; bufferPtr-&gt;data.size() ||</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;            tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>() &gt; bufferPtr-&gt;data.size())</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;    {</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;        std::stringstream ss;</div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;        ss &lt;&lt; <span class="stringliteral">&quot;Buffer #&quot;</span> &lt;&lt; bufferId &lt;&lt; <span class="stringliteral">&quot; has &quot;</span> &lt;&lt; bufferPtr-&gt;data.size() &lt;&lt; <span class="stringliteral">&quot; bytes. &quot;</span></div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;           &lt;&lt; <span class="stringliteral">&quot;For tensor: &quot;</span> &lt;&lt; tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;           &lt;&lt; <span class="stringliteral">&quot; expecting: &quot;</span> &lt;&lt; tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>() &lt;&lt; <span class="stringliteral">&quot; bytes and &quot;</span></div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;           &lt;&lt; tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() &lt;&lt; <span class="stringliteral">&quot; elements. &quot;</span> &lt;&lt; location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a5e3562cda960da001597e7dd5679b140">AsString</a>();</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;        <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="l00235"></a><span class="lineno">  235</span>&#160;    }</div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;}</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;</div><div class="line"><a name="l00238"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#ae6440b8ea95cf981cd7bbffa52c22fe1">  238</a></span>&#160;<span class="preprocessor">#define CHECK_BUFFER_SIZE(BUFFER_PTR, TENSOR_INFO, BUFFER_ID) \</span></div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;<span class="preprocessor">    CheckBufferSize(BUFFER_PTR, TENSOR_INFO, BUFFER_ID, CHECK_LOCATION())</span></div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;<span class="keywordtype">bool</span> <a class="code" href="namespacearmnn.xhtml#a58bfb9626d373249745d78b95543116e">IsActivationSupported</a>(tflite::ActivationFunctionType activationType)</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;{</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;    <span class="keywordflow">switch</span>(activationType)</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;    {</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;        <span class="keywordflow">case</span> tflite::ActivationFunctionType_NONE:</div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;        <span class="keywordflow">case</span> tflite::ActivationFunctionType_RELU:</div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;        <span class="keywordflow">case</span> tflite::ActivationFunctionType_RELU6:</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;        <span class="keywordflow">case</span> tflite::ActivationFunctionType_TANH:</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;        {</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;            <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;        }</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;        <span class="keywordflow">default</span>:</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;        {</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;            <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;        }</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;    }</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;}</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;</div><div class="line"><a name="l00259"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">  259</a></span>&#160;<span class="preprocessor">#define CHECK_SUPPORTED_FUSED_ACTIVATION(OPTION, SUBGRAPH_INDEX, OPERATOR_INDEX) \</span></div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;<span class="preprocessor">    do { \</span></div><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;<span class="preprocessor">        if (IsActivationSupported(OPTION-&gt;fused_activation_function) == false) \</span></div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;<span class="preprocessor">        { \</span></div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;<span class="preprocessor">            throw ParseException( \</span></div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;<span class="preprocessor">                boost::str( \</span></div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;<span class="preprocessor">                    boost::format(&quot;TfLite parser doesn&#39;t suppport fused activation: &quot; \</span></div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;<span class="preprocessor">                                  &quot;%1%/%2% in %3% subgraph:%4% operator:%5% at %6%&quot;) % \</span></div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;<span class="preprocessor">                                  OPTION-&gt;fused_activation_function % \</span></div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;<span class="preprocessor">                                  tflite::EnumNameActivationFunctionType(\</span></div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;<span class="preprocessor">                                      OPTION-&gt;fused_activation_function) % \</span></div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;<span class="preprocessor">                                  __func__ % \</span></div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;<span class="preprocessor">                                  SUBGRAPH_INDEX % \</span></div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;<span class="preprocessor">                                  OPERATOR_INDEX % \</span></div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;<span class="preprocessor">                                  CHECK_LOCATION().FileLine())); \</span></div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;<span class="preprocessor">        } \</span></div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;<span class="preprocessor">    } while(false)</span></div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;</div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;</div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;std::vector&lt;unsigned int&gt; AsUnsignedVector(<span class="keyword">const</span> std::vector&lt;int32_t&gt; &amp; in)</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;{</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;    std::vector&lt;unsigned int&gt; result;</div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;    result.reserve(in.size());</div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;    <span class="keywordflow">for</span> (<span class="keyword">auto</span> &amp; i : in)</div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;    {</div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;        result.push_back(<a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(i));</div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;    }</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;}</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(uint32_t inputSize,</div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;                 uint32_t filterSize,</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;                 uint32_t stride,</div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;                 uint32_t dilation,</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;                 uint32_t&amp; paddingFront,</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;                 uint32_t&amp; paddingBack,</div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;                 tflite::Padding padding)</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;{</div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;    paddingFront = 0;</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;    paddingBack = 0;</div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    <span class="keywordflow">if</span> (padding == tflite::Padding_SAME)</div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;    {</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;        uint32_t outputSize = (inputSize + stride - 1) / stride;</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;        uint32_t dilatedSize = filterSize + (dilation - 1) * (filterSize - 1);</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;        uint32_t temp = (outputSize - 1) * stride + dilatedSize;</div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;        <span class="keywordflow">if</span> (temp &gt; inputSize)</div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;        {</div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;            paddingFront = (temp - inputSize) / 2;</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;            paddingBack = (temp - inputSize) - paddingFront;</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;        }</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;    }</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;}</div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(<a class="code" href="_parser_flatbuffers_serialize_fixture_8hpp.xhtml#a15c20a0693cd3fc4d85565e2f920d8ef">TfLiteParser::TensorRawPtr</a> tensorPtr, <span class="keyword">const</span> std::vector&lt;unsigned int&gt;&amp; shapes,</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;                               <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a>&amp; dimensionMappings = {0, 1, 2, 3})</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;{</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> type;</div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#ae38d96fe05581ea025713b3e781c5a43">CHECK_TENSOR_PTR</a>(tensorPtr);</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;</div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;    <span class="keywordflow">switch</span> (tensorPtr-&gt;type)</div><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;    {</div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;        <span class="keywordflow">case</span> tflite::TensorType_UINT8:</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;            type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>;</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;        <span class="keywordflow">case</span> tflite::TensorType_FLOAT32:</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;            type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>;</div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;        <span class="keywordflow">case</span> tflite::TensorType_INT8:</div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;            <span class="keywordflow">if</span> (tensorPtr-&gt;quantization-&gt;zero_point.size() == 1)</div><div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;            {</div><div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;                <span class="comment">// Per-tensor</span></div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;                type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>;</div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;            }</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;            <span class="keywordflow">else</span></div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;            {</div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;                <span class="comment">// Per-channel</span></div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;                type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>;</div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;            }</div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;        <span class="keywordflow">case</span> tflite::TensorType_INT16:</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;            type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>;</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;        <span class="keywordflow">case</span> tflite::TensorType_INT32:</div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;            type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>;</div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;</div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;        <span class="keywordflow">default</span>:</div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;        {</div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;            <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="l00348"></a><span class="lineno">  348</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;                boost::str(</div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;                    boost::format(<span class="stringliteral">&quot;Unsupported data type %1% = %2% for tensor: %3%. %4%&quot;</span>) %</div><div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;                                  tensorPtr-&gt;type %</div><div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;                                  tflite::EnumNameTensorType(tensorPtr-&gt;type) %</div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;                                  tensorPtr-&gt;name %</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;                                  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a5e3562cda960da001597e7dd5679b140">AsString</a>()));</div><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;        }</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;    }</div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;    std::vector&lt;unsigned int&gt; safeShape = shapes;</div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;    <span class="keywordflow">if</span> (safeShape.size() == 0)</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;    {</div><div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;        safeShape.push_back(1);</div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;    }</div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;    <span class="keywordtype">float</span> quantizationScale = 0.0f;</div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;    int32_t quantizationOffset = 0;</div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;</div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;    <span class="keywordflow">if</span> (tensorPtr-&gt;quantization.get())</div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;    {</div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;        <span class="keywordflow">if</span> (tensorPtr-&gt;quantization-&gt;scale.size() &lt;= 1)</div><div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;        {</div><div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;            <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(tensorPtr-&gt;quantization-&gt;zero_point.size(), 0, 1);</div><div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;            <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(tensorPtr-&gt;quantization-&gt;zero_point.size(), 0, 1);</div><div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;</div><div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;            <span class="keywordflow">if</span> (tensorPtr-&gt;quantization-&gt;scale.size() == 1)</div><div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;            {</div><div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;                quantizationScale = tensorPtr-&gt;quantization-&gt;scale[0];</div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;            }</div><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;            <span class="keywordflow">if</span> (tensorPtr-&gt;quantization-&gt;zero_point.size() == 1)</div><div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;            {</div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;                <span class="comment">// NOTE: we lose precision here when converting from 64 bit to 32</span></div><div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;                <span class="comment">//       but this is what we support at the moment in ArmNN</span></div><div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;                quantizationOffset = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;int32_t&gt;(tensorPtr-&gt;quantization-&gt;zero_point[0]);</div><div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;            }</div><div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;</div><div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;            <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> result(boost::numeric_cast&lt;unsigned int&gt;(safeShape.size()),</div><div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;                              safeShape.data(),</div><div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;                              type,</div><div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;                              quantizationScale,</div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;                              quantizationOffset);</div><div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;</div><div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;            <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;        }</div><div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;        <span class="keywordflow">else</span></div><div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;        {</div><div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;            std::vector&lt;float&gt; quantizationScales;</div><div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;            std::vector&lt;int32_t&gt; quantizationOffsets;</div><div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;</div><div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;            <span class="comment">// Scale</span></div><div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;            std::copy(tensorPtr-&gt;quantization-&gt;scale.begin(),</div><div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;                      tensorPtr-&gt;quantization-&gt;scale.end(),</div><div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;                      std::back_inserter(quantizationScales));</div><div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;</div><div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;            <span class="comment">// QSymmS8 Per-axis</span></div><div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;            <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> result(boost::numeric_cast&lt;unsigned int&gt;(safeShape.size()),</div><div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;                              safeShape.data(),</div><div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;                              type,</div><div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;                              quantizationScales,</div><div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;                              dimensionMappings[<a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(</div><div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;                              tensorPtr-&gt;quantization-&gt;quantized_dimension)]);</div><div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;            <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;        }</div><div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;    }</div><div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;    {</div><div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> result(boost::numeric_cast&lt;unsigned int&gt;(safeShape.size()),</div><div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;                                 safeShape.data(),</div><div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;                                 type,</div><div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;                                 quantizationScale,</div><div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;                                 quantizationOffset);</div><div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;        <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;    }</div><div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;}</div><div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;</div><div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(<a class="code" href="_parser_flatbuffers_serialize_fixture_8hpp.xhtml#a15c20a0693cd3fc4d85565e2f920d8ef">TfLiteParser::TensorRawPtr</a> tensorPtr, </div><div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;                               <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a>&amp; dimensionMappings = {0, 1, 2, 3})</div><div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;{</div><div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;    <span class="keyword">auto</span> <span class="keyword">const</span> &amp; dimensions = AsUnsignedVector(tensorPtr-&gt;shape);</div><div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(tensorPtr, dimensions, dimensionMappings);</div><div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;}</div><div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;</div><div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;std::pair&lt;armnn::ConstTensor, std::unique_ptr&lt;T[]&gt;&gt;</div><div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;CreateConstTensorImpl(TfLiteParser::BufferRawPtr bufferPtr,</div><div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;                      <a class="code" href="_parser_flatbuffers_serialize_fixture_8hpp.xhtml#a15c20a0693cd3fc4d85565e2f920d8ef">TfLiteParser::TensorRawPtr</a> tensorPtr,</div><div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;                      <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>&amp; tensorInfo,</div><div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;                      <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::PermutationVector&amp;&gt;</a> permutationVector)</div><div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;{</div><div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(tensorPtr);</div><div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;    BOOST_ASSERT_MSG(tensorPtr != <span class="keyword">nullptr</span>, <span class="stringliteral">&quot;tensorPtr is null&quot;</span>);</div><div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;    BOOST_ASSERT_MSG(bufferPtr != <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;        boost::str(</div><div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;            boost::format(<span class="stringliteral">&quot;Buffer for buffer:%1% is null&quot;</span>) % tensorPtr-&gt;buffer).c_str());</div><div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;</div><div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;    std::unique_ptr&lt;T[]&gt; 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="l00444"></a><span class="lineno">  444</span>&#160;</div><div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;    <span class="keywordflow">if</span> (permutationVector.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() &amp;&amp; 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>() &gt; 0)</div><div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;    {</div><div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;        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="l00448"></a><span class="lineno">  448</span>&#160;        <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="l00449"></a><span class="lineno">  449</span>&#160;                            <span class="keyword">reinterpret_cast&lt;</span><span class="keyword">const </span>T*<span class="keyword">&gt;</span>(bufferPtr-&gt;data.data()), data.get(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;    }</div><div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;    {</div><div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;        ::memcpy(data.get(), bufferPtr-&gt;data.data(), tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;    }</div><div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;</div><div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;    <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="l00457"></a><span class="lineno">  457</span>&#160;}</div><div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;</div><div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;<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="l00460"></a><span class="lineno">  460</span>&#160;{</div><div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;    <span class="comment">// generate the binding id by shifting the tensor id by 8 bit</span></div><div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;    <span class="comment">// and add the subgraph id, which allows 256 subgraphs</span></div><div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;    <span class="keywordflow">return</span> <span class="keyword">static_cast&lt;</span><a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a><span class="keyword">&gt;</span>((tensorIndex&lt;&lt;8)+subgraphIndex);</div><div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;}</div><div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;</div><div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;<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>&amp; actual, <span class="keyword">const</span> std::vector&lt;int32_t&gt;&amp; expected)</div><div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;{</div><div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;    <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="l00469"></a><span class="lineno">  469</span>&#160;    <span class="keywordflow">if</span> (actualSize != expected.size())</div><div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;    {</div><div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;        <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;    }</div><div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;</div><div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0u; i &lt; actualSize; i++)</div><div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;    {</div><div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;        <span class="keywordflow">if</span> (expected[i] &lt; 0 ||</div><div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;            actual[i] != static_cast&lt;unsigned int&gt;(expected[i]))</div><div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;        {</div><div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;            <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;        }</div><div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;    }</div><div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;</div><div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;    <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;}</div><div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;</div><div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;} <span class="comment">// &lt;anonymous&gt;</span></div><div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;</div><div class="line"><a name="l00488"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a2ed4853234865d838da50085da99b2a6">  488</a></span>&#160;TfLiteParser::TfLiteParser(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ITfLiteParser::TfLiteParserOptions&gt;</a>&amp; <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>)</div><div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;: m_Options(options)</div><div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;, m_Network(nullptr, nullptr)</div><div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;, m_ParserFunctions(tflite::BuiltinOperator_MAX+1, &amp;<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml">TfLiteParser</a>::ParseUnsupportedOperator)</div><div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;{</div><div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;    <span class="comment">// register supported operators</span></div><div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_ADD]                     = &amp;TfLiteParser::ParseAdd;</div><div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_AVERAGE_POOL_2D]         = &amp;TfLiteParser::ParseAveragePool2D;</div><div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_BATCH_TO_SPACE_ND]       = &amp;TfLiteParser::ParseBatchToSpaceND;</div><div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_CONCATENATION]           = &amp;TfLiteParser::ParseConcatenation;</div><div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_CONV_2D]                 = &amp;TfLiteParser::ParseConv2D;</div><div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_CUSTOM]                  = &amp;TfLiteParser::ParseCustomOperator;</div><div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_DEPTHWISE_CONV_2D]       = &amp;TfLiteParser::ParseDepthwiseConv2D;</div><div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_DEQUANTIZE]              = &amp;TfLiteParser::ParseDequantize;</div><div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_FULLY_CONNECTED]         = &amp;TfLiteParser::ParseFullyConnected;</div><div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_LOGISTIC]                = &amp;TfLiteParser::ParseLogistic;</div><div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_L2_NORMALIZATION]        = &amp;TfLiteParser::ParseL2Normalization;</div><div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_MAX_POOL_2D]             = &amp;TfLiteParser::ParseMaxPool2D;</div><div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_MAXIMUM]                 = &amp;TfLiteParser::ParseMaximum;</div><div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_MEAN]                    = &amp;TfLiteParser::ParseMean;</div><div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_MINIMUM]                 = &amp;TfLiteParser::ParseMinimum;</div><div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_MUL]                     = &amp;TfLiteParser::ParseMul;</div><div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_PACK]                    = &amp;TfLiteParser::ParsePack;</div><div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_PAD]                     = &amp;TfLiteParser::ParsePad;</div><div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_QUANTIZE]                = &amp;TfLiteParser::ParseQuantize;</div><div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_RELU]                    = &amp;TfLiteParser::ParseRelu;</div><div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_RELU6]                   = &amp;TfLiteParser::ParseRelu6;</div><div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_RESHAPE]                 = &amp;TfLiteParser::ParseReshape;</div><div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_RESIZE_BILINEAR]         = &amp;TfLiteParser::ParseResizeBilinear;</div><div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_RESIZE_NEAREST_NEIGHBOR] = &amp;TfLiteParser::ParseResizeNearestNeighbor;</div><div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_SLICE]                   = &amp;TfLiteParser::ParseSlice;</div><div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_SOFTMAX]                 = &amp;TfLiteParser::ParseSoftmax;</div><div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_SPACE_TO_BATCH_ND]       = &amp;TfLiteParser::ParseSpaceToBatchND;</div><div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_SPLIT]                   = &amp;TfLiteParser::ParseSplit;</div><div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_SQUEEZE]                 = &amp;TfLiteParser::ParseSqueeze;</div><div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_STRIDED_SLICE]           = &amp;TfLiteParser::ParseStridedSlice;</div><div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_SUB]                     = &amp;TfLiteParser::ParseSub;</div><div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_TANH]                    = &amp;TfLiteParser::ParseTanH;</div><div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_TRANSPOSE]               = &amp;TfLiteParser::ParseTranspose;</div><div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_TRANSPOSE_CONV]          = &amp;TfLiteParser::ParseTransposeConv;</div><div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;    m_ParserFunctions[tflite::BuiltinOperator_UNPACK]                  = &amp;TfLiteParser::ParseUnpack;</div><div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;</div><div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;    <span class="comment">// register supported custom operators</span></div><div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;    m_CustomParserFunctions[<span class="stringliteral">&quot;TFLite_Detection_PostProcess&quot;</span>]      = &amp;TfLiteParser::ParseDetectionPostProcess;</div><div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;}</div><div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;</div><div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ResetParser()</div><div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;{</div><div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;    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="l00537"></a><span class="lineno">  537</span>&#160;    m_Model = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;    m_SubgraphConnections.clear();</div><div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;}</div><div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;</div><div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;<span class="keywordtype">void</span> TfLiteParser::AddBroadcastReshapeLayer(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;                                            <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;                                            <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a> *layer)</div><div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;{</div><div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;    BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;</div><div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; subgraphPtr = m_Model-&gt;subgraphs[subgraphIndex];</div><div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = subgraphPtr-&gt;operators[operatorIndex];</div><div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;</div><div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;    BOOST_ASSERT(operatorPtr-&gt;inputs.size() &gt; 1);</div><div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;</div><div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;    uint32_t reshapedInputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(operatorPtr-&gt;inputs[0]);</div><div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160;    <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TensorRawPtr</a> tensorPtr = subgraphPtr-&gt;tensors[reshapedInputId].get();</div><div class="line"><a name="l00555"></a><span class="lineno">  555</span>&#160;    uint32_t inputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(operatorPtr-&gt;inputs[1]);</div><div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;    <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TensorRawPtr</a> tensorPtr1 = subgraphPtr-&gt;tensors[inputId].get();</div><div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;</div><div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> reshapedTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(tensorPtr);</div><div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo    = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(tensorPtr1);</div><div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;</div><div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;    <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() &lt; reshapedTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160;    {</div><div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160;        uint32_t <span class="keywordtype">id</span> = reshapedInputId;</div><div class="line"><a name="l00564"></a><span class="lineno">  564</span>&#160;        reshapedInputId = inputId;</div><div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;        inputId = id;</div><div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160;</div><div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;        reshapedTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(tensorPtr1);</div><div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160;        inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(tensorPtr);</div><div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160;    }</div><div class="line"><a name="l00570"></a><span class="lineno">  570</span>&#160;</div><div class="line"><a name="l00571"></a><span class="lineno">  571</span>&#160;    uint32_t numDimensions = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00572"></a><span class="lineno">  572</span>&#160;</div><div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160;    std::vector&lt;unsigned&gt; reshapedDim;</div><div class="line"><a name="l00574"></a><span class="lineno">  574</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; reshapedTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++i)</div><div class="line"><a name="l00575"></a><span class="lineno">  575</span>&#160;    {</div><div class="line"><a name="l00576"></a><span class="lineno">  576</span>&#160;        reshapedDim.push_back(reshapedTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i]);</div><div class="line"><a name="l00577"></a><span class="lineno">  577</span>&#160;    }</div><div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160;</div><div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160;    std::vector&lt;unsigned int&gt; reshapedDimensions(numDimensions, 1);</div><div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160;    std::copy_backward (reshapedDim.begin(), reshapedDim.end(), reshapedDimensions.end());</div><div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160;</div><div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160;    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>{ numDimensions, reshapedDimensions.data() });</div><div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;</div><div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;    std::string layerName = boost::str(boost::format(<span class="stringliteral">&quot;Reshape_for:%1%&quot;</span>) % layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>());</div><div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;    <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">armnn::ReshapeDescriptor</a> desc;</div><div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;    desc.<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="l00587"></a><span class="lineno">  587</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* reshapeLayer = m_Network-&gt;AddReshapeLayer(desc, layerName.c_str());</div><div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;</div><div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;    reshapeLayer-&gt;<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="l00590"></a><span class="lineno">  590</span>&#160;    reshapeLayer-&gt;<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-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;</div><div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, reshapeLayer, {reshapedInputId});</div><div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;</div><div class="line"><a name="l00594"></a><span class="lineno">  594</span>&#160;    <a class="code" href="classarmnn_1_1_i_input_slot.xhtml">armnn::IInputSlot</a>* input1Slot = &amp;(layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160;    RegisterConsumerOfTensor(subgraphIndex, inputId, input1Slot);</div><div class="line"><a name="l00596"></a><span class="lineno">  596</span>&#160;}</div><div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160;</div><div class="line"><a name="l00598"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a012b24cafd443425314d4f9e06cec6c1">  598</a></span>&#160;<a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a012b24cafd443425314d4f9e06cec6c1">TfLiteParser::CreateNetworkFromBinaryFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile)</div><div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;{</div><div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;    ResetParser();</div><div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;    m_Model = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac5411554ab8c02ca286af52c98f6bd87">LoadModelFromFile</a>(graphFile);</div><div class="line"><a name="l00602"></a><span class="lineno">  602</span>&#160;    <span class="keywordflow">return</span> CreateNetworkFromModel();</div><div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160;}</div><div class="line"><a name="l00604"></a><span class="lineno">  604</span>&#160;</div><div class="line"><a name="l00605"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ae8ee09f5e3e78ecfdf00acfdc37588dc">  605</a></span>&#160;<a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ae8ee09f5e3e78ecfdf00acfdc37588dc">TfLiteParser::CreateNetworkFromBinary</a>(<span class="keyword">const</span> std::vector&lt;uint8_t&gt; &amp; binaryContent)</div><div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160;{</div><div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160;    ResetParser();</div><div class="line"><a name="l00608"></a><span class="lineno">  608</span>&#160;    m_Model = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a858104f225c302988fba35c1cb299066">LoadModelFromBinary</a>(binaryContent.data(), binaryContent.size());</div><div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;    <span class="keywordflow">return</span> CreateNetworkFromModel();</div><div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;}</div><div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160;</div><div class="line"><a name="l00612"></a><span class="lineno">  612</span>&#160;<a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> TfLiteParser::CreateNetworkFromModel()</div><div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160;{</div><div class="line"><a name="l00614"></a><span class="lineno">  614</span>&#160;    m_Network = INetwork::Create();</div><div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;    BOOST_ASSERT(m_Model.get() != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160;</div><div class="line"><a name="l00617"></a><span class="lineno">  617</span>&#160;    <span class="keywordtype">bool</span> failedToCreate = <span class="keyword">false</span>;</div><div class="line"><a name="l00618"></a><span class="lineno">  618</span>&#160;    std::stringstream errors;</div><div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160;</div><div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160;    <span class="keywordflow">if</span> (m_Model-&gt;subgraphs.size() != 1)</div><div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160;    {</div><div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160;                boost::str(</div><div class="line"><a name="l00624"></a><span class="lineno">  624</span>&#160;                        boost::format(<span class="stringliteral">&quot;Current TfLite parser only supports 1 subgraph. Current one has: %1% %2%&quot;</span>) %</div><div class="line"><a name="l00625"></a><span class="lineno">  625</span>&#160;                        m_Model-&gt;subgraphs.size() %</div><div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160;                        <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00627"></a><span class="lineno">  627</span>&#160;    }</div><div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160;</div><div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160;    <span class="keywordtype">size_t</span> subgraphIndex = 0;</div><div class="line"><a name="l00630"></a><span class="lineno">  630</span>&#160;    <span class="keywordflow">for</span> (<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a988cb5e216eb87d854414c6a0282eeb4">SubgraphPtr</a> <span class="keyword">const</span> &amp; subgraph : m_Model-&gt;subgraphs)</div><div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;    {</div><div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160;        m_SubgraphConnections.emplace_back(subgraph-&gt;tensors.size());</div><div class="line"><a name="l00633"></a><span class="lineno">  633</span>&#160;</div><div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;        <span class="keywordtype">size_t</span> operatorIndex = 0;</div><div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;        <span class="keywordflow">for</span> (<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aadad81a95152fe5aad839db352d4012c">OperatorPtr</a> <span class="keyword">const</span> &amp; op : subgraph-&gt;operators)</div><div class="line"><a name="l00636"></a><span class="lineno">  636</span>&#160;        {</div><div class="line"><a name="l00637"></a><span class="lineno">  637</span>&#160;            <span class="keywordflow">try</span></div><div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160;            {</div><div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160;                <span class="keyword">auto</span> <span class="keyword">const</span> &amp; opCodePtr = m_Model-&gt;operator_codes[op-&gt;opcode_index];</div><div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;                <span class="keyword">auto</span> builtinCode = opCodePtr-&gt;builtin_code;</div><div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160;</div><div class="line"><a name="l00642"></a><span class="lineno">  642</span>&#160;                <span class="keywordflow">if</span> (builtinCode &gt; tflite::BuiltinOperator_MAX)</div><div class="line"><a name="l00643"></a><span class="lineno">  643</span>&#160;                {</div><div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160;                    <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00645"></a><span class="lineno">  645</span>&#160;                            boost::str(</div><div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160;                                    boost::format(<span class="stringliteral">&quot;Operator code %1% is out of range 0-%2%. &quot;</span></div><div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160;                                                  <span class="stringliteral">&quot;subgraph:%3% operator idx:%4%. %5%&quot;</span>) %</div><div class="line"><a name="l00648"></a><span class="lineno">  648</span>&#160;                                                  builtinCode %</div><div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;                                                  tflite::BuiltinOperator_MAX %</div><div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160;                                                  subgraphIndex %</div><div class="line"><a name="l00651"></a><span class="lineno">  651</span>&#160;                                                  operatorIndex %</div><div class="line"><a name="l00652"></a><span class="lineno">  652</span>&#160;                                                  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00653"></a><span class="lineno">  653</span>&#160;                }</div><div class="line"><a name="l00654"></a><span class="lineno">  654</span>&#160;</div><div class="line"><a name="l00655"></a><span class="lineno">  655</span>&#160;                <span class="comment">// lookup and call the parser function</span></div><div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160;                <span class="keyword">auto</span> &amp; parserFunction = m_ParserFunctions[builtinCode];</div><div class="line"><a name="l00657"></a><span class="lineno">  657</span>&#160;                (this-&gt;*parserFunction)(subgraphIndex, operatorIndex);</div><div class="line"><a name="l00658"></a><span class="lineno">  658</span>&#160;            }</div><div class="line"><a name="l00659"></a><span class="lineno">  659</span>&#160;            <span class="keywordflow">catch</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>&amp; e)</div><div class="line"><a name="l00660"></a><span class="lineno">  660</span>&#160;            {</div><div class="line"><a name="l00661"></a><span class="lineno">  661</span>&#160;                failedToCreate = <span class="keyword">true</span>;</div><div class="line"><a name="l00662"></a><span class="lineno">  662</span>&#160;                std::stringstream errorString;</div><div class="line"><a name="l00663"></a><span class="lineno">  663</span>&#160;</div><div class="line"><a name="l00664"></a><span class="lineno">  664</span>&#160;                errorString &lt;&lt; <span class="stringliteral">&quot;Failed to parse operator #&quot;</span> &lt;&lt; operatorIndex</div><div class="line"><a name="l00665"></a><span class="lineno">  665</span>&#160;                            &lt;&lt; <span class="stringliteral">&quot; within subgraph #&quot;</span> &lt;&lt; subgraphIndex</div><div class="line"><a name="l00666"></a><span class="lineno">  666</span>&#160;                            &lt;&lt; <span class="stringliteral">&quot; error: &quot;</span> &lt;&lt; e.<a class="code" href="classarmnn_1_1_exception.xhtml#abf843cbb29dec939d0731e491bab6f70">what</a>();</div><div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160;                <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>) &lt;&lt; errorString.str();</div><div class="line"><a name="l00668"></a><span class="lineno">  668</span>&#160;</div><div class="line"><a name="l00669"></a><span class="lineno">  669</span>&#160;                errors &lt;&lt; errorString.str() &lt;&lt; <span class="stringliteral">&quot;\n&quot;</span>;</div><div class="line"><a name="l00670"></a><span class="lineno">  670</span>&#160;            }</div><div class="line"><a name="l00671"></a><span class="lineno">  671</span>&#160;            ++operatorIndex;</div><div class="line"><a name="l00672"></a><span class="lineno">  672</span>&#160;        }</div><div class="line"><a name="l00673"></a><span class="lineno">  673</span>&#160;</div><div class="line"><a name="l00674"></a><span class="lineno">  674</span>&#160;        SetupInputLayers(subgraphIndex);</div><div class="line"><a name="l00675"></a><span class="lineno">  675</span>&#160;        SetupOutputLayers(subgraphIndex);</div><div class="line"><a name="l00676"></a><span class="lineno">  676</span>&#160;        SetupConstantLayers(subgraphIndex);</div><div class="line"><a name="l00677"></a><span class="lineno">  677</span>&#160;</div><div class="line"><a name="l00678"></a><span class="lineno">  678</span>&#160;        ++subgraphIndex;</div><div class="line"><a name="l00679"></a><span class="lineno">  679</span>&#160;    }</div><div class="line"><a name="l00680"></a><span class="lineno">  680</span>&#160;</div><div class="line"><a name="l00681"></a><span class="lineno">  681</span>&#160;    <span class="keywordflow">if</span> (failedToCreate)</div><div class="line"><a name="l00682"></a><span class="lineno">  682</span>&#160;    {</div><div class="line"><a name="l00683"></a><span class="lineno">  683</span>&#160;        <span class="comment">// we can skip everything and let the outer exception handler deal with the error</span></div><div class="line"><a name="l00684"></a><span class="lineno">  684</span>&#160;        <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="l00685"></a><span class="lineno">  685</span>&#160;    }</div><div class="line"><a name="l00686"></a><span class="lineno">  686</span>&#160;</div><div class="line"><a name="l00687"></a><span class="lineno">  687</span>&#160;    <span class="comment">// establish the connections from the layer outputs to the inputs of the subsequent layers</span></div><div class="line"><a name="l00688"></a><span class="lineno">  688</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> subgraphIndex = 0; subgraphIndex &lt; m_SubgraphConnections.size(); ++subgraphIndex)</div><div class="line"><a name="l00689"></a><span class="lineno">  689</span>&#160;    {</div><div class="line"><a name="l00690"></a><span class="lineno">  690</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> tensorIndex = 0; tensorIndex &lt; m_SubgraphConnections[subgraphIndex].size(); ++tensorIndex)</div><div class="line"><a name="l00691"></a><span class="lineno">  691</span>&#160;        {</div><div class="line"><a name="l00692"></a><span class="lineno">  692</span>&#160;            <span class="keywordflow">if</span> (m_SubgraphConnections[subgraphIndex][tensorIndex].outputSlot != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00693"></a><span class="lineno">  693</span>&#160;            {</div><div class="line"><a name="l00694"></a><span class="lineno">  694</span>&#160;                <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> inputSlotIdx = 0;</div><div class="line"><a name="l00695"></a><span class="lineno">  695</span>&#160;                    inputSlotIdx &lt; m_SubgraphConnections[subgraphIndex][tensorIndex].inputSlots.size();</div><div class="line"><a name="l00696"></a><span class="lineno">  696</span>&#160;                    ++inputSlotIdx)</div><div class="line"><a name="l00697"></a><span class="lineno">  697</span>&#160;                {</div><div class="line"><a name="l00698"></a><span class="lineno">  698</span>&#160;                    m_SubgraphConnections[subgraphIndex][tensorIndex].outputSlot-&gt;Connect(</div><div class="line"><a name="l00699"></a><span class="lineno">  699</span>&#160;                        *(m_SubgraphConnections[subgraphIndex][tensorIndex].inputSlots[inputSlotIdx]));</div><div class="line"><a name="l00700"></a><span class="lineno">  700</span>&#160;                }</div><div class="line"><a name="l00701"></a><span class="lineno">  701</span>&#160;            }</div><div class="line"><a name="l00702"></a><span class="lineno">  702</span>&#160;        }</div><div class="line"><a name="l00703"></a><span class="lineno">  703</span>&#160;    }</div><div class="line"><a name="l00704"></a><span class="lineno">  704</span>&#160;</div><div class="line"><a name="l00705"></a><span class="lineno">  705</span>&#160;    <span class="keywordflow">return</span> std::move(m_Network);</div><div class="line"><a name="l00706"></a><span class="lineno">  706</span>&#160;}</div><div class="line"><a name="l00707"></a><span class="lineno">  707</span>&#160;</div><div class="line"><a name="l00708"></a><span class="lineno">  708</span>&#160;<span class="keywordtype">void</span> TfLiteParser::RegisterProducerOfTensor(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00709"></a><span class="lineno">  709</span>&#160;                                            <span class="keywordtype">size_t</span> tensorIndex,</div><div class="line"><a name="l00710"></a><span class="lineno">  710</span>&#160;                                            <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>* slot)</div><div class="line"><a name="l00711"></a><span class="lineno">  711</span>&#160;{</div><div class="line"><a name="l00712"></a><span class="lineno">  712</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#aa1664dc13adbc85ac12fb584b76bfdae">CHECK_TENSOR</a>(m_Model, subgraphIndex, tensorIndex);</div><div class="line"><a name="l00713"></a><span class="lineno">  713</span>&#160;    BOOST_ASSERT(m_SubgraphConnections.size() &gt; subgraphIndex);</div><div class="line"><a name="l00714"></a><span class="lineno">  714</span>&#160;    BOOST_ASSERT(m_SubgraphConnections[subgraphIndex].size() &gt; tensorIndex);</div><div class="line"><a name="l00715"></a><span class="lineno">  715</span>&#160;</div><div class="line"><a name="l00716"></a><span class="lineno">  716</span>&#160;    TensorSlots &amp; tensorSlots = m_SubgraphConnections[subgraphIndex][tensorIndex];</div><div class="line"><a name="l00717"></a><span class="lineno">  717</span>&#160;</div><div class="line"><a name="l00718"></a><span class="lineno">  718</span>&#160;    <span class="comment">// assuming there is only one producer for that tensor</span></div><div class="line"><a name="l00719"></a><span class="lineno">  719</span>&#160;    <span class="keywordflow">if</span> (tensorSlots.outputSlot != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00720"></a><span class="lineno">  720</span>&#160;    {</div><div class="line"><a name="l00721"></a><span class="lineno">  721</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l00722"></a><span class="lineno">  722</span>&#160;                boost::format(<span class="stringliteral">&quot;Another layer has already registered itself as the producer of &quot;</span></div><div class="line"><a name="l00723"></a><span class="lineno">  723</span>&#160;                              <span class="stringliteral">&quot;subgraph:%1% tensor:%2% %3%&quot;</span>) %</div><div class="line"><a name="l00724"></a><span class="lineno">  724</span>&#160;                               subgraphIndex %</div><div class="line"><a name="l00725"></a><span class="lineno">  725</span>&#160;                               tensorIndex %</div><div class="line"><a name="l00726"></a><span class="lineno">  726</span>&#160;                               <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00727"></a><span class="lineno">  727</span>&#160;    }</div><div class="line"><a name="l00728"></a><span class="lineno">  728</span>&#160;</div><div class="line"><a name="l00729"></a><span class="lineno">  729</span>&#160;    tensorSlots.outputSlot = slot;</div><div class="line"><a name="l00730"></a><span class="lineno">  730</span>&#160;}</div><div class="line"><a name="l00731"></a><span class="lineno">  731</span>&#160;</div><div class="line"><a name="l00732"></a><span class="lineno">  732</span>&#160;<span class="keywordtype">void</span> TfLiteParser::RegisterConsumerOfTensor(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00733"></a><span class="lineno">  733</span>&#160;                                            <span class="keywordtype">size_t</span> tensorIndex,</div><div class="line"><a name="l00734"></a><span class="lineno">  734</span>&#160;                                            <a class="code" href="classarmnn_1_1_i_input_slot.xhtml">armnn::IInputSlot</a>* slot)</div><div class="line"><a name="l00735"></a><span class="lineno">  735</span>&#160;{</div><div class="line"><a name="l00736"></a><span class="lineno">  736</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#aa1664dc13adbc85ac12fb584b76bfdae">CHECK_TENSOR</a>(m_Model, subgraphIndex, tensorIndex);</div><div class="line"><a name="l00737"></a><span class="lineno">  737</span>&#160;    BOOST_ASSERT(m_SubgraphConnections.size() &gt; subgraphIndex);</div><div class="line"><a name="l00738"></a><span class="lineno">  738</span>&#160;    BOOST_ASSERT(m_SubgraphConnections[subgraphIndex].size() &gt; tensorIndex);</div><div class="line"><a name="l00739"></a><span class="lineno">  739</span>&#160;</div><div class="line"><a name="l00740"></a><span class="lineno">  740</span>&#160;    TensorSlots &amp; tensorSlots = m_SubgraphConnections[subgraphIndex][tensorIndex];</div><div class="line"><a name="l00741"></a><span class="lineno">  741</span>&#160;    tensorSlots.inputSlots.push_back(slot);</div><div class="line"><a name="l00742"></a><span class="lineno">  742</span>&#160;}</div><div class="line"><a name="l00743"></a><span class="lineno">  743</span>&#160;</div><div class="line"><a name="l00744"></a><span class="lineno">  744</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseCustomOperator(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l00745"></a><span class="lineno">  745</span>&#160;{</div><div class="line"><a name="l00746"></a><span class="lineno">  746</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00747"></a><span class="lineno">  747</span>&#160;</div><div class="line"><a name="l00748"></a><span class="lineno">  748</span>&#160;    <span class="comment">// NOTE: By default we presume the custom operator is not supported</span></div><div class="line"><a name="l00749"></a><span class="lineno">  749</span>&#160;    <span class="keyword">auto</span> customParserFunction = &amp;TfLiteParser::ParseUnsupportedOperator;</div><div class="line"><a name="l00750"></a><span class="lineno">  750</span>&#160;</div><div class="line"><a name="l00751"></a><span class="lineno">  751</span>&#160;    <span class="comment">// Identify custom code defined for custom operator</span></div><div class="line"><a name="l00752"></a><span class="lineno">  752</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span>&amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l00753"></a><span class="lineno">  753</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span>&amp; customCode  = m_Model-&gt;operator_codes[operatorPtr-&gt;opcode_index]-&gt;custom_code;</div><div class="line"><a name="l00754"></a><span class="lineno">  754</span>&#160;</div><div class="line"><a name="l00755"></a><span class="lineno">  755</span>&#160;    <span class="comment">// Find parser function that correspondes to custom code (if any)</span></div><div class="line"><a name="l00756"></a><span class="lineno">  756</span>&#160;    <span class="keyword">auto</span> iterator = m_CustomParserFunctions.find(customCode);</div><div class="line"><a name="l00757"></a><span class="lineno">  757</span>&#160;    <span class="keywordflow">if</span> (iterator != m_CustomParserFunctions.end())</div><div class="line"><a name="l00758"></a><span class="lineno">  758</span>&#160;    {</div><div class="line"><a name="l00759"></a><span class="lineno">  759</span>&#160;        customParserFunction = iterator-&gt;second;</div><div class="line"><a name="l00760"></a><span class="lineno">  760</span>&#160;    }</div><div class="line"><a name="l00761"></a><span class="lineno">  761</span>&#160;</div><div class="line"><a name="l00762"></a><span class="lineno">  762</span>&#160;    <span class="comment">// Run parser function</span></div><div class="line"><a name="l00763"></a><span class="lineno">  763</span>&#160;    (this-&gt;*customParserFunction)(subgraphIndex, operatorIndex);</div><div class="line"><a name="l00764"></a><span class="lineno">  764</span>&#160;}</div><div class="line"><a name="l00765"></a><span class="lineno">  765</span>&#160;</div><div class="line"><a name="l00766"></a><span class="lineno">  766</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseUnsupportedOperator(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l00767"></a><span class="lineno">  767</span>&#160;{</div><div class="line"><a name="l00768"></a><span class="lineno">  768</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00769"></a><span class="lineno">  769</span>&#160;</div><div class="line"><a name="l00770"></a><span class="lineno">  770</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l00771"></a><span class="lineno">  771</span>&#160;</div><div class="line"><a name="l00772"></a><span class="lineno">  772</span>&#160;    <span class="keyword">auto</span> opcodeIndex = operatorPtr-&gt;opcode_index;</div><div class="line"><a name="l00773"></a><span class="lineno">  773</span>&#160;    <span class="keyword">auto</span> opcode      = m_Model-&gt;operator_codes[opcodeIndex]-&gt;builtin_code;</div><div class="line"><a name="l00774"></a><span class="lineno">  774</span>&#160;</div><div class="line"><a name="l00775"></a><span class="lineno">  775</span>&#160;    <span class="keywordflow">if</span> (!m_Options || !m_Options.value().m_StandInLayerForUnsupported)</div><div class="line"><a name="l00776"></a><span class="lineno">  776</span>&#160;    {</div><div class="line"><a name="l00777"></a><span class="lineno">  777</span>&#160;        <span class="comment">// Do not add StandInLayer, throw ParseException instead</span></div><div class="line"><a name="l00778"></a><span class="lineno">  778</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00779"></a><span class="lineno">  779</span>&#160;            boost::str(</div><div class="line"><a name="l00780"></a><span class="lineno">  780</span>&#160;                boost::format(<span class="stringliteral">&quot;Operator not supported. &quot;</span></div><div class="line"><a name="l00781"></a><span class="lineno">  781</span>&#160;                              <span class="stringliteral">&quot;subgraph:%1% operator:%2% &quot;</span></div><div class="line"><a name="l00782"></a><span class="lineno">  782</span>&#160;                              <span class="stringliteral">&quot;opcode_index:%3% opcode:%4% / %5% %6%&quot;</span>) %</div><div class="line"><a name="l00783"></a><span class="lineno">  783</span>&#160;                              subgraphIndex %</div><div class="line"><a name="l00784"></a><span class="lineno">  784</span>&#160;                              operatorIndex %</div><div class="line"><a name="l00785"></a><span class="lineno">  785</span>&#160;                              opcodeIndex %</div><div class="line"><a name="l00786"></a><span class="lineno">  786</span>&#160;                              opcode %</div><div class="line"><a name="l00787"></a><span class="lineno">  787</span>&#160;                              tflite::EnumNameBuiltinOperator(opcode) %</div><div class="line"><a name="l00788"></a><span class="lineno">  788</span>&#160;                              <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00789"></a><span class="lineno">  789</span>&#160;    }</div><div class="line"><a name="l00790"></a><span class="lineno">  790</span>&#160;</div><div class="line"><a name="l00791"></a><span class="lineno">  791</span>&#160;    <span class="keyword">auto</span> inputs  = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00792"></a><span class="lineno">  792</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00793"></a><span class="lineno">  793</span>&#160;</div><div class="line"><a name="l00794"></a><span class="lineno">  794</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numInputs  = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(inputs.size());</div><div class="line"><a name="l00795"></a><span class="lineno">  795</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numOutputs = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(outputs.size());</div><div class="line"><a name="l00796"></a><span class="lineno">  796</span>&#160;</div><div class="line"><a name="l00797"></a><span class="lineno">  797</span>&#160;    <a class="code" href="structarmnn_1_1_stand_in_descriptor.xhtml">StandInDescriptor</a> descriptor(numInputs, numOutputs);</div><div class="line"><a name="l00798"></a><span class="lineno">  798</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;StandIn:%1%:%2%:%3%&quot;</span>) % subgraphIndex % operatorIndex % opcode);</div><div class="line"><a name="l00799"></a><span class="lineno">  799</span>&#160;</div><div class="line"><a name="l00800"></a><span class="lineno">  800</span>&#160;    <span class="comment">// Add a non-executable StandInLayer as a placeholder for any unsupported operator</span></div><div class="line"><a name="l00801"></a><span class="lineno">  801</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-&gt;AddStandInLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l00802"></a><span class="lineno">  802</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0u; i &lt; numOutputs; ++i)</div><div class="line"><a name="l00803"></a><span class="lineno">  803</span>&#160;    {</div><div class="line"><a name="l00804"></a><span class="lineno">  804</span>&#160;        layer-&gt;<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#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[i]));</div><div class="line"><a name="l00805"></a><span class="lineno">  805</span>&#160;    }</div><div class="line"><a name="l00806"></a><span class="lineno">  806</span>&#160;</div><div class="line"><a name="l00807"></a><span class="lineno">  807</span>&#160;    <span class="keyword">auto</span> inputTensorIds  = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l00808"></a><span class="lineno">  808</span>&#160;    <span class="keyword">auto</span> outputTensorIds = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l00809"></a><span class="lineno">  809</span>&#160;</div><div class="line"><a name="l00810"></a><span class="lineno">  810</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, layer, inputTensorIds);</div><div class="line"><a name="l00811"></a><span class="lineno">  811</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIds);</div><div class="line"><a name="l00812"></a><span class="lineno">  812</span>&#160;}</div><div class="line"><a name="l00813"></a><span class="lineno">  813</span>&#160;</div><div class="line"><a name="l00814"></a><span class="lineno">  814</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseConv2D(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l00815"></a><span class="lineno">  815</span>&#160;{</div><div class="line"><a name="l00816"></a><span class="lineno">  816</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00817"></a><span class="lineno">  817</span>&#160;</div><div class="line"><a name="l00818"></a><span class="lineno">  818</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l00819"></a><span class="lineno">  819</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr-&gt;builtin_options.AsConv2DOptions();</div><div class="line"><a name="l00820"></a><span class="lineno">  820</span>&#160;</div><div class="line"><a name="l00821"></a><span class="lineno">  821</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00822"></a><span class="lineno">  822</span>&#160;</div><div class="line"><a name="l00823"></a><span class="lineno">  823</span>&#160;    <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> desc;</div><div class="line"><a name="l00824"></a><span class="lineno">  824</span>&#160;    desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00825"></a><span class="lineno">  825</span>&#160;    desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;stride_w);</div><div class="line"><a name="l00826"></a><span class="lineno">  826</span>&#160;    desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;stride_h);</div><div class="line"><a name="l00827"></a><span class="lineno">  827</span>&#160;    desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00828"></a><span class="lineno">  828</span>&#160;    desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;dilation_w_factor);</div><div class="line"><a name="l00829"></a><span class="lineno">  829</span>&#160;    desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;dilation_h_factor);</div><div class="line"><a name="l00830"></a><span class="lineno">  830</span>&#160;</div><div class="line"><a name="l00831"></a><span class="lineno">  831</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00832"></a><span class="lineno">  832</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2, 3);</div><div class="line"><a name="l00833"></a><span class="lineno">  833</span>&#160;</div><div class="line"><a name="l00834"></a><span class="lineno">  834</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00835"></a><span class="lineno">  835</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l00836"></a><span class="lineno">  836</span>&#160;</div><div class="line"><a name="l00837"></a><span class="lineno">  837</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo  = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l00838"></a><span class="lineno">  838</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> filterTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l00839"></a><span class="lineno">  839</span>&#160;</div><div class="line"><a name="l00840"></a><span class="lineno">  840</span>&#160;    <span class="comment">// assuming input is NHWC</span></div><div class="line"><a name="l00841"></a><span class="lineno">  841</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = inputTensorInfo.GetShape()[1];</div><div class="line"><a name="l00842"></a><span class="lineno">  842</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth  = inputTensorInfo.GetShape()[2];</div><div class="line"><a name="l00843"></a><span class="lineno">  843</span>&#160;</div><div class="line"><a name="l00844"></a><span class="lineno">  844</span>&#160;    <span class="comment">// assuming the filter is OHWI : Output, H, W, Input</span></div><div class="line"><a name="l00845"></a><span class="lineno">  845</span>&#160;    <span class="comment">// which is essentially the same as NHWC</span></div><div class="line"><a name="l00846"></a><span class="lineno">  846</span>&#160;    <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="l00847"></a><span class="lineno">  847</span>&#160;    <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="l00848"></a><span class="lineno">  848</span>&#160;</div><div class="line"><a name="l00849"></a><span class="lineno">  849</span>&#160;    <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputHeight, filterHeight, desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>,</div><div class="line"><a name="l00850"></a><span class="lineno">  850</span>&#160;                desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a>, desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>, desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;padding);</div><div class="line"><a name="l00851"></a><span class="lineno">  851</span>&#160;    <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputWidth, filterWidth, desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>,</div><div class="line"><a name="l00852"></a><span class="lineno">  852</span>&#160;                desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a>, desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>, desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;padding);</div><div class="line"><a name="l00853"></a><span class="lineno">  853</span>&#160;</div><div class="line"><a name="l00854"></a><span class="lineno">  854</span>&#160;    <span class="keyword">auto</span> filterTensorAndData = CreateConstTensor(inputs[1],</div><div class="line"><a name="l00855"></a><span class="lineno">  855</span>&#160;                                                 filterTensorInfo,</div><div class="line"><a name="l00856"></a><span class="lineno">  856</span>&#160;                                                 <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::PermutationVector&amp;&gt;</a>());</div><div class="line"><a name="l00857"></a><span class="lineno">  857</span>&#160;    <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="l00858"></a><span class="lineno">  858</span>&#160;</div><div class="line"><a name="l00859"></a><span class="lineno">  859</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;Conv2D:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l00860"></a><span class="lineno">  860</span>&#160;</div><div class="line"><a name="l00861"></a><span class="lineno">  861</span>&#160;    <span class="keywordflow">if</span> (inputs.size() == 3)</div><div class="line"><a name="l00862"></a><span class="lineno">  862</span>&#160;    {</div><div class="line"><a name="l00863"></a><span class="lineno">  863</span>&#160;        desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00864"></a><span class="lineno">  864</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[2]);</div><div class="line"><a name="l00865"></a><span class="lineno">  865</span>&#160;        <span class="keyword">auto</span> biasTensorAndData = CreateConstTensor(inputs[2],</div><div class="line"><a name="l00866"></a><span class="lineno">  866</span>&#160;                                                   biasTensorInfo,</div><div class="line"><a name="l00867"></a><span class="lineno">  867</span>&#160;                                                   <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::PermutationVector&amp;&gt;</a>());</div><div class="line"><a name="l00868"></a><span class="lineno">  868</span>&#160;        layer = m_Network-&gt;AddConvolution2dLayer(desc,</div><div class="line"><a name="l00869"></a><span class="lineno">  869</span>&#160;                                                 filterTensorAndData.first,</div><div class="line"><a name="l00870"></a><span class="lineno">  870</span>&#160;                                                 <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ConstTensor&gt;</a>(biasTensorAndData.first),</div><div class="line"><a name="l00871"></a><span class="lineno">  871</span>&#160;                                                 layerName.c_str());</div><div class="line"><a name="l00872"></a><span class="lineno">  872</span>&#160;    }</div><div class="line"><a name="l00873"></a><span class="lineno">  873</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l00874"></a><span class="lineno">  874</span>&#160;    {</div><div class="line"><a name="l00875"></a><span class="lineno">  875</span>&#160;        layer = m_Network-&gt;AddConvolution2dLayer(desc,</div><div class="line"><a name="l00876"></a><span class="lineno">  876</span>&#160;                                                 filterTensorAndData.first,</div><div class="line"><a name="l00877"></a><span class="lineno">  877</span>&#160;                                                 <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>(),</div><div class="line"><a name="l00878"></a><span class="lineno">  878</span>&#160;                                                 layerName.c_str());</div><div class="line"><a name="l00879"></a><span class="lineno">  879</span>&#160;    }</div><div class="line"><a name="l00880"></a><span class="lineno">  880</span>&#160;</div><div class="line"><a name="l00881"></a><span class="lineno">  881</span>&#160;    BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00882"></a><span class="lineno">  882</span>&#160;</div><div class="line"><a name="l00883"></a><span class="lineno">  883</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l00884"></a><span class="lineno">  884</span>&#160;    layer-&gt;<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="l00885"></a><span class="lineno">  885</span>&#160;</div><div class="line"><a name="l00886"></a><span class="lineno">  886</span>&#160;    <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="l00887"></a><span class="lineno">  887</span>&#160;    <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l00888"></a><span class="lineno">  888</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l00889"></a><span class="lineno">  889</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l00890"></a><span class="lineno">  890</span>&#160;</div><div class="line"><a name="l00891"></a><span class="lineno">  891</span>&#160;    layer = AddFusedActivationLayer(layer, 0, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;fused_activation_function);</div><div class="line"><a name="l00892"></a><span class="lineno">  892</span>&#160;    <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="l00893"></a><span class="lineno">  893</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l00894"></a><span class="lineno">  894</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l00895"></a><span class="lineno">  895</span>&#160;}</div><div class="line"><a name="l00896"></a><span class="lineno">  896</span>&#160;</div><div class="line"><a name="l00897"></a><span class="lineno">  897</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseDepthwiseConv2D(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l00898"></a><span class="lineno">  898</span>&#160;{</div><div class="line"><a name="l00899"></a><span class="lineno">  899</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00900"></a><span class="lineno">  900</span>&#160;</div><div class="line"><a name="l00901"></a><span class="lineno">  901</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l00902"></a><span class="lineno">  902</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr-&gt;builtin_options.AsDepthwiseConv2DOptions();</div><div class="line"><a name="l00903"></a><span class="lineno">  903</span>&#160;</div><div class="line"><a name="l00904"></a><span class="lineno">  904</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00905"></a><span class="lineno">  905</span>&#160;</div><div class="line"><a name="l00906"></a><span class="lineno">  906</span>&#160;    <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> desc;</div><div class="line"><a name="l00907"></a><span class="lineno">  907</span>&#160;    desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00908"></a><span class="lineno">  908</span>&#160;    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>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;stride_w);</div><div class="line"><a name="l00909"></a><span class="lineno">  909</span>&#160;    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>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;stride_h);</div><div class="line"><a name="l00910"></a><span class="lineno">  910</span>&#160;    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="l00911"></a><span class="lineno">  911</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;depth_multiplier);</div><div class="line"><a name="l00912"></a><span class="lineno">  912</span>&#160;</div><div class="line"><a name="l00913"></a><span class="lineno">  913</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00914"></a><span class="lineno">  914</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2, 3);</div><div class="line"><a name="l00915"></a><span class="lineno">  915</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00916"></a><span class="lineno">  916</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l00917"></a><span class="lineno">  917</span>&#160;    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>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;dilation_w_factor);</div><div class="line"><a name="l00918"></a><span class="lineno">  918</span>&#160;    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>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;dilation_h_factor);</div><div class="line"><a name="l00919"></a><span class="lineno">  919</span>&#160;</div><div class="line"><a name="l00920"></a><span class="lineno">  920</span>&#160;    <span class="comment">// Mappings from TensorflowLite filter tensors to the ArmNN filter tensors (ArmNN weights have to be [M, I, H, W])</span></div><div class="line"><a name="l00921"></a><span class="lineno">  921</span>&#160;    <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> permutationVector{ 2, 3, 1, 0 }; <span class="comment">// [H, W, I, M] -&gt; [M, I, H, W]</span></div><div class="line"><a name="l00922"></a><span class="lineno">  922</span>&#160;  </div><div class="line"><a name="l00923"></a><span class="lineno">  923</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo  = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l00924"></a><span class="lineno">  924</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> filterTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1], permutationVector);</div><div class="line"><a name="l00925"></a><span class="lineno">  925</span>&#160;</div><div class="line"><a name="l00926"></a><span class="lineno">  926</span>&#160;    <span class="comment">// Assuming input is NHWC</span></div><div class="line"><a name="l00927"></a><span class="lineno">  927</span>&#160;    <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="l00928"></a><span class="lineno">  928</span>&#160;    <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="l00929"></a><span class="lineno">  929</span>&#160;</div><div class="line"><a name="l00930"></a><span class="lineno">  930</span>&#160;    <span class="comment">// TensorflowLite weights come in the format [1, H, W, I * M]</span></div><div class="line"><a name="l00931"></a><span class="lineno">  931</span>&#160;    <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="l00932"></a><span class="lineno">  932</span>&#160;    <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="l00933"></a><span class="lineno">  933</span>&#160;</div><div class="line"><a name="l00934"></a><span class="lineno">  934</span>&#160;    <span class="comment">// Reshape weights as [ H, W, I, M ]</span></div><div class="line"><a name="l00935"></a><span class="lineno">  935</span>&#160;    filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ filterHeight,</div><div class="line"><a name="l00936"></a><span class="lineno">  936</span>&#160;                                filterWidth,</div><div class="line"><a name="l00937"></a><span class="lineno">  937</span>&#160;                                inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3],</div><div class="line"><a name="l00938"></a><span class="lineno">  938</span>&#160;                                filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3] / inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3] });</div><div class="line"><a name="l00939"></a><span class="lineno">  939</span>&#160;</div><div class="line"><a name="l00940"></a><span class="lineno">  940</span>&#160;    <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputHeight, filterHeight, desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>,</div><div class="line"><a name="l00941"></a><span class="lineno">  941</span>&#160;                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>, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;padding);</div><div class="line"><a name="l00942"></a><span class="lineno">  942</span>&#160;    <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputWidth, filterWidth, desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>,</div><div class="line"><a name="l00943"></a><span class="lineno">  943</span>&#160;                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>, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;padding);</div><div class="line"><a name="l00944"></a><span class="lineno">  944</span>&#160;</div><div class="line"><a name="l00945"></a><span class="lineno">  945</span>&#160;    <span class="keyword">auto</span> filterTensorAndData = CreateConstTensor(inputs[1], filterTensorInfo, permutationVector);</div><div class="line"><a name="l00946"></a><span class="lineno">  946</span>&#160;    <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="l00947"></a><span class="lineno">  947</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;DepthwiseConv2D:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l00948"></a><span class="lineno">  948</span>&#160;</div><div class="line"><a name="l00949"></a><span class="lineno">  949</span>&#160;    <span class="keywordflow">if</span> (inputs.size() == 3)</div><div class="line"><a name="l00950"></a><span class="lineno">  950</span>&#160;    {</div><div class="line"><a name="l00951"></a><span class="lineno">  951</span>&#160;        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="l00952"></a><span class="lineno">  952</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[2]);</div><div class="line"><a name="l00953"></a><span class="lineno">  953</span>&#160;        <span class="keyword">auto</span> biasTensorAndData = CreateConstTensor(inputs[2],</div><div class="line"><a name="l00954"></a><span class="lineno">  954</span>&#160;                                                   biasTensorInfo,</div><div class="line"><a name="l00955"></a><span class="lineno">  955</span>&#160;                                                   <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::PermutationVector&amp;&gt;</a>());</div><div class="line"><a name="l00956"></a><span class="lineno">  956</span>&#160;        layer = m_Network-&gt;AddDepthwiseConvolution2dLayer(desc,</div><div class="line"><a name="l00957"></a><span class="lineno">  957</span>&#160;                                                          filterTensorAndData.first,</div><div class="line"><a name="l00958"></a><span class="lineno">  958</span>&#160;                                                          <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ConstTensor&gt;</a>(biasTensorAndData.first),</div><div class="line"><a name="l00959"></a><span class="lineno">  959</span>&#160;                                                          layerName.c_str());</div><div class="line"><a name="l00960"></a><span class="lineno">  960</span>&#160;    }</div><div class="line"><a name="l00961"></a><span class="lineno">  961</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l00962"></a><span class="lineno">  962</span>&#160;    {</div><div class="line"><a name="l00963"></a><span class="lineno">  963</span>&#160;        layer = m_Network-&gt;AddDepthwiseConvolution2dLayer(desc,</div><div class="line"><a name="l00964"></a><span class="lineno">  964</span>&#160;                                                          filterTensorAndData.first,</div><div class="line"><a name="l00965"></a><span class="lineno">  965</span>&#160;                                                          <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>(),</div><div class="line"><a name="l00966"></a><span class="lineno">  966</span>&#160;                                                          layerName.c_str());</div><div class="line"><a name="l00967"></a><span class="lineno">  967</span>&#160;    }</div><div class="line"><a name="l00968"></a><span class="lineno">  968</span>&#160;    BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00969"></a><span class="lineno">  969</span>&#160;</div><div class="line"><a name="l00970"></a><span class="lineno">  970</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l00971"></a><span class="lineno">  971</span>&#160;    layer-&gt;<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="l00972"></a><span class="lineno">  972</span>&#160;</div><div class="line"><a name="l00973"></a><span class="lineno">  973</span>&#160;    <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="l00974"></a><span class="lineno">  974</span>&#160;    <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l00975"></a><span class="lineno">  975</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l00976"></a><span class="lineno">  976</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l00977"></a><span class="lineno">  977</span>&#160;</div><div class="line"><a name="l00978"></a><span class="lineno">  978</span>&#160;    layer = AddFusedActivationLayer(layer, 0, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;fused_activation_function);</div><div class="line"><a name="l00979"></a><span class="lineno">  979</span>&#160;    <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="l00980"></a><span class="lineno">  980</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l00981"></a><span class="lineno">  981</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l00982"></a><span class="lineno">  982</span>&#160;}</div><div class="line"><a name="l00983"></a><span class="lineno">  983</span>&#160;</div><div class="line"><a name="l00984"></a><span class="lineno">  984</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseDequantize(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l00985"></a><span class="lineno">  985</span>&#160;{</div><div class="line"><a name="l00986"></a><span class="lineno">  986</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00987"></a><span class="lineno">  987</span>&#160;</div><div class="line"><a name="l00988"></a><span class="lineno">  988</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00989"></a><span class="lineno">  989</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l00990"></a><span class="lineno">  990</span>&#160;</div><div class="line"><a name="l00991"></a><span class="lineno">  991</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00992"></a><span class="lineno">  992</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l00993"></a><span class="lineno">  993</span>&#160;</div><div class="line"><a name="l00994"></a><span class="lineno">  994</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;Dequantize:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l00995"></a><span class="lineno">  995</span>&#160;</div><div class="line"><a name="l00996"></a><span class="lineno">  996</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-&gt;AddDequantizeLayer(layerName.c_str());</div><div class="line"><a name="l00997"></a><span class="lineno">  997</span>&#160;    BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00998"></a><span class="lineno">  998</span>&#160;</div><div class="line"><a name="l00999"></a><span class="lineno">  999</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>&#160;    layer-&gt;<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="l01001"></a><span class="lineno"> 1001</span>&#160;</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>&#160;</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>&#160;}</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>&#160;</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseTranspose(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>&#160;{</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>&#160;</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1, 2);</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>&#160;</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>&#160;</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>&#160;    <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="l01020"></a><span class="lineno"> 1020</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;Transpose:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>&#160;</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>&#160;    <a class="code" href="structarmnn_1_1_transpose_descriptor.xhtml">TransposeDescriptor</a> desc;</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>&#160;</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>&#160; 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   }</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>&#160;</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>&#160;    layer = m_Network-&gt;AddTransposeLayer(desc, layerName.c_str());</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>&#160;</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>&#160;    BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>&#160;</div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>&#160;    layer-&gt;GetOutputSlot(0).SetTensorInfo(outputTensorInfo);</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>&#160;</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>&#160; 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   <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>&#160;</div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr-&gt;builtin_options.AsTransposeConvOptions();</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>&#160;</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>&#160;    <a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">TransposeConvolution2dDescriptor</a> desc;</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>&#160;    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="l01059"></a><span class="lineno"> 1059</span>&#160;    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>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;stride_w);</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>&#160;    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>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;stride_h);</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>&#160;    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="l01062"></a><span class="lineno"> 1062</span>&#160;</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 3);</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>&#160;</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>&#160;</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo  = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[2]);</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> filterTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>&#160;</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>&#160;    <span class="comment">// TfLite uses NHWC tensors</span></div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = inputTensorInfo.GetShape()[1];</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth  = inputTensorInfo.GetShape()[2];</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>&#160;</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>&#160;    <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="l01077"></a><span class="lineno"> 1077</span>&#160;    <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="l01078"></a><span class="lineno"> 1078</span>&#160;</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>&#160;    <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputHeight,</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>&#160;                filterHeight,</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>&#160;                desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>,</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>&#160;                1, <span class="comment">// DilationY</span></div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>&#160;                desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>,</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>&#160;                desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>,</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>&#160;                <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;padding);</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>&#160;</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>&#160;    <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputWidth,</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>&#160;                filterWidth,</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>&#160;                desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>,</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>&#160;                1, <span class="comment">// DilationX</span></div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>&#160;                desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>,</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>&#160;                desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>,</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>&#160;                <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;padding);</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>&#160;</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>&#160;    <span class="keyword">auto</span> filterTensorAndData = CreateConstTensor(inputs[1],</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>&#160;                                                 filterTensorInfo,</div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>&#160;                                                 <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::PermutationVector&amp;&gt;</a>());</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>&#160;</div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>&#160;    <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="l01100"></a><span class="lineno"> 1100</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;TransposeConv:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>&#160;</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>&#160;    layer = m_Network-&gt;AddTransposeConvolution2dLayer(desc,</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>&#160;                                                      filterTensorAndData.first,</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>&#160;                                                      <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>(),</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>&#160;                                                      layerName.c_str());</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>&#160;</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>&#160;    BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>&#160;</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>&#160;    layer-&gt;<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="l01111"></a><span class="lineno"> 1111</span>&#160;</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>&#160;    <span class="comment">// only the tensors for the inputs are relevant, exclude the const (filter) tensor</span></div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[2]});</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>&#160;</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>&#160;}</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>&#160;</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseAveragePool2D(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>&#160;{</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>&#160; 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   <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> blockShapeBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[1]-&gt;buffer);</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>&#160;</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cropsTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[2]);</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>&#160;    <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> cropsBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[2]-&gt;buffer);</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>&#160;</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>&#160; 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   <span class="keywordtype">size_t</span> step = 2;</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>&#160;    std::vector&lt;std::pair&lt;unsigned int, unsigned int&gt;&gt; crops;</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; cropsTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / step; ++i)</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>&#160;    {</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>&#160;        crops.emplace_back(cropsVector[i * step], cropsVector[i * step + 1]);</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>&#160;    }</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>&#160;</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>&#160;    <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">armnn::BatchToSpaceNdDescriptor</a> desc;</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>&#160;    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="l01156"></a><span class="lineno"> 1156</span>&#160;    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="l01157"></a><span class="lineno"> 1157</span>&#160;    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="l01158"></a><span class="lineno"> 1158</span>&#160;</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>&#160;</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;BatchToSpaceND:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-&gt;AddBatchToSpaceNdLayer(desc, layerName.c_str());</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>&#160;</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>&#160;    layer-&gt;<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="l01165"></a><span class="lineno"> 1165</span>&#160;</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>&#160; 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   <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>&#160;</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>&#160;</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>&#160;</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>&#160;    <a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml">L2NormalizationDescriptor</a> desc;</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>&#160;    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="l01185"></a><span class="lineno"> 1185</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;L2Normalization:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>&#160; 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   ParsePool(subgraphIndex, operatorIndex, PoolingAlgorithm::Max);</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>&#160;}</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>&#160;</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseMaximum(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>&#160;{</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>&#160;</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>&#160;</div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>&#160;</div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo  = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>&#160; 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   layer-&gt;<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="l01223"></a><span class="lineno"> 1223</span>&#160;</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>&#160;    <span class="keywordflow">if</span> (inputTensorInfo.GetNumDimensions() != input1TensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>&#160;    {</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>&#160;        AddBroadcastReshapeLayer(subgraphIndex, operatorIndex, layer);</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>&#160;    }</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>&#160;    {</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>&#160;        RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>&#160;    }</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>&#160;</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>&#160;}</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>&#160;</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseMinimum(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>&#160;{</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>&#160;</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>&#160;</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>&#160;</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo  = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>&#160;</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;Minimum:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-&gt;AddMinimumLayer(layerName.c_str());</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>&#160;</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>&#160;    layer-&gt;<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="l01256"></a><span class="lineno"> 1256</span>&#160;</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>&#160;    <span class="keywordflow">if</span> (inputTensorInfo.GetNumDimensions() != input1TensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>&#160;    {</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>&#160;        AddBroadcastReshapeLayer(subgraphIndex, operatorIndex, layer);</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span>&#160;    }</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>&#160;    {</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>&#160;        RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>&#160;    }</div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>&#160;</div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>&#160;}</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>&#160;</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParsePool(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>&#160;                             <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>&#160;                             <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718">PoolingAlgorithm</a> algorithm)</div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>&#160;{</div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>&#160;</div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr-&gt;builtin_options.AsPool2DOptions();</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>&#160;</div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>&#160;</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>&#160;    std::string layerName;</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>&#160;</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>&#160;    <span class="keywordflow">switch</span> (algorithm)</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>&#160;    {</div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>&#160;        <span class="keywordflow">case</span> PoolingAlgorithm::Average:</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>&#160;            layerName =</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>&#160;                boost::str(boost::format(<span class="stringliteral">&quot;AveragePool2D:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>&#160;        <span class="keywordflow">case</span> PoolingAlgorithm::Max:</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>&#160;            layerName =</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>&#160;                boost::str(boost::format(<span class="stringliteral">&quot;MaxPool2D:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>&#160;        <span class="keywordflow">default</span>:</div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>&#160;            BOOST_ASSERT_MSG(<span class="keyword">false</span>, <span class="stringliteral">&quot;Unsupported Pooling Algorithm&quot;</span>);</div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>&#160;    }</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>&#160;</div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>&#160;    <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> desc;</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>&#160;</div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>&#160;    desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = algorithm;</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>&#160;    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>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;stride_w);</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>&#160;    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>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;stride_h);</div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>&#160;    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>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;filter_width);</div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>&#160;    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>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;filter_height);</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>&#160;    desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = PaddingMethod::Exclude;</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>&#160;    desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#affb5b68b3eba3ed45a06c7cde7781962">m_OutputShapeRounding</a> = OutputShapeRounding::Floor;</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>&#160;    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="l01308"></a><span class="lineno"> 1308</span>&#160;</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo  = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>&#160;</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>&#160;    <span class="comment">// assuming input is NHWC</span></div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = inputTensorInfo.GetShape()[1];</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth  = inputTensorInfo.GetShape()[2];</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>&#160;</div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>&#160;    <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(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="l01318"></a><span class="lineno"> 1318</span>&#160;                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>, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;padding);</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>&#160;    <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(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="l01320"></a><span class="lineno"> 1320</span>&#160;                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>, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;padding);</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>&#160;</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>&#160;</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>&#160; 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   <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="l01333"></a><span class="lineno"> 1333</span>&#160;    <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>&#160;</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>&#160;    layer = AddFusedActivationLayer(layer, 0, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;fused_activation_function);</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>&#160;    <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="l01339"></a><span class="lineno"> 1339</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>&#160;}</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span>&#160;</div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseSlice(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>&#160;{</div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span>&#160;</div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 3);</div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>&#160;</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>&#160;    <a class="code" href="structarmnn_1_1_slice_descriptor.xhtml">SliceDescriptor</a> desc;</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>&#160;</div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>&#160;    <span class="comment">// set begin tensor info for slice descriptor</span></div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> beginTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>&#160;    <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> beginBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[1]-&gt;buffer);</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>&#160;</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>&#160;    std::vector&lt;unsigned int&gt; begin(beginTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>&#160;    ::memcpy(begin.data(), beginBufferPtr-&gt;data.data(), beginTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>&#160;</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>&#160;    <span class="comment">// set size tensor info for slice descriptor</span></div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> sizeTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[2]);</div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>&#160;    <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> sizeBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[2]-&gt;buffer);</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>&#160;</div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>&#160;    std::vector&lt;unsigned int&gt; size(sizeTensorInfo.GetNumElements());</div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>&#160;    ::memcpy(size.data(), sizeBufferPtr-&gt;data.data(), sizeTensorInfo.GetNumBytes());</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>&#160;    desc = <a class="code" href="structarmnn_1_1_slice_descriptor.xhtml">SliceDescriptor</a>(begin, size);</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>&#160;</div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;Slice:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-&gt;AddSliceLayer(desc, layerName.c_str());</div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span>&#160;</div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span>&#160; 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   RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>&#160;}</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span>&#160;</div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseSoftmax(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span>&#160;{</div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr-&gt;builtin_options.AsSoftmaxOptions();</div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>&#160;</div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>&#160;    <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">SoftmaxDescriptor</a> desc;</div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>&#160;    desc.<a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;beta;</div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>&#160;</div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>&#160;</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span>&#160; 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i &lt; padListTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / step; ++i)</div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>&#160;    {</div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span>&#160;        padList.emplace_back(padListVector[i * step], padListVector[i * step + 1]);</div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>&#160;    }</div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>&#160;</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>&#160;    <a class="code" href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml">armnn::SpaceToBatchNdDescriptor</a> desc;</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>&#160;    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="l01446"></a><span class="lineno"> 1446</span>&#160; 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&amp; squeezeDimsIn,</div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span>&#160;                                                     <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> &amp; inputTensorInfo)</div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span>&#160;{</div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(squeezeDimsIn.size(), 0, 1, 2, 3, 4);</div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span>&#160;    std::vector&lt;uint32_t&gt; squeezeDims = squeezeDimsIn;</div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span>&#160;    <span class="keyword">static</span> <span class="keyword">const</span> uint32_t dimensionSequence[] = { 0, 1, 2, 3 };</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span>&#160;</div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>&#160; 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<span class="stringliteral">&quot; &quot;</span></div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>&#160;           &lt;&lt; <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString();</div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>&#160;        <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="l01477"></a><span class="lineno"> 1477</span>&#160;    }</div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>&#160;</div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>&#160;    <span class="keywordflow">if</span> (squeezeDims.empty())</div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>&#160;    {</div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</span>&#160;        squeezeDims.assign(dimensionSequence,</div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span>&#160; 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   RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span>&#160;</div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span>&#160;}</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>&#160;</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseStridedSlice(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>&#160;{</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</span>&#160;</div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 4);</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>&#160;</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>&#160;</div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr-&gt;builtin_options.AsStridedSliceOptions();</div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>&#160;</div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>&#160;    <a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml">StridedSliceDescriptor</a> desc;</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>&#160;    desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a61081be1483984e33db452c75d569f51">m_BeginMask</a> = <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;begin_mask;</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>&#160;    desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#af996d82c47e43a16f4c8faa6c6b3e030">m_EllipsisMask</a> = <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;ellipsis_mask;</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>&#160;    desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#ac37e49c0d6e6e54f9d2015d0f11f8ee7">m_EndMask</a> = <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;end_mask;</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>&#160;    desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a7c91eda2b331d607bae92cd8ebf50bb9">m_NewAxisMask</a> = <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;new_axis_mask;</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>&#160;    desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a6d0384878432cfc9652b7ae8bc59506f">m_ShrinkAxisMask</a> = <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;shrink_axis_mask;</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>&#160;    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="l01567"></a><span class="lineno"> 1567</span>&#160;</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> beginTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>&#160;    <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> beginBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[1]-&gt;buffer);</div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>&#160;</div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span>&#160;    std::vector&lt;int&gt; begin(beginTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span>&#160;    ::memcpy(begin.data(), beginBufferPtr-&gt;data.data(), beginTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>&#160;</div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> endTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[2]);</div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span>&#160;    <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> endBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[2]-&gt;buffer);</div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span>&#160;</div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>&#160;    std::vector&lt;int&gt; end(endTensorInfo.GetNumElements());</div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span>&#160;    ::memcpy(end.data(), endBufferPtr-&gt;data.data(), endTensorInfo.GetNumBytes());</div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span>&#160;</div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> strideTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[3]);</div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span>&#160;    <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> strideBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[3]-&gt;buffer);</div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</span>&#160;</div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span>&#160; 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   layer-&gt;<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="l01624"></a><span class="lineno"> 1624</span>&#160;</div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>&#160;    <span class="keywordflow">if</span> (inputTensorInfo.GetNumDimensions() != input1TensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>&#160;    {</div><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span>&#160;        AddBroadcastReshapeLayer(subgraphIndex, operatorIndex, layer);</div><div class="line"><a name="l01629"></a><span class="lineno"> 1629</span>&#160;    }</div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>&#160;    {</div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span>&#160;        RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</span>&#160;    }</div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span>&#160;</div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>&#160;    layer = AddFusedActivationLayer(layer, 0, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;fused_activation_function);</div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span>&#160;</div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span>&#160;}</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span>&#160;</div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseAdd(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span>&#160;{</div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span>&#160;</div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr-&gt;builtin_options.AsAddOptions();</div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>&#160;</div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>&#160;</div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span>&#160;</div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo  = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span>&#160;</div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;Add:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-&gt;AddAdditionLayer(layerName.c_str());</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>&#160;</div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span>&#160;    layer-&gt;<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="l01662"></a><span class="lineno"> 1662</span>&#160;</div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span>&#160;    <span class="keywordflow">if</span> (inputTensorInfo.GetNumDimensions() != input1TensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>&#160;    {</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span>&#160;        AddBroadcastReshapeLayer(subgraphIndex, operatorIndex, layer);</div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span>&#160;    }</div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span>&#160;    {</div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span>&#160;        RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span>&#160;    }</div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span>&#160;</div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span>&#160;    layer = AddFusedActivationLayer(layer, 0, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;fused_activation_function);</div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span>&#160;</div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>&#160;}</div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span>&#160;</div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseMul(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span>&#160;{</div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span>&#160;</div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr-&gt;builtin_options.AsMulOptions();</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>&#160;</div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</span>&#160;</div><div class="line"><a name="l01689"></a><span class="lineno"> 1689</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span>&#160;</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo  = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span>&#160;</div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;Mul:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-&gt;AddMultiplicationLayer(layerName.c_str());</div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span>&#160;</div><div class="line"><a name="l01698"></a><span class="lineno"> 1698</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>&#160;    layer-&gt;<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="l01700"></a><span class="lineno"> 1700</span>&#160;</div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span>&#160;    <span class="keywordflow">if</span> (inputTensorInfo.GetNumDimensions() != input1TensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span>&#160;    {</div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span>&#160;        AddBroadcastReshapeLayer(subgraphIndex, operatorIndex, layer);</div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span>&#160;    }</div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span>&#160;    {</div><div class="line"><a name="l01708"></a><span class="lineno"> 1708</span>&#160;        RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l01709"></a><span class="lineno"> 1709</span>&#160;    }</div><div class="line"><a name="l01710"></a><span class="lineno"> 1710</span>&#160;</div><div class="line"><a name="l01711"></a><span class="lineno"> 1711</span>&#160;    layer = AddFusedActivationLayer(layer, 0, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;fused_activation_function);</div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span>&#160;</div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>&#160; 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   <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>&#160;</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>&#160;</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>&#160;</div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>&#160; 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   ::memcpy(axis.data(), bufferPtr-&gt;data.data(), dimTensorInfo.GetNumBytes());</div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span>&#160;    desc.<a class="code" href="structarmnn_1_1_mean_descriptor.xhtml#a1f0d67b087c491248bd1cde3ff995a95">m_Axis</a> = axis;</div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>&#160;</div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo  = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span>&#160;</div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span>&#160;    desc.<a class="code" href="structarmnn_1_1_mean_descriptor.xhtml#a28e0548abfc4e79c48f29a3d11a062e9">m_KeepDims</a> =</div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span>&#160;        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="l01739"></a><span class="lineno"> 1739</span>&#160;            <a class="code" href="_cl_layer_tests_8cpp.xhtml#a37f1c3ccc9fc906be85185350dd83d48">true</a> : <span class="keyword">false</span>;</div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span>&#160;</div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;Mean:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-&gt;AddMeanLayer(desc, layerName.c_str());</div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>&#160;</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>&#160;    layer-&gt;<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="l01745"></a><span class="lineno"> 1745</span>&#160;</div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span>&#160;</div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>&#160;}</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span>&#160;</div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParsePad(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>&#160;{</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span>&#160;</div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span>&#160;    <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParser::TensorRawPtrVector</a> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span>&#160;</div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>&#160;    <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParser::TensorRawPtrVector</a> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>&#160;</div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> padTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>&#160;    <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> bufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[1]-&gt;buffer);</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>&#160;</div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>&#160;    std::vector&lt;unsigned int&gt; padBuffer(padTensorInfo.GetNumElements());</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span>&#160;    ::memcpy(padBuffer.data(), bufferPtr-&gt;data.data(), padTensorInfo.GetNumBytes());</div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span>&#160;</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>&#160;    <span class="keywordtype">size_t</span> step = 2;</div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span>&#160;    <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a> desc;</div><div class="line"><a name="l01770"></a><span class="lineno"> 1770</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; padTensorInfo.GetNumElements() / step; ++i)</div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span>&#160;    {</div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span>&#160;        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="l01773"></a><span class="lineno"> 1773</span>&#160;    }</div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span>&#160;</div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;Pad:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-&gt;AddPadLayer(desc, layerName.c_str());</div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span>&#160;</div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>&#160;    layer-&gt;<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>&#160;</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>&#160;</div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span>&#160;}</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span>&#160;</div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseQuantize(<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>&#160;{</div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>&#160;    <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>&#160;</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>&#160;    <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>&#160;</div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span>&#160;    <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>&#160;</div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;Quantize:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span>&#160;</div><div class="line"><a name="l01800"></a><span class="lineno"> 1800</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-&gt;AddQuantizeLayer(layerName.c_str());</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>&#160;    BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01802"></a><span class="lineno"> 1802</span>&#160;</div><div class="line"><a name="l01803"></a><span class="lineno"> 1803</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01804"></a><span class="lineno"> 1804</span>&#160;    layer-&gt;<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="l01805"></a><span class="lineno"> 1805</span>&#160;</div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>&#160;</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>&#160;}</div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span>&#160;</div><div class="line"><a name="l01813"></a><span class="lineno"> 1813</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseRelu(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01814"></a><span class="lineno"> 1814</span>&#160;{</div><div class="line"><a name="l01815"></a><span class="lineno"> 1815</span>&#160;    ParseActivation(subgraphIndex,operatorIndex, ActivationFunction::ReLu);</div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span>&#160;}</div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span>&#160;</div><div class="line"><a name="l01818"></a><span class="lineno"> 1818</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseRelu6(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</span>&#160;{</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span>&#160;    ParseActivation(subgraphIndex,operatorIndex, ActivationFunction::BoundedReLu);</div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>&#160;}</div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span>&#160;</div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseLogistic(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span>&#160;{</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>&#160;    ParseActivation(subgraphIndex,operatorIndex,ActivationFunction::Sigmoid);</div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span>&#160;}</div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span>&#160;</div><div class="line"><a name="l01828"></a><span class="lineno"> 1828</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseTanH(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01829"></a><span class="lineno"> 1829</span>&#160;{</div><div class="line"><a name="l01830"></a><span class="lineno"> 1830</span>&#160;    ParseActivation(subgraphIndex,operatorIndex,ActivationFunction::TanH);</div><div class="line"><a name="l01831"></a><span class="lineno"> 1831</span>&#160;}</div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span>&#160;</div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span>&#160;</div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>&#160;<span class="keywordtype">void</span> TfLiteParser::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="l01835"></a><span class="lineno"> 1835</span>&#160;{</div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l01838"></a><span class="lineno"> 1838</span>&#160;    <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(operatorPtr);</div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span>&#160;</div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01841"></a><span class="lineno"> 1841</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l01842"></a><span class="lineno"> 1842</span>&#160;</div><div class="line"><a name="l01843"></a><span class="lineno"> 1843</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01844"></a><span class="lineno"> 1844</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01845"></a><span class="lineno"> 1845</span>&#160;</div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span>&#160;    <span class="keyword">auto</span> layerName = str(boost::format(<span class="stringliteral">&quot;Activation:&quot;</span>));</div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>&#160;    <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDesc;</div><div class="line"><a name="l01848"></a><span class="lineno"> 1848</span>&#160;    activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = activationType;</div><div class="line"><a name="l01849"></a><span class="lineno"> 1849</span>&#160;</div><div class="line"><a name="l01850"></a><span class="lineno"> 1850</span>&#160;    <span class="keywordflow">switch</span> (activationType)</div><div class="line"><a name="l01851"></a><span class="lineno"> 1851</span>&#160;    {</div><div class="line"><a name="l01852"></a><span class="lineno"> 1852</span>&#160;        <span class="keywordflow">case</span> ActivationFunction::ReLu:</div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span>&#160;        {</div><div class="line"><a name="l01854"></a><span class="lineno"> 1854</span>&#160;            layerName += str(boost::format(<span class="stringliteral">&quot;RELU:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01855"></a><span class="lineno"> 1855</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>&#160;        }</div><div class="line"><a name="l01857"></a><span class="lineno"> 1857</span>&#160;        <span class="keywordflow">case</span> ActivationFunction::BoundedReLu:</div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</span>&#160;        {</div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</span>&#160;            layerName += str(boost::format(<span class="stringliteral">&quot;RELU6:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>&#160;            activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 6.0f;</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span>&#160;            activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = 0.0f;</div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span>&#160;        }</div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span>&#160;        <span class="keywordflow">case</span> ActivationFunction::Sigmoid:</div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span>&#160;        {</div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span>&#160;            layerName += str(boost::format(<span class="stringliteral">&quot;SIGMOID:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l01868"></a><span class="lineno"> 1868</span>&#160;        }</div><div class="line"><a name="l01869"></a><span class="lineno"> 1869</span>&#160;        <span class="keywordflow">case</span> ActivationFunction::TanH:</div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>&#160;        {</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span>&#160;            layerName += str(boost::format(<span class="stringliteral">&quot;TANH:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span>&#160;            activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span>&#160;            activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = 1.0f;</div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l01875"></a><span class="lineno"> 1875</span>&#160;        }</div><div class="line"><a name="l01876"></a><span class="lineno"> 1876</span>&#160;        <span class="keywordflow">default</span>:</div><div class="line"><a name="l01877"></a><span class="lineno"> 1877</span>&#160;        {</div><div class="line"><a name="l01878"></a><span class="lineno"> 1878</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</span>&#160;                boost::str(boost::format(<span class="stringliteral">&quot;Unexpected ActivationFunction[%1%] when creating layerName &quot;</span></div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span>&#160;                                         <span class="stringliteral">&quot; %2% &quot;</span>) %static_cast&lt;int&gt;(activationType)% <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01881"></a><span class="lineno"> 1881</span>&#160;        }</div><div class="line"><a name="l01882"></a><span class="lineno"> 1882</span>&#160;    }</div><div class="line"><a name="l01883"></a><span class="lineno"> 1883</span>&#160;</div><div class="line"><a name="l01884"></a><span class="lineno"> 1884</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-&gt;AddActivationLayer(activationDesc, layerName.c_str());</div><div class="line"><a name="l01885"></a><span class="lineno"> 1885</span>&#160;</div><div class="line"><a name="l01886"></a><span class="lineno"> 1886</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span>&#160;    layer-&gt;<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="l01888"></a><span class="lineno"> 1888</span>&#160;</div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>&#160;    <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="l01890"></a><span class="lineno"> 1890</span>&#160;    <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01893"></a><span class="lineno"> 1893</span>&#160;</div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>&#160;    <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="l01895"></a><span class="lineno"> 1895</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span>&#160;}</div><div class="line"><a name="l01898"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaebfa9a01a0bb8a0935114ff0140cc45"> 1898</a></span>&#160;<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.xhtml#aaebfa9a01a0bb8a0935114ff0140cc45">TfLiteParser::OutputShapeOfReshape</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> &amp; inputTensorInfo,</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span>&#160;                                                     <span class="keyword">const</span> std::vector&lt;int32_t&gt; &amp; targetDimsIn)</div><div class="line"><a name="l01900"></a><span class="lineno"> 1900</span>&#160;{</div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>&#160;    std::vector&lt;unsigned int&gt; outputDims(targetDimsIn.begin(), targetDimsIn.end());</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> stretchDim = std::find(targetDimsIn.begin(), targetDimsIn.end(), -1);</div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span>&#160;</div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>&#160;    <span class="keywordflow">if</span> (stretchDim != targetDimsIn.end())</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>&#160;    {</div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>&#160;        <span class="keywordflow">if</span> (std::find(std::next(stretchDim), targetDimsIn.end(), -1) != targetDimsIn.end())</div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>&#160;        {</div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>&#160;                boost::str(</div><div class="line"><a name="l01910"></a><span class="lineno"> 1910</span>&#160;                    boost::format(<span class="stringliteral">&quot;At most one component of shape can be -1 %1%&quot;</span>) % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>&#160;        }</div><div class="line"><a name="l01912"></a><span class="lineno"> 1912</span>&#160;</div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>&#160;        <span class="keyword">auto</span> targetNumElements =</div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span>&#160;            <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(</div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>&#160;                std::accumulate(targetDimsIn.begin(), targetDimsIn.end(), -1, std::multiplies&lt;int32_t&gt;()));</div><div class="line"><a name="l01916"></a><span class="lineno"> 1916</span>&#160;</div><div class="line"><a name="l01917"></a><span class="lineno"> 1917</span>&#160;        <span class="keyword">auto</span> stretchIndex = <span class="keyword">static_cast&lt;</span><span class="keywordtype">size_t</span><span class="keyword">&gt;</span>(std::distance(targetDimsIn.begin(), stretchDim));</div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>&#160;        outputDims[stretchIndex] = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / targetNumElements;</div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span>&#160;    }</div><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>&#160;</div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span>&#160;    <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&lt;unsigned int&gt;(outputDims.size()), outputDims.data());</div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>&#160;</div><div class="line"><a name="l01923"></a><span class="lineno"> 1923</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> reshapeInfo = inputTensorInfo;</div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>&#160;    reshapeInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(outputShape);</div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span>&#160;</div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span>&#160;    <span class="keywordflow">return</span> reshapeInfo;</div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span>&#160;}</div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span>&#160;</div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseReshape(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01930"></a><span class="lineno"> 1930</span>&#160;{</div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>&#160;</div><div class="line"><a name="l01933"></a><span class="lineno"> 1933</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>&#160;</div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>&#160;</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr-&gt;builtin_options.AsReshapeOptions();</div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>&#160;</div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo  = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> actualOutputTensorInfo  = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01943"></a><span class="lineno"> 1943</span>&#160;</div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>&#160;    std::vector&lt;int32_t&gt; targetShape;</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>&#160;    <span class="keywordflow">if</span> (inputs.size() &gt; 1 &amp;&amp; inputs[1] != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>&#160;    {</div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>&#160;        <span class="keywordflow">if</span> (<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>&#160;        {</div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span>&#160;            <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>(<span class="stringliteral">&quot;Target shape defined in reshape parameters and input tensor. &quot;</span></div><div class="line"><a name="l01950"></a><span class="lineno"> 1950</span>&#160;                                        <span class="stringliteral">&quot;Only one method expected&quot;</span>);</div><div class="line"><a name="l01951"></a><span class="lineno"> 1951</span>&#160;        }</div><div class="line"><a name="l01952"></a><span class="lineno"> 1952</span>&#160;</div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span>&#160;        <span class="keywordflow">if</span> (inputs[1]-&gt;is_variable)</div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>&#160;        {</div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>&#160;            <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>( <span class="stringliteral">&quot;Target shapes defined in non-const input tensors is not supported&quot;</span>);</div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>&#160;        }</div><div class="line"><a name="l01957"></a><span class="lineno"> 1957</span>&#160;</div><div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>&#160;        <span class="keywordflow">if</span> (inputs[1]-&gt;shape.size() != 1)</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>&#160;        {</div><div class="line"><a name="l01960"></a><span class="lineno"> 1960</span>&#160;            <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>(<span class="stringliteral">&quot;Target &#39;shape&#39; input is not a 1D tensor&quot;</span>);</div><div class="line"><a name="l01961"></a><span class="lineno"> 1961</span>&#160;        }</div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>&#160;</div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>&#160;        <span class="keywordflow">if</span> (inputs[1]-&gt;type != tflite::TensorType_INT32)</div><div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>&#160;        {</div><div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>&#160;            <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>(<span class="stringliteral">&quot;Target &#39;shape&#39; input is not an int32 type&quot;</span>);</div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span>&#160;        }</div><div class="line"><a name="l01967"></a><span class="lineno"> 1967</span>&#160;</div><div class="line"><a name="l01968"></a><span class="lineno"> 1968</span>&#160;        <span class="keyword">auto</span> bufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[1]-&gt;buffer);</div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span>&#160;        <span class="keyword">auto</span> vals = <span class="keyword">reinterpret_cast&lt;</span><span class="keyword">const </span>int32_t*<span class="keyword">&gt;</span>(bufferPtr-&gt;data.data());</div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i=0; i &lt; inputs[1]-&gt;shape[0]; i++)</div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>&#160;        {</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>&#160;            targetShape.push_back(vals[i]);</div><div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>&#160;        }</div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</span>&#160;    }</div><div class="line"><a name="l01975"></a><span class="lineno"> 1975</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>&#160;    {</div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>&#160;        <span class="keywordflow">if</span> (<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>&#160;        {</div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>&#160;            <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>(<span class="stringliteral">&quot;Target shape not defined in reshape parameters or input tensor. &quot;</span></div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>&#160;                                        <span class="stringliteral">&quot;At least one method required&quot;</span>);</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>&#160;        }</div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>&#160;</div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>&#160;        targetShape = <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;new_shape;</div><div class="line"><a name="l01984"></a><span class="lineno"> 1984</span>&#160;    }</div><div class="line"><a name="l01985"></a><span class="lineno"> 1985</span>&#160;</div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> reshapeOutputTensorInfo =</div><div class="line"><a name="l01987"></a><span class="lineno"> 1987</span>&#160;        <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaebfa9a01a0bb8a0935114ff0140cc45">TfLiteParser::OutputShapeOfReshape</a>(inputTensorInfo, targetShape);</div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span>&#160;</div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span>&#160;    <span class="comment">// Check for valid input size and that reshape parameters equal output shape</span></div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>&amp; reshapeOutputTensorShape = reshapeOutputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>&#160;    <span class="keywordflow">if</span> (inputs.size() &gt; 1 &amp;&amp; !<a class="code" href="namespacearmnn_deserializer.xhtml#a97b50f22cd99f0e09e6e48d20a35f6b2">CheckShape</a>(reshapeOutputTensorShape, outputs[0]-&gt;shape))</div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>&#160;    {</div><div class="line"><a name="l01993"></a><span class="lineno"> 1993</span>&#160;        std::stringstream ss;</div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>&#160;        ss &lt;&lt; <span class="stringliteral">&quot;New shape defined in reshape parameters &quot;</span></div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>&#160;           &lt;&lt; reshapeOutputTensorShape</div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>&#160;           &lt;&lt; <span class="stringliteral">&quot; does not equal output shape &quot;</span></div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>&#160;           &lt;&lt; actualOutputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>&#160;           &lt;&lt; <span class="stringliteral">&quot;: &quot;</span></div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span>&#160;           &lt;&lt; <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString();</div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>&#160;        <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="l02001"></a><span class="lineno"> 2001</span>&#160;    }</div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>&#160;</div><div class="line"><a name="l02003"></a><span class="lineno"> 2003</span>&#160;    <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">ReshapeDescriptor</a> reshapeDesc;</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>&#160;    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="l02005"></a><span class="lineno"> 2005</span>&#160;</div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;Reshape:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02007"></a><span class="lineno"> 2007</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-&gt;AddReshapeLayer(reshapeDesc, layerName.c_str());</div><div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>&#160;    layer-&gt;<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="l02009"></a><span class="lineno"> 2009</span>&#160;</div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>&#160;</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>&#160;}</div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>&#160;</div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseResizeBilinear(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>&#160;{</div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>&#160;    ParseResize(subgraphIndex, operatorIndex, ResizeMethod::Bilinear);</div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span>&#160;}</div><div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>&#160;</div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseResizeNearestNeighbor(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02023"></a><span class="lineno"> 2023</span>&#160;{</div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>&#160;    ParseResize(subgraphIndex, operatorIndex, ResizeMethod::NearestNeighbor);</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>&#160;}</div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>&#160;</div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>&#160;<span class="keywordtype">void</span> TfLiteParser::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="l02028"></a><span class="lineno"> 2028</span>&#160;{</div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>&#160;</div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span>&#160;</div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span>&#160;</div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> sizeTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span>&#160;</div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>&#160;    <span class="comment">// Data for the parsed tensor args (size) must be stored locally.</span></div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>&#160;    std::vector&lt;int32_t&gt; sizeTensorData(sizeTensorInfo.GetNumElements());</div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span>&#160;</div><div class="line"><a name="l02042"></a><span class="lineno"> 2042</span>&#160;    <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> sizeBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[1]-&gt;buffer);</div><div class="line"><a name="l02043"></a><span class="lineno"> 2043</span>&#160;    ::memcpy(sizeTensorData.data(), sizeBufferPtr-&gt;data.data(), sizeTensorInfo.GetNumBytes());</div><div class="line"><a name="l02044"></a><span class="lineno"> 2044</span>&#160;</div><div class="line"><a name="l02045"></a><span class="lineno"> 2045</span>&#160;    <a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">ResizeDescriptor</a> desc;</div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>&#160;    desc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">m_Method</a>       = resizeMethod;</div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span>&#160;    desc.m_TargetHeight = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span> (sizeTensorData[0]);</div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span>&#160;    desc.m_TargetWidth  = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span> (sizeTensorData[1]);</div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>&#160;    desc.m_DataLayout   = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span>&#160;</div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</span>&#160;    <span class="keyword">auto</span> layerName = str(boost::format(<span class="stringliteral">&quot;Resize:&quot;</span>));</div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span>&#160;</div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span>&#160;    <span class="keywordflow">switch</span> (resizeMethod)</div><div class="line"><a name="l02054"></a><span class="lineno"> 2054</span>&#160;    {</div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span>&#160;        <span class="keywordflow">case</span> ResizeMethod::Bilinear:</div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span>&#160;        {</div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span>&#160;            layerName += str(boost::format(<span class="stringliteral">&quot;BILINEAR:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02058"></a><span class="lineno"> 2058</span>&#160;</div><div class="line"><a name="l02059"></a><span class="lineno"> 2059</span>&#160;            <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span>&#160;            <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>     = operatorPtr-&gt;builtin_options.AsResizeBilinearOptions();</div><div class="line"><a name="l02061"></a><span class="lineno"> 2061</span>&#160;</div><div class="line"><a name="l02062"></a><span class="lineno"> 2062</span>&#160;            desc.m_BilinearAlignCorners = <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;align_corners;</div><div class="line"><a name="l02063"></a><span class="lineno"> 2063</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l02064"></a><span class="lineno"> 2064</span>&#160;        }</div><div class="line"><a name="l02065"></a><span class="lineno"> 2065</span>&#160;        <span class="keywordflow">case</span> ResizeMethod::NearestNeighbor:</div><div class="line"><a name="l02066"></a><span class="lineno"> 2066</span>&#160;        {</div><div class="line"><a name="l02067"></a><span class="lineno"> 2067</span>&#160;            layerName += str(boost::format(<span class="stringliteral">&quot;NEARESTNEIGHBOR:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span>&#160;        }</div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>&#160;        <span class="keywordflow">default</span>:</div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span>&#160;        {</div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span>&#160;                boost::str(boost::format(<span class="stringliteral">&quot;Unexpected ResizeMethod[%1%] when creating layerName &quot;</span></div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span>&#160;                                         <span class="stringliteral">&quot; %2% &quot;</span>) %static_cast&lt;int&gt;(resizeMethod)% <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span>&#160;        }</div><div class="line"><a name="l02076"></a><span class="lineno"> 2076</span>&#160;    }</div><div class="line"><a name="l02077"></a><span class="lineno"> 2077</span>&#160;</div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-&gt;AddResizeLayer(desc, layerName.c_str());</div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span>&#160;</div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>&#160;    layer-&gt;<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="l02082"></a><span class="lineno"> 2082</span>&#160;</div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02084"></a><span class="lineno"> 2084</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02085"></a><span class="lineno"> 2085</span>&#160;</div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span>&#160;}</div><div class="line"><a name="l02089"></a><span class="lineno"> 2089</span>&#160;</div><div class="line"><a name="l02090"></a><span class="lineno"> 2090</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseConcatenation(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span>&#160;{</div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span>&#160;</div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l02095"></a><span class="lineno"> 2095</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr-&gt;builtin_options.AsConcatenationOptions();</div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</span>&#160;</div><div class="line"><a name="l02097"></a><span class="lineno"> 2097</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02098"></a><span class="lineno"> 2098</span>&#160;</div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02100"></a><span class="lineno"> 2100</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02101"></a><span class="lineno"> 2101</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02102"></a><span class="lineno"> 2102</span>&#160;</div><div class="line"><a name="l02103"></a><span class="lineno"> 2103</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numConcatView = <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(inputs.size());</div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span>&#160;    uint32_t inputRank = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]).<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l02105"></a><span class="lineno"> 2105</span>&#160;</div><div class="line"><a name="l02106"></a><span class="lineno"> 2106</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatDimInput = <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(</div><div class="line"><a name="l02107"></a><span class="lineno"> 2107</span>&#160;        (<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(inputRank) + <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;axis) % <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(inputRank));</div><div class="line"><a name="l02108"></a><span class="lineno"> 2108</span>&#160;</div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span>&#160;    <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> concatDescriptor(static_cast&lt;uint32_t&gt;(numConcatView), inputRank);</div><div class="line"><a name="l02110"></a><span class="lineno"> 2110</span>&#160;    concatDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a5b192c5fcd96a0f75542524cf646b355">SetConcatAxis</a>(concatDimInput);</div><div class="line"><a name="l02111"></a><span class="lineno"> 2111</span>&#160;</div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> mergeDimOrigin = 0;</div><div class="line"><a name="l02113"></a><span class="lineno"> 2113</span>&#160;</div><div class="line"><a name="l02114"></a><span class="lineno"> 2114</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> viewIndex = 0; viewIndex &lt; numConcatView; ++viewIndex)</div><div class="line"><a name="l02115"></a><span class="lineno"> 2115</span>&#160;    {</div><div class="line"><a name="l02116"></a><span class="lineno"> 2116</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[viewIndex]);</div><div class="line"><a name="l02117"></a><span class="lineno"> 2117</span>&#160;</div><div class="line"><a name="l02118"></a><span class="lineno"> 2118</span>&#160;        <span class="comment">// This set up concatDescriptor view origin</span></div><div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>&#160;        <a class="code" href="namespacearmnn_utils.xhtml#a523deabeb7d0a884028b35eebfd1cb6c">armnnUtils::ProcessConcatInputTensorInfo</a>(</div><div class="line"><a name="l02120"></a><span class="lineno"> 2120</span>&#160;            inputTensorInfo, concatDescriptor, concatDimInput, viewIndex, mergeDimOrigin);</div><div class="line"><a name="l02121"></a><span class="lineno"> 2121</span>&#160;    }</div><div class="line"><a name="l02122"></a><span class="lineno"> 2122</span>&#160;</div><div class="line"><a name="l02123"></a><span class="lineno"> 2123</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;Concatenation:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02124"></a><span class="lineno"> 2124</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-&gt;AddConcatLayer(concatDescriptor, layerName.c_str());</div><div class="line"><a name="l02125"></a><span class="lineno"> 2125</span>&#160;</div><div class="line"><a name="l02126"></a><span class="lineno"> 2126</span>&#160;    BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02127"></a><span class="lineno"> 2127</span>&#160;</div><div class="line"><a name="l02128"></a><span class="lineno"> 2128</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l02129"></a><span class="lineno"> 2129</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02130"></a><span class="lineno"> 2130</span>&#160;</div><div class="line"><a name="l02131"></a><span class="lineno"> 2131</span>&#160;    layer-&gt;<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="l02132"></a><span class="lineno"> 2132</span>&#160;</div><div class="line"><a name="l02133"></a><span class="lineno"> 2133</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes});</div><div class="line"><a name="l02134"></a><span class="lineno"> 2134</span>&#160;</div><div class="line"><a name="l02135"></a><span class="lineno"> 2135</span>&#160;    <span class="comment">// add fused activation layer</span></div><div class="line"><a name="l02136"></a><span class="lineno"> 2136</span>&#160;    layer = AddFusedActivationLayer(layer, 0, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;fused_activation_function);</div><div class="line"><a name="l02137"></a><span class="lineno"> 2137</span>&#160;</div><div class="line"><a name="l02138"></a><span class="lineno"> 2138</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02139"></a><span class="lineno"> 2139</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02140"></a><span class="lineno"> 2140</span>&#160;}</div><div class="line"><a name="l02141"></a><span class="lineno"> 2141</span>&#160;</div><div class="line"><a name="l02142"></a><span class="lineno"> 2142</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseFullyConnected(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02143"></a><span class="lineno"> 2143</span>&#160;{</div><div class="line"><a name="l02144"></a><span class="lineno"> 2144</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02145"></a><span class="lineno"> 2145</span>&#160;</div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorRfr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorRfr-&gt;builtin_options.AsFullyConnectedOptions();</div><div class="line"><a name="l02148"></a><span class="lineno"> 2148</span>&#160;</div><div class="line"><a name="l02149"></a><span class="lineno"> 2149</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02150"></a><span class="lineno"> 2150</span>&#160;</div><div class="line"><a name="l02151"></a><span class="lineno"> 2151</span>&#160;    <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> desc;</div><div class="line"><a name="l02152"></a><span class="lineno"> 2152</span>&#160;    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="l02153"></a><span class="lineno"> 2153</span>&#160;    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="l02154"></a><span class="lineno"> 2154</span>&#160;</div><div class="line"><a name="l02155"></a><span class="lineno"> 2155</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span>&#160;</div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> filterTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span>&#160;</div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span>&#160;    <span class="comment">// Fully Connected Layer accepts two dimensional weights input</span></div><div class="line"><a name="l02162"></a><span class="lineno"> 2162</span>&#160;    int32_t weightsDimension = <span class="keyword">static_cast&lt;</span>int32_t<span class="keyword">&gt;</span>(filterTensorInfo.GetNumDimensions());</div><div class="line"><a name="l02163"></a><span class="lineno"> 2163</span>&#160;    <span class="keywordflow">if</span> (weightsDimension != 2)</div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span>&#160;    {</div><div class="line"><a name="l02165"></a><span class="lineno"> 2165</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02166"></a><span class="lineno"> 2166</span>&#160;            boost::str(</div><div class="line"><a name="l02167"></a><span class="lineno"> 2167</span>&#160;                boost::format(</div><div class="line"><a name="l02168"></a><span class="lineno"> 2168</span>&#160;                    <span class="stringliteral">&quot;Dimension %1% for Fully Connected weights is not supported by Armnn. &quot;</span></div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span>&#160;                    <span class="stringliteral">&quot;Node %2%&quot;</span>)</div><div class="line"><a name="l02170"></a><span class="lineno"> 2170</span>&#160;                % weightsDimension</div><div class="line"><a name="l02171"></a><span class="lineno"> 2171</span>&#160;                % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02172"></a><span class="lineno"> 2172</span>&#160;    }</div><div class="line"><a name="l02173"></a><span class="lineno"> 2173</span>&#160;</div><div class="line"><a name="l02174"></a><span class="lineno"> 2174</span>&#160;    <span class="keyword">auto</span> filterTensorAndData = CreateConstTensor(inputs[1],</div><div class="line"><a name="l02175"></a><span class="lineno"> 2175</span>&#160;                                                 filterTensorInfo,</div><div class="line"><a name="l02176"></a><span class="lineno"> 2176</span>&#160;                                                 <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::PermutationVector&amp;&gt;</a>());</div><div class="line"><a name="l02177"></a><span class="lineno"> 2177</span>&#160;    <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="l02178"></a><span class="lineno"> 2178</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;FullyConnected:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02179"></a><span class="lineno"> 2179</span>&#160;</div><div class="line"><a name="l02180"></a><span class="lineno"> 2180</span>&#160;    <span class="keywordflow">if</span> (inputs.size() == 3)</div><div class="line"><a name="l02181"></a><span class="lineno"> 2181</span>&#160;    {</div><div class="line"><a name="l02182"></a><span class="lineno"> 2182</span>&#160;        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="l02183"></a><span class="lineno"> 2183</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[2]);</div><div class="line"><a name="l02184"></a><span class="lineno"> 2184</span>&#160;        <span class="keyword">auto</span> biasTensorAndData = CreateConstTensor(inputs[2],</div><div class="line"><a name="l02185"></a><span class="lineno"> 2185</span>&#160;                                                   biasTensorInfo,</div><div class="line"><a name="l02186"></a><span class="lineno"> 2186</span>&#160;                                                   <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::PermutationVector&amp;&gt;</a>());</div><div class="line"><a name="l02187"></a><span class="lineno"> 2187</span>&#160;        layer = m_Network-&gt;AddFullyConnectedLayer(desc,</div><div class="line"><a name="l02188"></a><span class="lineno"> 2188</span>&#160;                                                  filterTensorAndData.first,</div><div class="line"><a name="l02189"></a><span class="lineno"> 2189</span>&#160;                                                  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ConstTensor&gt;</a>(biasTensorAndData.first),</div><div class="line"><a name="l02190"></a><span class="lineno"> 2190</span>&#160;                                                  layerName.c_str());</div><div class="line"><a name="l02191"></a><span class="lineno"> 2191</span>&#160;    }</div><div class="line"><a name="l02192"></a><span class="lineno"> 2192</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l02193"></a><span class="lineno"> 2193</span>&#160;    {</div><div class="line"><a name="l02194"></a><span class="lineno"> 2194</span>&#160;        layer = m_Network-&gt;AddFullyConnectedLayer(desc,</div><div class="line"><a name="l02195"></a><span class="lineno"> 2195</span>&#160;                                                  filterTensorAndData.first,</div><div class="line"><a name="l02196"></a><span class="lineno"> 2196</span>&#160;                                                  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>(),</div><div class="line"><a name="l02197"></a><span class="lineno"> 2197</span>&#160;                                                  layerName.c_str());</div><div class="line"><a name="l02198"></a><span class="lineno"> 2198</span>&#160;    }</div><div class="line"><a name="l02199"></a><span class="lineno"> 2199</span>&#160;    BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02200"></a><span class="lineno"> 2200</span>&#160;</div><div class="line"><a name="l02201"></a><span class="lineno"> 2201</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo  = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l02202"></a><span class="lineno"> 2202</span>&#160;</div><div class="line"><a name="l02203"></a><span class="lineno"> 2203</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02204"></a><span class="lineno"> 2204</span>&#160;</div><div class="line"><a name="l02205"></a><span class="lineno"> 2205</span>&#160;    <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() &gt; 2)</div><div class="line"><a name="l02206"></a><span class="lineno"> 2206</span>&#160;    {</div><div class="line"><a name="l02207"></a><span class="lineno"> 2207</span>&#160;        <span class="comment">// Add reshape to flatten to 2D [batch_size, input_size],</span></div><div class="line"><a name="l02208"></a><span class="lineno"> 2208</span>&#160;        <span class="comment">// where &quot;input_size&quot; corresponds to the number of inputs to the layer,</span></div><div class="line"><a name="l02209"></a><span class="lineno"> 2209</span>&#160;        <span class="comment">// matching the second dimension of weights,</span></div><div class="line"><a name="l02210"></a><span class="lineno"> 2210</span>&#160;        <span class="comment">// and &quot;batch_size&quot; is calculated by dividing the number of elements by &quot;input_size&quot;.</span></div><div class="line"><a name="l02211"></a><span class="lineno"> 2211</span>&#160;        std::vector&lt;unsigned int&gt; reshapedDimensions(2);</div><div class="line"><a name="l02212"></a><span class="lineno"> 2212</span>&#160;        reshapedDimensions[1] = filterTensorInfo.GetShape()[1];</div><div class="line"><a name="l02213"></a><span class="lineno"> 2213</span>&#160;        reshapedDimensions[0] = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / reshapedDimensions[1];</div><div class="line"><a name="l02214"></a><span class="lineno"> 2214</span>&#160;</div><div class="line"><a name="l02215"></a><span class="lineno"> 2215</span>&#160;        <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="l02216"></a><span class="lineno"> 2216</span>&#160;        {</div><div class="line"><a name="l02217"></a><span class="lineno"> 2217</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02218"></a><span class="lineno"> 2218</span>&#160;                    boost::str(</div><div class="line"><a name="l02219"></a><span class="lineno"> 2219</span>&#160;                            boost::format(</div><div class="line"><a name="l02220"></a><span class="lineno"> 2220</span>&#160;                                    <span class="stringliteral">&quot;Failed to deduce input tensor shape from filter size %1%&quot;</span>)</div><div class="line"><a name="l02221"></a><span class="lineno"> 2221</span>&#160;                            % reshapedDimensions[1]</div><div class="line"><a name="l02222"></a><span class="lineno"> 2222</span>&#160;                            % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02223"></a><span class="lineno"> 2223</span>&#160;        }</div><div class="line"><a name="l02224"></a><span class="lineno"> 2224</span>&#160;</div><div class="line"><a name="l02225"></a><span class="lineno"> 2225</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> reshapedTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l02226"></a><span class="lineno"> 2226</span>&#160;        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="l02227"></a><span class="lineno"> 2227</span>&#160;</div><div class="line"><a name="l02228"></a><span class="lineno"> 2228</span>&#160;        std::string reshapeLayerName = boost::str(boost::format(<span class="stringliteral">&quot;Reshape_for:%1%&quot;</span>) % layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>());</div><div class="line"><a name="l02229"></a><span class="lineno"> 2229</span>&#160;        <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">armnn::ReshapeDescriptor</a> desc;</div><div class="line"><a name="l02230"></a><span class="lineno"> 2230</span>&#160;        desc.m_TargetShape = reshapedTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l02231"></a><span class="lineno"> 2231</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* reshapeLayer = m_Network-&gt;AddReshapeLayer(desc, layerName.c_str());</div><div class="line"><a name="l02232"></a><span class="lineno"> 2232</span>&#160;</div><div class="line"><a name="l02233"></a><span class="lineno"> 2233</span>&#160;        reshapeLayer-&gt;<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="l02234"></a><span class="lineno"> 2234</span>&#160;        reshapeLayer-&gt;<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-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02235"></a><span class="lineno"> 2235</span>&#160;</div><div class="line"><a name="l02236"></a><span class="lineno"> 2236</span>&#160;        RegisterInputSlots(subgraphIndex, operatorIndex, reshapeLayer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02237"></a><span class="lineno"> 2237</span>&#160;    }</div><div class="line"><a name="l02238"></a><span class="lineno"> 2238</span>&#160;    <span class="keywordflow">else</span></div><div class="line"><a name="l02239"></a><span class="lineno"> 2239</span>&#160;    {</div><div class="line"><a name="l02240"></a><span class="lineno"> 2240</span>&#160;        <span class="comment">// register the input connection slot for the layer</span></div><div class="line"><a name="l02241"></a><span class="lineno"> 2241</span>&#160;        <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l02242"></a><span class="lineno"> 2242</span>&#160;        RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02243"></a><span class="lineno"> 2243</span>&#160;    }</div><div class="line"><a name="l02244"></a><span class="lineno"> 2244</span>&#160;</div><div class="line"><a name="l02245"></a><span class="lineno"> 2245</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l02246"></a><span class="lineno"> 2246</span>&#160;    layer-&gt;<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="l02247"></a><span class="lineno"> 2247</span>&#160;</div><div class="line"><a name="l02248"></a><span class="lineno"> 2248</span>&#160;    <span class="comment">// we need to add the activation layer and fortunately we don&#39;t need to care about the data layout</span></div><div class="line"><a name="l02249"></a><span class="lineno"> 2249</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* fusedActivationLayer = AddFusedActivationLayer(layer, 0,</div><div class="line"><a name="l02250"></a><span class="lineno"> 2250</span>&#160;                                                                             <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;fused_activation_function);</div><div class="line"><a name="l02251"></a><span class="lineno"> 2251</span>&#160;</div><div class="line"><a name="l02252"></a><span class="lineno"> 2252</span>&#160;    <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="l02253"></a><span class="lineno"> 2253</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02254"></a><span class="lineno"> 2254</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, fusedActivationLayer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02255"></a><span class="lineno"> 2255</span>&#160;}</div><div class="line"><a name="l02256"></a><span class="lineno"> 2256</span>&#160;</div><div class="line"><a name="l02257"></a><span class="lineno"> 2257</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseDetectionPostProcess(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02258"></a><span class="lineno"> 2258</span>&#160;{</div><div class="line"><a name="l02259"></a><span class="lineno"> 2259</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02260"></a><span class="lineno"> 2260</span>&#160;</div><div class="line"><a name="l02261"></a><span class="lineno"> 2261</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l02262"></a><span class="lineno"> 2262</span>&#160;</div><div class="line"><a name="l02263"></a><span class="lineno"> 2263</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02264"></a><span class="lineno"> 2264</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02265"></a><span class="lineno"> 2265</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 4);</div><div class="line"><a name="l02266"></a><span class="lineno"> 2266</span>&#160;</div><div class="line"><a name="l02267"></a><span class="lineno"> 2267</span>&#160;    <span class="comment">// Obtain custom options from flexbuffers</span></div><div class="line"><a name="l02268"></a><span class="lineno"> 2268</span>&#160;    <span class="keyword">auto</span> custom_options = operatorPtr-&gt;custom_options;</div><div class="line"><a name="l02269"></a><span class="lineno"> 2269</span>&#160;    <span class="keyword">const</span> flexbuffers::Map&amp; m = flexbuffers::GetRoot(custom_options.data(), custom_options.size()).AsMap();</div><div class="line"><a name="l02270"></a><span class="lineno"> 2270</span>&#160;</div><div class="line"><a name="l02271"></a><span class="lineno"> 2271</span>&#160;    <span class="comment">// Obtain descriptor information from tf lite</span></div><div class="line"><a name="l02272"></a><span class="lineno"> 2272</span>&#160;    <a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml">DetectionPostProcessDescriptor</a> desc;</div><div class="line"><a name="l02273"></a><span class="lineno"> 2273</span>&#160;    desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae72089bcab60ac175557f4241b16a014">m_MaxDetections</a>           = m[<span class="stringliteral">&quot;max_detections&quot;</span>].AsUInt32();</div><div class="line"><a name="l02274"></a><span class="lineno"> 2274</span>&#160;    desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a9ae2c9796692ebeafe19a4d3f09c8ea8">m_MaxClassesPerDetection</a>  = m[<span class="stringliteral">&quot;max_classes_per_detection&quot;</span>].AsUInt32();</div><div class="line"><a name="l02275"></a><span class="lineno"> 2275</span>&#160;    desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a4392dd6b4862cc9cf95ae8f1001ba592">m_NmsScoreThreshold</a>       = m[<span class="stringliteral">&quot;nms_score_threshold&quot;</span>].AsFloat();</div><div class="line"><a name="l02276"></a><span class="lineno"> 2276</span>&#160;    desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a53c8a7f33a40e1e240256bcfcf41b101">m_NmsIouThreshold</a>         = m[<span class="stringliteral">&quot;nms_iou_threshold&quot;</span>].AsFloat();</div><div class="line"><a name="l02277"></a><span class="lineno"> 2277</span>&#160;    desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a3a04b0ccee4bb2f21721ee5045e83df4">m_NumClasses</a>              = m[<span class="stringliteral">&quot;num_classes&quot;</span>].AsUInt32();</div><div class="line"><a name="l02278"></a><span class="lineno"> 2278</span>&#160;    desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#aa61510cbd529870182e918ac6e8b9d72">m_ScaleH</a>                  = m[<span class="stringliteral">&quot;h_scale&quot;</span>].AsFloat();</div><div class="line"><a name="l02279"></a><span class="lineno"> 2279</span>&#160;    desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ab509802c659de19929f18bad14a35c58">m_ScaleW</a>                  = m[<span class="stringliteral">&quot;w_scale&quot;</span>].AsFloat();</div><div class="line"><a name="l02280"></a><span class="lineno"> 2280</span>&#160;    desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae64523937ea910030ad66fee6fddd51f">m_ScaleX</a>                  = m[<span class="stringliteral">&quot;x_scale&quot;</span>].AsFloat();</div><div class="line"><a name="l02281"></a><span class="lineno"> 2281</span>&#160;    desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7a2156ec7d9c012ce00bbcc6afcb9028">m_ScaleY</a>                  = m[<span class="stringliteral">&quot;y_scale&quot;</span>].AsFloat();</div><div class="line"><a name="l02282"></a><span class="lineno"> 2282</span>&#160;</div><div class="line"><a name="l02283"></a><span class="lineno"> 2283</span>&#160;    <span class="keywordflow">if</span> (!(m[<span class="stringliteral">&quot;use_regular_nms&quot;</span>].IsNull()))</div><div class="line"><a name="l02284"></a><span class="lineno"> 2284</span>&#160;    {</div><div class="line"><a name="l02285"></a><span class="lineno"> 2285</span>&#160;        desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7ed9bc7c26df67d274d5dd4cd83adf0f">m_UseRegularNms</a>       = m[<span class="stringliteral">&quot;use_regular_nms&quot;</span>].AsBool();</div><div class="line"><a name="l02286"></a><span class="lineno"> 2286</span>&#160;    }</div><div class="line"><a name="l02287"></a><span class="lineno"> 2287</span>&#160;    <span class="keywordflow">if</span> (!(m[<span class="stringliteral">&quot;detections_per_class&quot;</span>].IsNull()))</div><div class="line"><a name="l02288"></a><span class="lineno"> 2288</span>&#160;    {</div><div class="line"><a name="l02289"></a><span class="lineno"> 2289</span>&#160;        desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7e2f87544b8bc7e497e1dec8d3ca4055">m_DetectionsPerClass</a>  = m[<span class="stringliteral">&quot;detections_per_class&quot;</span>].AsUInt32();</div><div class="line"><a name="l02290"></a><span class="lineno"> 2290</span>&#160;    }</div><div class="line"><a name="l02291"></a><span class="lineno"> 2291</span>&#160;</div><div class="line"><a name="l02292"></a><span class="lineno"> 2292</span>&#160;    <span class="keywordflow">if</span> (desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a53c8a7f33a40e1e240256bcfcf41b101">m_NmsIouThreshold</a> &lt;= 0.0f || desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a53c8a7f33a40e1e240256bcfcf41b101">m_NmsIouThreshold</a> &gt; 1.0f)</div><div class="line"><a name="l02293"></a><span class="lineno"> 2293</span>&#160;    {</div><div class="line"><a name="l02294"></a><span class="lineno"> 2294</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">&quot;DetectionPostProcessTFLiteParser: Intersection over union threshold &quot;</span></div><div class="line"><a name="l02295"></a><span class="lineno"> 2295</span>&#160;                                       <span class="stringliteral">&quot;must be positive and less than or equal to 1.&quot;</span>);</div><div class="line"><a name="l02296"></a><span class="lineno"> 2296</span>&#160;    }</div><div class="line"><a name="l02297"></a><span class="lineno"> 2297</span>&#160;</div><div class="line"><a name="l02298"></a><span class="lineno"> 2298</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> anchorTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[2]);</div><div class="line"><a name="l02299"></a><span class="lineno"> 2299</span>&#160;    <span class="keyword">auto</span> anchorTensorAndData = CreateConstTensor(inputs[2], anchorTensorInfo,</div><div class="line"><a name="l02300"></a><span class="lineno"> 2300</span>&#160;                                                 <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::PermutationVector&amp;&gt;</a>());</div><div class="line"><a name="l02301"></a><span class="lineno"> 2301</span>&#160;</div><div class="line"><a name="l02302"></a><span class="lineno"> 2302</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;DetectionPostProcess:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02303"></a><span class="lineno"> 2303</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-&gt;AddDetectionPostProcessLayer(desc, anchorTensorAndData.first,</div><div class="line"><a name="l02304"></a><span class="lineno"> 2304</span>&#160;                                                                       layerName.c_str());</div><div class="line"><a name="l02305"></a><span class="lineno"> 2305</span>&#160;</div><div class="line"><a name="l02306"></a><span class="lineno"> 2306</span>&#160;    BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02307"></a><span class="lineno"> 2307</span>&#160;</div><div class="line"><a name="l02308"></a><span class="lineno"> 2308</span>&#160;    <span class="comment">// The model does not specify the output shapes.</span></div><div class="line"><a name="l02309"></a><span class="lineno"> 2309</span>&#160;    <span class="comment">// The output shapes are calculated from the max_detection and max_classes_per_detection.</span></div><div class="line"><a name="l02310"></a><span class="lineno"> 2310</span>&#160;    <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="l02311"></a><span class="lineno"> 2311</span>&#160;    m_OverridenOutputShapes.push_back({ 1, numDetectedBox, 4 });</div><div class="line"><a name="l02312"></a><span class="lineno"> 2312</span>&#160;    m_OverridenOutputShapes.push_back({ 1, numDetectedBox });</div><div class="line"><a name="l02313"></a><span class="lineno"> 2313</span>&#160;    m_OverridenOutputShapes.push_back({ 1, numDetectedBox });</div><div class="line"><a name="l02314"></a><span class="lineno"> 2314</span>&#160;    m_OverridenOutputShapes.push_back({ 1 });</div><div class="line"><a name="l02315"></a><span class="lineno"> 2315</span>&#160;</div><div class="line"><a name="l02316"></a><span class="lineno"> 2316</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0 ; i &lt; outputs.size() ; ++i)</div><div class="line"><a name="l02317"></a><span class="lineno"> 2317</span>&#160;    {</div><div class="line"><a name="l02318"></a><span class="lineno"> 2318</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> detectionBoxOutputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[i], m_OverridenOutputShapes[i]);</div><div class="line"><a name="l02319"></a><span class="lineno"> 2319</span>&#160;        layer-&gt;<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="l02320"></a><span class="lineno"> 2320</span>&#160;    }</div><div class="line"><a name="l02321"></a><span class="lineno"> 2321</span>&#160;</div><div class="line"><a name="l02322"></a><span class="lineno"> 2322</span>&#160;    <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="l02323"></a><span class="lineno"> 2323</span>&#160;    <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l02324"></a><span class="lineno"> 2324</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02325"></a><span class="lineno"> 2325</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l02326"></a><span class="lineno"> 2326</span>&#160;</div><div class="line"><a name="l02327"></a><span class="lineno"> 2327</span>&#160;    <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="l02328"></a><span class="lineno"> 2328</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02329"></a><span class="lineno"> 2329</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0],</div><div class="line"><a name="l02330"></a><span class="lineno"> 2330</span>&#160;                                                              outputTensorIndexes[1],</div><div class="line"><a name="l02331"></a><span class="lineno"> 2331</span>&#160;                                                              outputTensorIndexes[2],</div><div class="line"><a name="l02332"></a><span class="lineno"> 2332</span>&#160;                                                              outputTensorIndexes[3]});</div><div class="line"><a name="l02333"></a><span class="lineno"> 2333</span>&#160;}</div><div class="line"><a name="l02334"></a><span class="lineno"> 2334</span>&#160;<span class="comment"></span></div><div class="line"><a name="l02335"></a><span class="lineno"> 2335</span>&#160;<span class="comment">/// The TfLite Pack operator is equivalent to the ArmNN Stack operator</span></div><div class="line"><a name="l02336"></a><span class="lineno"> 2336</span>&#160;<span class="comment"></span><span class="keywordtype">void</span> TfLiteParser::ParsePack(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02337"></a><span class="lineno"> 2337</span>&#160;{</div><div class="line"><a name="l02338"></a><span class="lineno"> 2338</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02339"></a><span class="lineno"> 2339</span>&#160;</div><div class="line"><a name="l02340"></a><span class="lineno"> 2340</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02341"></a><span class="lineno"> 2341</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02342"></a><span class="lineno"> 2342</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02343"></a><span class="lineno"> 2343</span>&#160;</div><div class="line"><a name="l02344"></a><span class="lineno"> 2344</span>&#160;    <span class="keywordflow">if</span> (inputs.size() &lt; 1)</div><div class="line"><a name="l02345"></a><span class="lineno"> 2345</span>&#160;    {</div><div class="line"><a name="l02346"></a><span class="lineno"> 2346</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">&quot;Pack must have at least one input.&quot;</span>);</div><div class="line"><a name="l02347"></a><span class="lineno"> 2347</span>&#160;    }</div><div class="line"><a name="l02348"></a><span class="lineno"> 2348</span>&#160;</div><div class="line"><a name="l02349"></a><span class="lineno"> 2349</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span>&amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l02350"></a><span class="lineno"> 2350</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span>* <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr-&gt;builtin_options.AsPackOptions();</div><div class="line"><a name="l02351"></a><span class="lineno"> 2351</span>&#160;</div><div class="line"><a name="l02352"></a><span class="lineno"> 2352</span>&#160;    <a class="code" href="structarmnn_1_1_stack_descriptor.xhtml">StackDescriptor</a> desc;</div><div class="line"><a name="l02353"></a><span class="lineno"> 2353</span>&#160;    desc.<a class="code" href="structarmnn_1_1_stack_descriptor.xhtml#ab218de7805899c8412d75d1fd1d846d2">m_Axis</a> = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;axis);</div><div class="line"><a name="l02354"></a><span class="lineno"> 2354</span>&#160;    desc.<a class="code" href="structarmnn_1_1_stack_descriptor.xhtml#aed6086070440ceb94129bef06f70173f">m_NumInputs</a> = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(inputs.size());</div><div class="line"><a name="l02355"></a><span class="lineno"> 2355</span>&#160;</div><div class="line"><a name="l02356"></a><span class="lineno"> 2356</span>&#160;    <span class="comment">// Use the tensor shape of the first input as the &quot;correct&quot; input shape in the descriptor</span></div><div class="line"><a name="l02357"></a><span class="lineno"> 2357</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l02358"></a><span class="lineno"> 2358</span>&#160;    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="l02359"></a><span class="lineno"> 2359</span>&#160;</div><div class="line"><a name="l02360"></a><span class="lineno"> 2360</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;Pack:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02361"></a><span class="lineno"> 2361</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-&gt;AddStackLayer(desc, layerName.c_str());</div><div class="line"><a name="l02362"></a><span class="lineno"> 2362</span>&#160;</div><div class="line"><a name="l02363"></a><span class="lineno"> 2363</span>&#160;    BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02364"></a><span class="lineno"> 2364</span>&#160;</div><div class="line"><a name="l02365"></a><span class="lineno"> 2365</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l02366"></a><span class="lineno"> 2366</span>&#160;    layer-&gt;<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="l02367"></a><span class="lineno"> 2367</span>&#160;</div><div class="line"><a name="l02368"></a><span class="lineno"> 2368</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02369"></a><span class="lineno"> 2369</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes});</div><div class="line"><a name="l02370"></a><span class="lineno"> 2370</span>&#160;</div><div class="line"><a name="l02371"></a><span class="lineno"> 2371</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02372"></a><span class="lineno"> 2372</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02373"></a><span class="lineno"> 2373</span>&#160;}</div><div class="line"><a name="l02374"></a><span class="lineno"> 2374</span>&#160;</div><div class="line"><a name="l02375"></a><span class="lineno"> 2375</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseUnpack(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02376"></a><span class="lineno"> 2376</span>&#160;{</div><div class="line"><a name="l02377"></a><span class="lineno"> 2377</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02378"></a><span class="lineno"> 2378</span>&#160;</div><div class="line"><a name="l02379"></a><span class="lineno"> 2379</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l02380"></a><span class="lineno"> 2380</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr-&gt;builtin_options.AsUnpackOptions();</div><div class="line"><a name="l02381"></a><span class="lineno"> 2381</span>&#160;</div><div class="line"><a name="l02382"></a><span class="lineno"> 2382</span>&#160;    <span class="comment">// This unpackAxis indicates the axis to unpack</span></div><div class="line"><a name="l02383"></a><span class="lineno"> 2383</span>&#160;    <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>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;axis);</div><div class="line"><a name="l02384"></a><span class="lineno"> 2384</span>&#160;</div><div class="line"><a name="l02385"></a><span class="lineno"> 2385</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02386"></a><span class="lineno"> 2386</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l02387"></a><span class="lineno"> 2387</span>&#160;</div><div class="line"><a name="l02388"></a><span class="lineno"> 2388</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo  = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l02389"></a><span class="lineno"> 2389</span>&#160;</div><div class="line"><a name="l02390"></a><span class="lineno"> 2390</span>&#160;    <span class="keywordflow">if</span> (unpackAxis &gt;= inputTensorInfo.GetNumDimensions())</div><div class="line"><a name="l02391"></a><span class="lineno"> 2391</span>&#160;    {</div><div class="line"><a name="l02392"></a><span class="lineno"> 2392</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02393"></a><span class="lineno"> 2393</span>&#160;                boost::str(</div><div class="line"><a name="l02394"></a><span class="lineno"> 2394</span>&#160;                        boost::format(</div><div class="line"><a name="l02395"></a><span class="lineno"> 2395</span>&#160;                                <span class="stringliteral">&quot;The unpack axis: %1% cannot be greater than or equal to &quot;</span></div><div class="line"><a name="l02396"></a><span class="lineno"> 2396</span>&#160;                                <span class="stringliteral">&quot;the number of input dimension %2% %3%&quot;</span>)</div><div class="line"><a name="l02397"></a><span class="lineno"> 2397</span>&#160;                        % unpackAxis</div><div class="line"><a name="l02398"></a><span class="lineno"> 2398</span>&#160;                        % inputTensorInfo.GetNumDimensions()</div><div class="line"><a name="l02399"></a><span class="lineno"> 2399</span>&#160;                        % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02400"></a><span class="lineno"> 2400</span>&#160;    }</div><div class="line"><a name="l02401"></a><span class="lineno"> 2401</span>&#160;</div><div class="line"><a name="l02402"></a><span class="lineno"> 2402</span>&#160; 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   <span class="comment">// If unpack number cannot be inferred and is still zero, throw ParseException.</span></div><div class="line"><a name="l02410"></a><span class="lineno"> 2410</span>&#160;    <span class="keywordflow">if</span>(unpackNum == 0)</div><div class="line"><a name="l02411"></a><span class="lineno"> 2411</span>&#160;    {</div><div class="line"><a name="l02412"></a><span class="lineno"> 2412</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">&quot;Number to unpack must greater than zero.&quot;</span>);</div><div class="line"><a name="l02413"></a><span class="lineno"> 2413</span>&#160;    }</div><div class="line"><a name="l02414"></a><span class="lineno"> 2414</span>&#160;</div><div class="line"><a name="l02415"></a><span class="lineno"> 2415</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02416"></a><span class="lineno"> 2416</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), unpackNum);</div><div class="line"><a name="l02417"></a><span class="lineno"> 2417</span>&#160;</div><div class="line"><a name="l02418"></a><span class="lineno"> 2418</span>&#160;    <span class="keyword">auto</span> inputDimSize = inputTensorInfo.GetNumDimensions();</div><div class="line"><a name="l02419"></a><span class="lineno"> 2419</span>&#160;    std::vector&lt;unsigned int&gt; unpackDimSizes(inputDimSize);</div><div class="line"><a name="l02420"></a><span class="lineno"> 2420</span>&#160;</div><div class="line"><a name="l02421"></a><span class="lineno"> 2421</span>&#160;    <span class="comment">// Add current input shape to unpackDimSizes</span></div><div class="line"><a name="l02422"></a><span class="lineno"> 2422</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; inputDimSize; ++i)</div><div class="line"><a name="l02423"></a><span class="lineno"> 2423</span>&#160;    {</div><div class="line"><a name="l02424"></a><span class="lineno"> 2424</span>&#160;        unpackDimSizes[i] = inputTensorInfo.GetShape()[i];</div><div class="line"><a name="l02425"></a><span class="lineno"> 2425</span>&#160;    }</div><div class="line"><a name="l02426"></a><span class="lineno"> 2426</span>&#160;</div><div class="line"><a name="l02427"></a><span class="lineno"> 2427</span>&#160;    <span class="keywordflow">if</span> (unpackDimSizes[unpackAxis] != unpackNum)</div><div class="line"><a name="l02428"></a><span class="lineno"> 2428</span>&#160;    {</div><div class="line"><a name="l02429"></a><span class="lineno"> 2429</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">&quot;Number to unpack must be the same as length of the dimension to &quot;</span></div><div class="line"><a name="l02430"></a><span class="lineno"> 2430</span>&#160;                             <span class="stringliteral">&quot;unpack along.&quot;</span>);</div><div class="line"><a name="l02431"></a><span class="lineno"> 2431</span>&#160;    }</div><div class="line"><a name="l02432"></a><span class="lineno"> 2432</span>&#160;</div><div class="line"><a name="l02433"></a><span class="lineno"> 2433</span>&#160;    unpackDimSizes[unpackAxis] /= unpackNum;</div><div class="line"><a name="l02434"></a><span class="lineno"> 2434</span>&#160;</div><div class="line"><a name="l02435"></a><span class="lineno"> 2435</span>&#160;    <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">SplitterDescriptor</a> splitDesc(unpackNum, static_cast&lt;unsigned int&gt;(unpackDimSizes.size()));</div><div class="line"><a name="l02436"></a><span class="lineno"> 2436</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> j = 0; j &lt; unpackNum; ++j)</div><div class="line"><a name="l02437"></a><span class="lineno"> 2437</span>&#160;    {</div><div class="line"><a name="l02438"></a><span class="lineno"> 2438</span>&#160;        <span class="comment">// Set the size of the views.</span></div><div class="line"><a name="l02439"></a><span class="lineno"> 2439</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx = 0; dimIdx &lt; unpackDimSizes.size(); ++dimIdx)</div><div class="line"><a name="l02440"></a><span class="lineno"> 2440</span>&#160;        {</div><div class="line"><a name="l02441"></a><span class="lineno"> 2441</span>&#160;            splitDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(j, dimIdx, unpackDimSizes[dimIdx]);</div><div class="line"><a name="l02442"></a><span class="lineno"> 2442</span>&#160;        }</div><div class="line"><a name="l02443"></a><span class="lineno"> 2443</span>&#160;        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="l02444"></a><span class="lineno"> 2444</span>&#160;    }</div><div class="line"><a name="l02445"></a><span class="lineno"> 2445</span>&#160;</div><div class="line"><a name="l02446"></a><span class="lineno"> 2446</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;Unpack:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02447"></a><span class="lineno"> 2447</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-&gt;AddSplitterLayer(splitDesc, layerName.c_str());</div><div class="line"><a name="l02448"></a><span class="lineno"> 2448</span>&#160;</div><div class="line"><a name="l02449"></a><span class="lineno"> 2449</span>&#160;    <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&lt;unsigned int&gt;(unpackDimSizes.size()),</div><div class="line"><a name="l02450"></a><span class="lineno"> 2450</span>&#160;                                            unpackDimSizes.data());</div><div class="line"><a name="l02451"></a><span class="lineno"> 2451</span>&#160;</div><div class="line"><a name="l02452"></a><span class="lineno"> 2452</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02453"></a><span class="lineno"> 2453</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02454"></a><span class="lineno"> 2454</span>&#160;</div><div class="line"><a name="l02455"></a><span class="lineno"> 2455</span>&#160;    <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="l02456"></a><span class="lineno"> 2456</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> k = 0; k &lt; layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(); ++k)</div><div class="line"><a name="l02457"></a><span class="lineno"> 2457</span>&#160;    {</div><div class="line"><a name="l02458"></a><span class="lineno"> 2458</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo  = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[k]);</div><div class="line"><a name="l02459"></a><span class="lineno"> 2459</span>&#160;        std::string reshapeLayerName = boost::str(boost::format(<span class="stringliteral">&quot;Reshape_for:%1%&quot;</span>) % layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>());</div><div class="line"><a name="l02460"></a><span class="lineno"> 2460</span>&#160;        <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">armnn::ReshapeDescriptor</a> desc;</div><div class="line"><a name="l02461"></a><span class="lineno"> 2461</span>&#160;        desc.<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="l02462"></a><span class="lineno"> 2462</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* reshapeLayer = m_Network-&gt;AddReshapeLayer(desc, layerName.c_str());</div><div class="line"><a name="l02463"></a><span class="lineno"> 2463</span>&#160;</div><div class="line"><a name="l02464"></a><span class="lineno"> 2464</span>&#160;        layer-&gt;<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="l02465"></a><span class="lineno"> 2465</span>&#160;                                                                outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>(),</div><div class="line"><a name="l02466"></a><span class="lineno"> 2466</span>&#160;                                                                outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l02467"></a><span class="lineno"> 2467</span>&#160;                                                                outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>()));</div><div class="line"><a name="l02468"></a><span class="lineno"> 2468</span>&#160;        layer-&gt;<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-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02469"></a><span class="lineno"> 2469</span>&#160;</div><div class="line"><a name="l02470"></a><span class="lineno"> 2470</span>&#160;        reshapeLayer-&gt;<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="l02471"></a><span class="lineno"> 2471</span>&#160;</div><div class="line"><a name="l02472"></a><span class="lineno"> 2472</span>&#160;        uint32_t reshapedOutputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(operatorPtr-&gt;outputs[k]);</div><div class="line"><a name="l02473"></a><span class="lineno"> 2473</span>&#160;        <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>* slot = &amp;(reshapeLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l02474"></a><span class="lineno"> 2474</span>&#160;        RegisterProducerOfTensor(subgraphIndex, reshapedOutputId, slot);</div><div class="line"><a name="l02475"></a><span class="lineno"> 2475</span>&#160;    }</div><div class="line"><a name="l02476"></a><span class="lineno"> 2476</span>&#160;}</div><div class="line"><a name="l02477"></a><span class="lineno"> 2477</span>&#160;</div><div class="line"><a name="l02478"></a><span class="lineno"> 2478</span>&#160;<span class="keywordtype">void</span> TfLiteParser::ParseSplit(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02479"></a><span class="lineno"> 2479</span>&#160;{</div><div class="line"><a name="l02480"></a><span class="lineno"> 2480</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02481"></a><span class="lineno"> 2481</span>&#160;</div><div class="line"><a name="l02482"></a><span class="lineno"> 2482</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = m_Model-&gt;subgraphs[subgraphIndex]-&gt;operators[operatorIndex];</div><div class="line"><a name="l02483"></a><span class="lineno"> 2483</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr-&gt;builtin_options.AsSplitOptions();</div><div class="line"><a name="l02484"></a><span class="lineno"> 2484</span>&#160;</div><div class="line"><a name="l02485"></a><span class="lineno"> 2485</span>&#160;    <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>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>-&gt;num_splits);</div><div class="line"><a name="l02486"></a><span class="lineno"> 2486</span>&#160;</div><div class="line"><a name="l02487"></a><span class="lineno"> 2487</span>&#160; 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   <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l02495"></a><span class="lineno"> 2495</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02496"></a><span class="lineno"> 2496</span>&#160;    <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), numSplits);</div><div class="line"><a name="l02497"></a><span class="lineno"> 2497</span>&#160;</div><div class="line"><a name="l02498"></a><span class="lineno"> 2498</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo  = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l02499"></a><span class="lineno"> 2499</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> axisTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l02500"></a><span class="lineno"> 2500</span>&#160;</div><div class="line"><a name="l02501"></a><span class="lineno"> 2501</span>&#160;    <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> axisBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[0]-&gt;buffer);</div><div class="line"><a name="l02502"></a><span class="lineno"> 2502</span>&#160;    std::vector&lt;unsigned int&gt; axisData(axisTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l02503"></a><span class="lineno"> 2503</span>&#160;    ::memcpy(axisData.data(), axisBufferPtr-&gt;data.data(), axisTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l02504"></a><span class="lineno"> 2504</span>&#160;</div><div class="line"><a name="l02505"></a><span class="lineno"> 2505</span>&#160;    BOOST_ASSERT(axisTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() == 1);</div><div class="line"><a name="l02506"></a><span class="lineno"> 2506</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitDim = axisData[0];</div><div class="line"><a name="l02507"></a><span class="lineno"> 2507</span>&#160;</div><div class="line"><a name="l02508"></a><span class="lineno"> 2508</span>&#160;    <span class="keyword">auto</span> inputDimSize = inputTensorInfo.GetNumDimensions();</div><div class="line"><a name="l02509"></a><span class="lineno"> 2509</span>&#160; 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                   % inputTensorInfo.GetNumDimensions()</div><div class="line"><a name="l02517"></a><span class="lineno"> 2517</span>&#160;                    % <a class="code" href="namespacearmnn.xhtml#abdcd184ed3bd648bb31d385040cafd5d">MaxNumOfTensorDimensions</a></div><div class="line"><a name="l02518"></a><span class="lineno"> 2518</span>&#160;                    % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02519"></a><span class="lineno"> 2519</span>&#160;    }</div><div class="line"><a name="l02520"></a><span class="lineno"> 2520</span>&#160;</div><div class="line"><a name="l02521"></a><span class="lineno"> 2521</span>&#160;    std::vector&lt;unsigned int&gt; splitterDimSizes(inputDimSize);</div><div class="line"><a name="l02522"></a><span class="lineno"> 2522</span>&#160;</div><div class="line"><a name="l02523"></a><span class="lineno"> 2523</span>&#160;    <span class="comment">// Add current input shape to splitterDimSizes</span></div><div class="line"><a name="l02524"></a><span class="lineno"> 2524</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; inputDimSize; ++i)</div><div class="line"><a name="l02525"></a><span class="lineno"> 2525</span>&#160;    {</div><div class="line"><a name="l02526"></a><span class="lineno"> 2526</span>&#160;        splitterDimSizes[i] = inputTensorInfo.GetShape()[i];</div><div class="line"><a name="l02527"></a><span class="lineno"> 2527</span>&#160;    }</div><div class="line"><a name="l02528"></a><span class="lineno"> 2528</span>&#160;</div><div class="line"><a name="l02529"></a><span class="lineno"> 2529</span>&#160;    <span class="keywordflow">if</span> (splitterDimSizes[splitDim] % numSplits != 0)</div><div class="line"><a name="l02530"></a><span class="lineno"> 2530</span>&#160;    {</div><div class="line"><a name="l02531"></a><span class="lineno"> 2531</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">&quot;Number of splits must evenly divide the dimension&quot;</span>);</div><div class="line"><a name="l02532"></a><span class="lineno"> 2532</span>&#160;    }</div><div class="line"><a name="l02533"></a><span class="lineno"> 2533</span>&#160;    splitterDimSizes[splitDim] /= numSplits;</div><div class="line"><a name="l02534"></a><span class="lineno"> 2534</span>&#160;</div><div class="line"><a name="l02535"></a><span class="lineno"> 2535</span>&#160;    <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">SplitterDescriptor</a> splitDesc(numSplits, inputDimSize);</div><div class="line"><a name="l02536"></a><span class="lineno"> 2536</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> j = 0; j &lt; numSplits; ++j)</div><div class="line"><a name="l02537"></a><span class="lineno"> 2537</span>&#160;    {</div><div class="line"><a name="l02538"></a><span class="lineno"> 2538</span>&#160;        <span class="comment">// Set the size of the views.</span></div><div class="line"><a name="l02539"></a><span class="lineno"> 2539</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx = 0; dimIdx &lt; splitterDimSizes.size(); ++dimIdx)</div><div class="line"><a name="l02540"></a><span class="lineno"> 2540</span>&#160;        {</div><div class="line"><a name="l02541"></a><span class="lineno"> 2541</span>&#160;            splitDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(j, dimIdx, splitterDimSizes[dimIdx]);</div><div class="line"><a name="l02542"></a><span class="lineno"> 2542</span>&#160;        }</div><div class="line"><a name="l02543"></a><span class="lineno"> 2543</span>&#160;        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="l02544"></a><span class="lineno"> 2544</span>&#160;    }</div><div class="line"><a name="l02545"></a><span class="lineno"> 2545</span>&#160;</div><div class="line"><a name="l02546"></a><span class="lineno"> 2546</span>&#160;    <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">&quot;Split:%1%:%2%&quot;</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02547"></a><span class="lineno"> 2547</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-&gt;AddSplitterLayer(splitDesc, layerName.c_str());</div><div class="line"><a name="l02548"></a><span class="lineno"> 2548</span>&#160;</div><div class="line"><a name="l02549"></a><span class="lineno"> 2549</span>&#160;    <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02550"></a><span class="lineno"> 2550</span>&#160;    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[1]});</div><div class="line"><a name="l02551"></a><span class="lineno"> 2551</span>&#160;</div><div class="line"><a name="l02552"></a><span class="lineno"> 2552</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> k = 0; k &lt; layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(); ++k)</div><div class="line"><a name="l02553"></a><span class="lineno"> 2553</span>&#160;    {</div><div class="line"><a name="l02554"></a><span class="lineno"> 2554</span>&#160;        <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[k]);</div><div class="line"><a name="l02555"></a><span class="lineno"> 2555</span>&#160;        layer-&gt;<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="l02556"></a><span class="lineno"> 2556</span>&#160;    }</div><div class="line"><a name="l02557"></a><span class="lineno"> 2557</span>&#160;</div><div class="line"><a name="l02558"></a><span class="lineno"> 2558</span>&#160;    <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02559"></a><span class="lineno"> 2559</span>&#160;    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</div><div class="line"><a name="l02560"></a><span class="lineno"> 2560</span>&#160;}</div><div class="line"><a name="l02561"></a><span class="lineno"> 2561</span>&#160;</div><div class="line"><a name="l02562"></a><span class="lineno"> 2562</span>&#160;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* TfLiteParser::AddFusedActivationLayer(<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* prevLayer,</div><div class="line"><a name="l02563"></a><span class="lineno"> 2563</span>&#160;                                                                <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSlot,</div><div class="line"><a name="l02564"></a><span class="lineno"> 2564</span>&#160;                                                                tflite::ActivationFunctionType activationType)</div><div class="line"><a name="l02565"></a><span class="lineno"> 2565</span>&#160;{</div><div class="line"><a name="l02566"></a><span class="lineno"> 2566</span>&#160;    <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDesc;</div><div class="line"><a name="l02567"></a><span class="lineno"> 2567</span>&#160;    std::string layerName = prevLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>();</div><div class="line"><a name="l02568"></a><span class="lineno"> 2568</span>&#160;</div><div class="line"><a name="l02569"></a><span class="lineno"> 2569</span>&#160;    <span class="keywordflow">switch</span>(activationType)</div><div class="line"><a name="l02570"></a><span class="lineno"> 2570</span>&#160;    {</div><div class="line"><a name="l02571"></a><span class="lineno"> 2571</span>&#160;        <span class="keywordflow">case</span> tflite::ActivationFunctionType_NONE:</div><div class="line"><a name="l02572"></a><span class="lineno"> 2572</span>&#160;        {</div><div class="line"><a name="l02573"></a><span class="lineno"> 2573</span>&#160;            <span class="comment">// this is a no-op: return previous layer</span></div><div class="line"><a name="l02574"></a><span class="lineno"> 2574</span>&#160;            <span class="keywordflow">return</span> prevLayer;</div><div class="line"><a name="l02575"></a><span class="lineno"> 2575</span>&#160;        }</div><div class="line"><a name="l02576"></a><span class="lineno"> 2576</span>&#160;        <span class="keywordflow">case</span> tflite::ActivationFunctionType_RELU:</div><div class="line"><a name="l02577"></a><span class="lineno"> 2577</span>&#160;        {</div><div class="line"><a name="l02578"></a><span class="lineno"> 2578</span>&#160;            activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::ReLu;</div><div class="line"><a name="l02579"></a><span class="lineno"> 2579</span>&#160;            layerName += <span class="stringliteral">&quot;:RELU&quot;</span>;</div><div class="line"><a name="l02580"></a><span class="lineno"> 2580</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l02581"></a><span class="lineno"> 2581</span>&#160;        }</div><div class="line"><a name="l02582"></a><span class="lineno"> 2582</span>&#160;        <span class="keywordflow">case</span> tflite::ActivationFunctionType_RELU6:</div><div class="line"><a name="l02583"></a><span class="lineno"> 2583</span>&#160;        {</div><div class="line"><a name="l02584"></a><span class="lineno"> 2584</span>&#160;            activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::BoundedReLu;</div><div class="line"><a name="l02585"></a><span class="lineno"> 2585</span>&#160;            activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 6.0f;</div><div class="line"><a name="l02586"></a><span class="lineno"> 2586</span>&#160;            activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = 0.0f;</div><div class="line"><a name="l02587"></a><span class="lineno"> 2587</span>&#160;            layerName += <span class="stringliteral">&quot;:RELU6&quot;</span>;</div><div class="line"><a name="l02588"></a><span class="lineno"> 2588</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l02589"></a><span class="lineno"> 2589</span>&#160;        }</div><div class="line"><a name="l02590"></a><span class="lineno"> 2590</span>&#160;        <span class="keywordflow">case</span> tflite::ActivationFunctionType_TANH:</div><div class="line"><a name="l02591"></a><span class="lineno"> 2591</span>&#160;        {</div><div class="line"><a name="l02592"></a><span class="lineno"> 2592</span>&#160;            activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::TanH;</div><div class="line"><a name="l02593"></a><span class="lineno"> 2593</span>&#160;            activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l02594"></a><span class="lineno"> 2594</span>&#160;            activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = 1.0f;</div><div class="line"><a name="l02595"></a><span class="lineno"> 2595</span>&#160;            layerName += <span class="stringliteral">&quot;:TANH&quot;</span>;</div><div class="line"><a name="l02596"></a><span class="lineno"> 2596</span>&#160;            <span class="keywordflow">break</span>;</div><div class="line"><a name="l02597"></a><span class="lineno"> 2597</span>&#160;        }</div><div class="line"><a name="l02598"></a><span class="lineno"> 2598</span>&#160;</div><div class="line"><a name="l02599"></a><span class="lineno"> 2599</span>&#160;        <span class="comment">// I only put these here as a reminder what others we could support</span></div><div class="line"><a name="l02600"></a><span class="lineno"> 2600</span>&#160;        <span class="keywordflow">case</span> tflite::ActivationFunctionType_RELU_N1_TO_1:</div><div class="line"><a name="l02601"></a><span class="lineno"> 2601</span>&#160;        <span class="keywordflow">case</span> tflite::ActivationFunctionType_SIGN_BIT:</div><div class="line"><a name="l02602"></a><span class="lineno"> 2602</span>&#160;        <span class="keywordflow">default</span>:</div><div class="line"><a name="l02603"></a><span class="lineno"> 2603</span>&#160;        {</div><div class="line"><a name="l02604"></a><span class="lineno"> 2604</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02605"></a><span class="lineno"> 2605</span>&#160;                boost::str(</div><div class="line"><a name="l02606"></a><span class="lineno"> 2606</span>&#160;                    boost::format(<span class="stringliteral">&quot;TfLite parser doesn&#39;t suppport fused activation: &quot;</span></div><div class="line"><a name="l02607"></a><span class="lineno"> 2607</span>&#160;                                  <span class="stringliteral">&quot;%1%/%2% %3% &quot;</span>) %</div><div class="line"><a name="l02608"></a><span class="lineno"> 2608</span>&#160;                                  activationType %</div><div class="line"><a name="l02609"></a><span class="lineno"> 2609</span>&#160;                                  tflite::EnumNameActivationFunctionType(activationType) %</div><div class="line"><a name="l02610"></a><span class="lineno"> 2610</span>&#160;                                  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02611"></a><span class="lineno"> 2611</span>&#160;</div><div class="line"><a name="l02612"></a><span class="lineno"> 2612</span>&#160;        }</div><div class="line"><a name="l02613"></a><span class="lineno"> 2613</span>&#160;    }</div><div class="line"><a name="l02614"></a><span class="lineno"> 2614</span>&#160;</div><div class="line"><a name="l02615"></a><span class="lineno"> 2615</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activationLayer =</div><div class="line"><a name="l02616"></a><span class="lineno"> 2616</span>&#160;        m_Network-&gt;AddActivationLayer(activationDesc, layerName.c_str());</div><div class="line"><a name="l02617"></a><span class="lineno"> 2617</span>&#160;</div><div class="line"><a name="l02618"></a><span class="lineno"> 2618</span>&#160;    <span class="keyword">auto</span> &amp; prevOutputSlot = prevLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(outputSlot);</div><div class="line"><a name="l02619"></a><span class="lineno"> 2619</span>&#160;    prevOutputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(activationLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02620"></a><span class="lineno"> 2620</span>&#160;    activationLayer-&gt;<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="l02621"></a><span class="lineno"> 2621</span>&#160;    <span class="keywordflow">return</span> activationLayer;</div><div class="line"><a name="l02622"></a><span class="lineno"> 2622</span>&#160;}</div><div class="line"><a name="l02623"></a><span class="lineno"> 2623</span>&#160;</div><div class="line"><a name="l02624"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac5411554ab8c02ca286af52c98f6bd87"> 2624</a></span>&#160;<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">TfLiteParser::ModelPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac5411554ab8c02ca286af52c98f6bd87">TfLiteParser::LoadModelFromFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span> * fileName)</div><div class="line"><a name="l02625"></a><span class="lineno"> 2625</span>&#160;{</div><div class="line"><a name="l02626"></a><span class="lineno"> 2626</span>&#160;    <span class="keywordflow">if</span> (fileName == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l02627"></a><span class="lineno"> 2627</span>&#160;    {</div><div class="line"><a name="l02628"></a><span class="lineno"> 2628</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(boost::format(<span class="stringliteral">&quot;Invalid (null) file name %1%&quot;</span>) %</div><div class="line"><a name="l02629"></a><span class="lineno"> 2629</span>&#160;                                       <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02630"></a><span class="lineno"> 2630</span>&#160;    }</div><div class="line"><a name="l02631"></a><span class="lineno"> 2631</span>&#160;    boost::system::error_code errorCode;</div><div class="line"><a name="l02632"></a><span class="lineno"> 2632</span>&#160;    boost::filesystem::path pathToFile(fileName);</div><div class="line"><a name="l02633"></a><span class="lineno"> 2633</span>&#160;    <span class="keywordflow">if</span> (!boost::filesystem::exists(pathToFile, errorCode))</div><div class="line"><a name="l02634"></a><span class="lineno"> 2634</span>&#160;    {</div><div class="line"><a name="l02635"></a><span class="lineno"> 2635</span>&#160;        std::string locationString = <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString();</div><div class="line"><a name="l02636"></a><span class="lineno"> 2636</span>&#160;        std::string msg = boost::str(boost::format(<span class="stringliteral">&quot;Cannot find the file (%1%) errorCode: %2% %3%&quot;</span>) %</div><div class="line"><a name="l02637"></a><span class="lineno"> 2637</span>&#160;                                     fileName %</div><div class="line"><a name="l02638"></a><span class="lineno"> 2638</span>&#160;                                     errorCode %</div><div class="line"><a name="l02639"></a><span class="lineno"> 2639</span>&#160;                                         locationString);</div><div class="line"><a name="l02640"></a><span class="lineno"> 2640</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_file_not_found_exception.xhtml">FileNotFoundException</a>(msg);</div><div class="line"><a name="l02641"></a><span class="lineno"> 2641</span>&#160;    }</div><div class="line"><a name="l02642"></a><span class="lineno"> 2642</span>&#160;    std::ifstream file(fileName, std::ios::binary);</div><div class="line"><a name="l02643"></a><span class="lineno"> 2643</span>&#160;    std::string fileContent((std::istreambuf_iterator&lt;char&gt;(file)), std::istreambuf_iterator&lt;char&gt;());</div><div class="line"><a name="l02644"></a><span class="lineno"> 2644</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a858104f225c302988fba35c1cb299066">LoadModelFromBinary</a>(reinterpret_cast&lt;const uint8_t *&gt;(fileContent.c_str()),</div><div class="line"><a name="l02645"></a><span class="lineno"> 2645</span>&#160;                               fileContent.size());</div><div class="line"><a name="l02646"></a><span class="lineno"> 2646</span>&#160;}</div><div class="line"><a name="l02647"></a><span class="lineno"> 2647</span>&#160;</div><div class="line"><a name="l02648"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a858104f225c302988fba35c1cb299066"> 2648</a></span>&#160;<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">TfLiteParser::ModelPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a858104f225c302988fba35c1cb299066">TfLiteParser::LoadModelFromBinary</a>(<span class="keyword">const</span> uint8_t * binaryContent, <span class="keywordtype">size_t</span> len)</div><div class="line"><a name="l02649"></a><span class="lineno"> 2649</span>&#160;{</div><div class="line"><a name="l02650"></a><span class="lineno"> 2650</span>&#160;    <span class="keywordflow">if</span> (binaryContent == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l02651"></a><span class="lineno"> 2651</span>&#160;     {</div><div class="line"><a name="l02652"></a><span class="lineno"> 2652</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(boost::format(<span class="stringliteral">&quot;Invalid (null) binary content %1%&quot;</span>) %</div><div class="line"><a name="l02653"></a><span class="lineno"> 2653</span>&#160;                                       <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02654"></a><span class="lineno"> 2654</span>&#160;     }</div><div class="line"><a name="l02655"></a><span class="lineno"> 2655</span>&#160;    flatbuffers::Verifier verifier(binaryContent, len);</div><div class="line"><a name="l02656"></a><span class="lineno"> 2656</span>&#160;    <span class="keywordflow">if</span> (verifier.VerifyBuffer&lt;tflite::Model&gt;() == <span class="keyword">false</span>)</div><div class="line"><a name="l02657"></a><span class="lineno"> 2657</span>&#160;    {</div><div class="line"><a name="l02658"></a><span class="lineno"> 2658</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02659"></a><span class="lineno"> 2659</span>&#160;            boost::str(boost::format(<span class="stringliteral">&quot;Buffer doesn&#39;t conform to the expected Tensorflow Lite &quot;</span></div><div class="line"><a name="l02660"></a><span class="lineno"> 2660</span>&#160;                                     <span class="stringliteral">&quot;flatbuffers format. size:%1% %2%&quot;</span>) %</div><div class="line"><a name="l02661"></a><span class="lineno"> 2661</span>&#160;                       len %</div><div class="line"><a name="l02662"></a><span class="lineno"> 2662</span>&#160;                       <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02663"></a><span class="lineno"> 2663</span>&#160;    }</div><div class="line"><a name="l02664"></a><span class="lineno"> 2664</span>&#160;    <span class="keywordflow">return</span> tflite::UnPackModel(binaryContent);</div><div class="line"><a name="l02665"></a><span class="lineno"> 2665</span>&#160;}</div><div class="line"><a name="l02666"></a><span class="lineno"> 2666</span>&#160;</div><div class="line"><a name="l02667"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876"> 2667</a></span>&#160;<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParser::TensorRawPtrVector</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">TfLiteParser::GetInputs</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a> &amp; model,</div><div class="line"><a name="l02668"></a><span class="lineno"> 2668</span>&#160;                                                         <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l02669"></a><span class="lineno"> 2669</span>&#160;                                                         <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02670"></a><span class="lineno"> 2670</span>&#160;{</div><div class="line"><a name="l02671"></a><span class="lineno"> 2671</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02672"></a><span class="lineno"> 2672</span>&#160;</div><div class="line"><a name="l02673"></a><span class="lineno"> 2673</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; subgraphPtr = model-&gt;subgraphs[subgraphIndex];</div><div class="line"><a name="l02674"></a><span class="lineno"> 2674</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = subgraphPtr-&gt;operators[operatorIndex];</div><div class="line"><a name="l02675"></a><span class="lineno"> 2675</span>&#160;</div><div class="line"><a name="l02676"></a><span class="lineno"> 2676</span>&#160;    <span class="keywordtype">size_t</span> inputCount = operatorPtr-&gt;inputs.size();</div><div class="line"><a name="l02677"></a><span class="lineno"> 2677</span>&#160;    <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TensorRawPtrVector</a> result(inputCount);</div><div class="line"><a name="l02678"></a><span class="lineno"> 2678</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i=0; i&lt;inputCount; ++i)</div><div class="line"><a name="l02679"></a><span class="lineno"> 2679</span>&#160;    {</div><div class="line"><a name="l02680"></a><span class="lineno"> 2680</span>&#160;        uint32_t inputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(operatorPtr-&gt;inputs[i]);</div><div class="line"><a name="l02681"></a><span class="lineno"> 2681</span>&#160;        result[i] = subgraphPtr-&gt;tensors[inputId].get();</div><div class="line"><a name="l02682"></a><span class="lineno"> 2682</span>&#160;    }</div><div class="line"><a name="l02683"></a><span class="lineno"> 2683</span>&#160;    <span class="keywordflow">return</span> result;</div><div class="line"><a name="l02684"></a><span class="lineno"> 2684</span>&#160;}</div><div class="line"><a name="l02685"></a><span class="lineno"> 2685</span>&#160;</div><div class="line"><a name="l02686"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8"> 2686</a></span>&#160;<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParser::TensorRawPtrVector</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">TfLiteParser::GetOutputs</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a> &amp; model,</div><div class="line"><a name="l02687"></a><span class="lineno"> 2687</span>&#160;                                                          <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l02688"></a><span class="lineno"> 2688</span>&#160;                                                          <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02689"></a><span class="lineno"> 2689</span>&#160;{</div><div class="line"><a name="l02690"></a><span class="lineno"> 2690</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02691"></a><span class="lineno"> 2691</span>&#160;</div><div class="line"><a name="l02692"></a><span class="lineno"> 2692</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; subgraphPtr = model-&gt;subgraphs[subgraphIndex];</div><div class="line"><a name="l02693"></a><span class="lineno"> 2693</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = subgraphPtr-&gt;operators[operatorIndex];</div><div class="line"><a name="l02694"></a><span class="lineno"> 2694</span>&#160;</div><div class="line"><a name="l02695"></a><span class="lineno"> 2695</span>&#160;    <span class="keywordtype">size_t</span> outputCount = operatorPtr-&gt;outputs.size();</div><div class="line"><a name="l02696"></a><span class="lineno"> 2696</span>&#160;    <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TensorRawPtrVector</a> result(outputCount);</div><div class="line"><a name="l02697"></a><span class="lineno"> 2697</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i=0; i&lt;outputCount; ++i)</div><div class="line"><a name="l02698"></a><span class="lineno"> 2698</span>&#160;    {</div><div class="line"><a name="l02699"></a><span class="lineno"> 2699</span>&#160;        uint32_t outputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(operatorPtr-&gt;outputs[i]);</div><div class="line"><a name="l02700"></a><span class="lineno"> 2700</span>&#160;        <a class="code" href="_tf_lite_parser_8cpp.xhtml#aa1664dc13adbc85ac12fb584b76bfdae">CHECK_TENSOR</a>(model, subgraphIndex, outputId);</div><div class="line"><a name="l02701"></a><span class="lineno"> 2701</span>&#160;        result[i] = subgraphPtr-&gt;tensors[outputId].get();</div><div class="line"><a name="l02702"></a><span class="lineno"> 2702</span>&#160;    }</div><div class="line"><a name="l02703"></a><span class="lineno"> 2703</span>&#160;    <span class="keywordflow">return</span> result;</div><div class="line"><a name="l02704"></a><span class="lineno"> 2704</span>&#160;}</div><div class="line"><a name="l02705"></a><span class="lineno"> 2705</span>&#160;</div><div class="line"><a name="l02706"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abda91b07a94d0f498b76655e03647d9a"> 2706</a></span>&#160;<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a86428e0c674542488c7292dfbe2ce381">TfLiteParser::TensorIdRawPtrVector</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abda91b07a94d0f498b76655e03647d9a">TfLiteParser::GetSubgraphInputs</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a> &amp; model,</div><div class="line"><a name="l02707"></a><span class="lineno"> 2707</span>&#160;                                                                   <span class="keywordtype">size_t</span> subgraphIndex)</div><div class="line"><a name="l02708"></a><span class="lineno"> 2708</span>&#160;{</div><div class="line"><a name="l02709"></a><span class="lineno"> 2709</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(model, subgraphIndex);</div><div class="line"><a name="l02710"></a><span class="lineno"> 2710</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; subgraphPtr = model-&gt;subgraphs[subgraphIndex];</div><div class="line"><a name="l02711"></a><span class="lineno"> 2711</span>&#160;</div><div class="line"><a name="l02712"></a><span class="lineno"> 2712</span>&#160;    <span class="keywordtype">size_t</span> inputCount = subgraphPtr-&gt;inputs.size();</div><div class="line"><a name="l02713"></a><span class="lineno"> 2713</span>&#160;    <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a86428e0c674542488c7292dfbe2ce381">TensorIdRawPtrVector</a> result(inputCount);</div><div class="line"><a name="l02714"></a><span class="lineno"> 2714</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i=0; i&lt;inputCount; ++i)</div><div class="line"><a name="l02715"></a><span class="lineno"> 2715</span>&#160;    {</div><div class="line"><a name="l02716"></a><span class="lineno"> 2716</span>&#160;        uint32_t inputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(subgraphPtr-&gt;inputs[i]);</div><div class="line"><a name="l02717"></a><span class="lineno"> 2717</span>&#160;        <a class="code" href="_tf_lite_parser_8cpp.xhtml#aa1664dc13adbc85ac12fb584b76bfdae">CHECK_TENSOR</a>(model, subgraphIndex, inputId);</div><div class="line"><a name="l02718"></a><span class="lineno"> 2718</span>&#160;        result[i] = std::make_pair(inputId, subgraphPtr-&gt;tensors[inputId].get());</div><div class="line"><a name="l02719"></a><span class="lineno"> 2719</span>&#160;    }</div><div class="line"><a name="l02720"></a><span class="lineno"> 2720</span>&#160;    <span class="keywordflow">return</span> result;</div><div class="line"><a name="l02721"></a><span class="lineno"> 2721</span>&#160;}</div><div class="line"><a name="l02722"></a><span class="lineno"> 2722</span>&#160;</div><div class="line"><a name="l02723"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afba7f99227765786c4ed9cb2acc09739"> 2723</a></span>&#160;<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a86428e0c674542488c7292dfbe2ce381">TfLiteParser::TensorIdRawPtrVector</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afba7f99227765786c4ed9cb2acc09739">TfLiteParser::GetSubgraphOutputs</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a> &amp; model,</div><div class="line"><a name="l02724"></a><span class="lineno"> 2724</span>&#160;                                                                    <span class="keywordtype">size_t</span> subgraphIndex)</div><div class="line"><a name="l02725"></a><span class="lineno"> 2725</span>&#160;{</div><div class="line"><a name="l02726"></a><span class="lineno"> 2726</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(model, subgraphIndex);</div><div class="line"><a name="l02727"></a><span class="lineno"> 2727</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; subgraphPtr = model-&gt;subgraphs[subgraphIndex];</div><div class="line"><a name="l02728"></a><span class="lineno"> 2728</span>&#160;</div><div class="line"><a name="l02729"></a><span class="lineno"> 2729</span>&#160;    <span class="keywordtype">size_t</span> outputCount = subgraphPtr-&gt;outputs.size();</div><div class="line"><a name="l02730"></a><span class="lineno"> 2730</span>&#160;    <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a86428e0c674542488c7292dfbe2ce381">TensorIdRawPtrVector</a> result(outputCount);</div><div class="line"><a name="l02731"></a><span class="lineno"> 2731</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i=0; i&lt;outputCount; ++i)</div><div class="line"><a name="l02732"></a><span class="lineno"> 2732</span>&#160;    {</div><div class="line"><a name="l02733"></a><span class="lineno"> 2733</span>&#160;        uint32_t outputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(subgraphPtr-&gt;outputs[i]);</div><div class="line"><a name="l02734"></a><span class="lineno"> 2734</span>&#160;        result[i] = std::make_pair(outputId, subgraphPtr-&gt;tensors[outputId].get());</div><div class="line"><a name="l02735"></a><span class="lineno"> 2735</span>&#160;    }</div><div class="line"><a name="l02736"></a><span class="lineno"> 2736</span>&#160;    <span class="keywordflow">return</span> result;</div><div class="line"><a name="l02737"></a><span class="lineno"> 2737</span>&#160;}</div><div class="line"><a name="l02738"></a><span class="lineno"> 2738</span>&#160;</div><div class="line"><a name="l02739"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575"> 2739</a></span>&#160;std::vector&lt;int32_t&gt;&amp; <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">TfLiteParser::GetInputTensorIds</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a>&amp; model,</div><div class="line"><a name="l02740"></a><span class="lineno"> 2740</span>&#160;                                                      <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l02741"></a><span class="lineno"> 2741</span>&#160;                                                      <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02742"></a><span class="lineno"> 2742</span>&#160;{</div><div class="line"><a name="l02743"></a><span class="lineno"> 2743</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02744"></a><span class="lineno"> 2744</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; subgraphPtr = model-&gt;subgraphs[subgraphIndex];</div><div class="line"><a name="l02745"></a><span class="lineno"> 2745</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = subgraphPtr-&gt;operators[operatorIndex];</div><div class="line"><a name="l02746"></a><span class="lineno"> 2746</span>&#160;    <span class="keywordflow">return</span> operatorPtr-&gt;inputs;</div><div class="line"><a name="l02747"></a><span class="lineno"> 2747</span>&#160;}</div><div class="line"><a name="l02748"></a><span class="lineno"> 2748</span>&#160;</div><div class="line"><a name="l02749"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f"> 2749</a></span>&#160;std::vector&lt;int32_t&gt;&amp; <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">TfLiteParser::GetOutputTensorIds</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a>&amp; model,</div><div class="line"><a name="l02750"></a><span class="lineno"> 2750</span>&#160;                                                       <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l02751"></a><span class="lineno"> 2751</span>&#160;                                                       <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02752"></a><span class="lineno"> 2752</span>&#160;{</div><div class="line"><a name="l02753"></a><span class="lineno"> 2753</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02754"></a><span class="lineno"> 2754</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; subgraphPtr = model-&gt;subgraphs[subgraphIndex];</div><div class="line"><a name="l02755"></a><span class="lineno"> 2755</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; operatorPtr = subgraphPtr-&gt;operators[operatorIndex];</div><div class="line"><a name="l02756"></a><span class="lineno"> 2756</span>&#160;    <span class="keywordflow">return</span> operatorPtr-&gt;outputs;</div><div class="line"><a name="l02757"></a><span class="lineno"> 2757</span>&#160;}</div><div class="line"><a name="l02758"></a><span class="lineno"> 2758</span>&#160;</div><div class="line"><a name="l02759"></a><span class="lineno"> 2759</span>&#160;<span class="keywordtype">void</span> TfLiteParser::RegisterInputSlots(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l02760"></a><span class="lineno"> 2760</span>&#160;                                      <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l02761"></a><span class="lineno"> 2761</span>&#160;                                      <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer,</div><div class="line"><a name="l02762"></a><span class="lineno"> 2762</span>&#160;                                      <span class="keyword">const</span> std::vector&lt;unsigned int&gt;&amp; tensorIndexes)</div><div class="line"><a name="l02763"></a><span class="lineno"> 2763</span>&#160;{</div><div class="line"><a name="l02764"></a><span class="lineno"> 2764</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02765"></a><span class="lineno"> 2765</span>&#160;    BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02766"></a><span class="lineno"> 2766</span>&#160;    <span class="keywordflow">if</span> (tensorIndexes.size() != layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>())</div><div class="line"><a name="l02767"></a><span class="lineno"> 2767</span>&#160;    {</div><div class="line"><a name="l02768"></a><span class="lineno"> 2768</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02769"></a><span class="lineno"> 2769</span>&#160;            boost::str(boost::format(<span class="stringliteral">&quot;The number of tensor inputs (%1%) does not match the number expected (%2%)&quot;</span></div><div class="line"><a name="l02770"></a><span class="lineno"> 2770</span>&#160;                                     <span class="stringliteral">&quot; for subgraph:%3% operator index:%4% %5%&quot;</span>) %</div><div class="line"><a name="l02771"></a><span class="lineno"> 2771</span>&#160;                       tensorIndexes.size() %</div><div class="line"><a name="l02772"></a><span class="lineno"> 2772</span>&#160;                       layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>() %</div><div class="line"><a name="l02773"></a><span class="lineno"> 2773</span>&#160;                       subgraphIndex %</div><div class="line"><a name="l02774"></a><span class="lineno"> 2774</span>&#160;                       operatorIndex %</div><div class="line"><a name="l02775"></a><span class="lineno"> 2775</span>&#160;                       <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02776"></a><span class="lineno"> 2776</span>&#160;    }</div><div class="line"><a name="l02777"></a><span class="lineno"> 2777</span>&#160;</div><div class="line"><a name="l02778"></a><span class="lineno"> 2778</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> slotIndex = 0; slotIndex &lt; layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>(); ++slotIndex)</div><div class="line"><a name="l02779"></a><span class="lineno"> 2779</span>&#160;    {</div><div class="line"><a name="l02780"></a><span class="lineno"> 2780</span>&#160;        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tensorIndex = tensorIndexes[slotIndex];</div><div class="line"><a name="l02781"></a><span class="lineno"> 2781</span>&#160;        <a class="code" href="classarmnn_1_1_i_input_slot.xhtml">armnn::IInputSlot</a>* slot = &amp;(layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(slotIndex));</div><div class="line"><a name="l02782"></a><span class="lineno"> 2782</span>&#160;        RegisterConsumerOfTensor(subgraphIndex, tensorIndex, slot);</div><div class="line"><a name="l02783"></a><span class="lineno"> 2783</span>&#160;    }</div><div class="line"><a name="l02784"></a><span class="lineno"> 2784</span>&#160;}</div><div class="line"><a name="l02785"></a><span class="lineno"> 2785</span>&#160;</div><div class="line"><a name="l02786"></a><span class="lineno"> 2786</span>&#160;<span class="keywordtype">void</span> TfLiteParser::RegisterOutputSlots(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l02787"></a><span class="lineno"> 2787</span>&#160;                                       <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l02788"></a><span class="lineno"> 2788</span>&#160;                                       <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer,</div><div class="line"><a name="l02789"></a><span class="lineno"> 2789</span>&#160;                                       <span class="keyword">const</span> std::vector&lt;unsigned int&gt;&amp; tensorIndexes)</div><div class="line"><a name="l02790"></a><span class="lineno"> 2790</span>&#160;{</div><div class="line"><a name="l02791"></a><span class="lineno"> 2791</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02792"></a><span class="lineno"> 2792</span>&#160;    BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02793"></a><span class="lineno"> 2793</span>&#160;    <span class="keywordflow">if</span> (tensorIndexes.size() != layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>())</div><div class="line"><a name="l02794"></a><span class="lineno"> 2794</span>&#160;    {</div><div class="line"><a name="l02795"></a><span class="lineno"> 2795</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02796"></a><span class="lineno"> 2796</span>&#160;            boost::str(boost::format(<span class="stringliteral">&quot;The number of tensor outputs (%1%) does not match the number expected (%2%)&quot;</span></div><div class="line"><a name="l02797"></a><span class="lineno"> 2797</span>&#160;                                     <span class="stringliteral">&quot; for subgraph:%3% operator index:%4% %5%&quot;</span>) %</div><div class="line"><a name="l02798"></a><span class="lineno"> 2798</span>&#160;                       tensorIndexes.size() %</div><div class="line"><a name="l02799"></a><span class="lineno"> 2799</span>&#160;                       layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>() %</div><div class="line"><a name="l02800"></a><span class="lineno"> 2800</span>&#160;                       subgraphIndex %</div><div class="line"><a name="l02801"></a><span class="lineno"> 2801</span>&#160;                       operatorIndex %</div><div class="line"><a name="l02802"></a><span class="lineno"> 2802</span>&#160;                       <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02803"></a><span class="lineno"> 2803</span>&#160;    }</div><div class="line"><a name="l02804"></a><span class="lineno"> 2804</span>&#160;</div><div class="line"><a name="l02805"></a><span class="lineno"> 2805</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> slotIndex = 0; slotIndex &lt; layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(); ++slotIndex)</div><div class="line"><a name="l02806"></a><span class="lineno"> 2806</span>&#160;    {</div><div class="line"><a name="l02807"></a><span class="lineno"> 2807</span>&#160;        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tensorIndex = tensorIndexes[slotIndex];</div><div class="line"><a name="l02808"></a><span class="lineno"> 2808</span>&#160;        <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>* slot = &amp;(layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(slotIndex));</div><div class="line"><a name="l02809"></a><span class="lineno"> 2809</span>&#160;        RegisterProducerOfTensor(subgraphIndex, tensorIndex, slot);</div><div class="line"><a name="l02810"></a><span class="lineno"> 2810</span>&#160;    }</div><div class="line"><a name="l02811"></a><span class="lineno"> 2811</span>&#160;}</div><div class="line"><a name="l02812"></a><span class="lineno"> 2812</span>&#160;</div><div class="line"><a name="l02813"></a><span class="lineno"> 2813</span>&#160;<span class="keywordtype">void</span> TfLiteParser::SetupInputLayers(<span class="keywordtype">size_t</span> subgraphIndex)</div><div class="line"><a name="l02814"></a><span class="lineno"> 2814</span>&#160;{</div><div class="line"><a name="l02815"></a><span class="lineno"> 2815</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphIndex);</div><div class="line"><a name="l02816"></a><span class="lineno"> 2816</span>&#160;</div><div class="line"><a name="l02817"></a><span class="lineno"> 2817</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abda91b07a94d0f498b76655e03647d9a">GetSubgraphInputs</a>(m_Model, subgraphIndex);</div><div class="line"><a name="l02818"></a><span class="lineno"> 2818</span>&#160;    <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span> &amp; tensorIdAndPtr : inputs)</div><div class="line"><a name="l02819"></a><span class="lineno"> 2819</span>&#160;    {</div><div class="line"><a name="l02820"></a><span class="lineno"> 2820</span>&#160;        <span class="keyword">auto</span> bindingId = GenerateLayerBindingId(subgraphIndex, tensorIdAndPtr.first);</div><div class="line"><a name="l02821"></a><span class="lineno"> 2821</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer =</div><div class="line"><a name="l02822"></a><span class="lineno"> 2822</span>&#160;            m_Network-&gt;AddInputLayer(bindingId, tensorIdAndPtr.second-&gt;name.c_str());</div><div class="line"><a name="l02823"></a><span class="lineno"> 2823</span>&#160;</div><div class="line"><a name="l02824"></a><span class="lineno"> 2824</span>&#160;        <span class="keyword">auto</span> tensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(tensorIdAndPtr.second);</div><div class="line"><a name="l02825"></a><span class="lineno"> 2825</span>&#160;        layer-&gt;<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="l02826"></a><span class="lineno"> 2826</span>&#160;</div><div class="line"><a name="l02827"></a><span class="lineno"> 2827</span>&#160;        RegisterOutputSlots(subgraphIndex,</div><div class="line"><a name="l02828"></a><span class="lineno"> 2828</span>&#160;                            VIRTUAL_OPERATOR_ID,</div><div class="line"><a name="l02829"></a><span class="lineno"> 2829</span>&#160;                            layer,</div><div class="line"><a name="l02830"></a><span class="lineno"> 2830</span>&#160;                            { <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(tensorIdAndPtr.first) });</div><div class="line"><a name="l02831"></a><span class="lineno"> 2831</span>&#160;    }</div><div class="line"><a name="l02832"></a><span class="lineno"> 2832</span>&#160;}</div><div class="line"><a name="l02833"></a><span class="lineno"> 2833</span>&#160;</div><div class="line"><a name="l02834"></a><span class="lineno"> 2834</span>&#160;<span class="keywordtype">void</span> TfLiteParser::SetupOutputLayers(<span class="keywordtype">size_t</span> subgraphIndex)</div><div class="line"><a name="l02835"></a><span class="lineno"> 2835</span>&#160;{</div><div class="line"><a name="l02836"></a><span class="lineno"> 2836</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphIndex);</div><div class="line"><a name="l02837"></a><span class="lineno"> 2837</span>&#160;</div><div class="line"><a name="l02838"></a><span class="lineno"> 2838</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afba7f99227765786c4ed9cb2acc09739">GetSubgraphOutputs</a>(m_Model, subgraphIndex);</div><div class="line"><a name="l02839"></a><span class="lineno"> 2839</span>&#160;    <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span> &amp; tensorIdAndPtr : outputs)</div><div class="line"><a name="l02840"></a><span class="lineno"> 2840</span>&#160;    {</div><div class="line"><a name="l02841"></a><span class="lineno"> 2841</span>&#160;        <span class="keyword">auto</span> bindingId = GenerateLayerBindingId(subgraphIndex, tensorIdAndPtr.first);</div><div class="line"><a name="l02842"></a><span class="lineno"> 2842</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer =</div><div class="line"><a name="l02843"></a><span class="lineno"> 2843</span>&#160;            m_Network-&gt;AddOutputLayer(bindingId, tensorIdAndPtr.second-&gt;name.c_str());</div><div class="line"><a name="l02844"></a><span class="lineno"> 2844</span>&#160;</div><div class="line"><a name="l02845"></a><span class="lineno"> 2845</span>&#160;        RegisterInputSlots(subgraphIndex,</div><div class="line"><a name="l02846"></a><span class="lineno"> 2846</span>&#160;                           VIRTUAL_OPERATOR_ID,</div><div class="line"><a name="l02847"></a><span class="lineno"> 2847</span>&#160;                           layer,</div><div class="line"><a name="l02848"></a><span class="lineno"> 2848</span>&#160;                           { <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(tensorIdAndPtr.first) });</div><div class="line"><a name="l02849"></a><span class="lineno"> 2849</span>&#160;    }</div><div class="line"><a name="l02850"></a><span class="lineno"> 2850</span>&#160;}</div><div class="line"><a name="l02851"></a><span class="lineno"> 2851</span>&#160;</div><div class="line"><a name="l02852"></a><span class="lineno"> 2852</span>&#160;<span class="keywordtype">void</span> TfLiteParser::SetupConstantLayers(<span class="keywordtype">size_t</span> subgraphIndex)</div><div class="line"><a name="l02853"></a><span class="lineno"> 2853</span>&#160;{</div><div class="line"><a name="l02854"></a><span class="lineno"> 2854</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphIndex);</div><div class="line"><a name="l02855"></a><span class="lineno"> 2855</span>&#160;</div><div class="line"><a name="l02856"></a><span class="lineno"> 2856</span>&#160;    <span class="keyword">const</span> <span class="keyword">auto</span> &amp; subgraphPtr = m_Model-&gt;subgraphs[subgraphIndex];</div><div class="line"><a name="l02857"></a><span class="lineno"> 2857</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> subgraphIndex = 0; subgraphIndex &lt; m_SubgraphConnections.size(); ++subgraphIndex)</div><div class="line"><a name="l02858"></a><span class="lineno"> 2858</span>&#160;    {</div><div class="line"><a name="l02859"></a><span class="lineno"> 2859</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tensorIndex = 0; tensorIndex &lt; m_SubgraphConnections[subgraphIndex].size(); ++tensorIndex)</div><div class="line"><a name="l02860"></a><span class="lineno"> 2860</span>&#160;        {</div><div class="line"><a name="l02861"></a><span class="lineno"> 2861</span>&#160;            <span class="keywordflow">if</span> (m_SubgraphConnections[subgraphIndex][tensorIndex].outputSlot == <span class="keyword">nullptr</span> &amp;&amp;</div><div class="line"><a name="l02862"></a><span class="lineno"> 2862</span>&#160;                m_SubgraphConnections[subgraphIndex][tensorIndex].inputSlots.size() &gt; 0)</div><div class="line"><a name="l02863"></a><span class="lineno"> 2863</span>&#160;            {</div><div class="line"><a name="l02864"></a><span class="lineno"> 2864</span>&#160;                <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TensorRawPtr</a> tensorPtr = subgraphPtr-&gt;tensors[tensorIndex].get();</div><div class="line"><a name="l02865"></a><span class="lineno"> 2865</span>&#160;                <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(tensorPtr);</div><div class="line"><a name="l02866"></a><span class="lineno"> 2866</span>&#160;                <span class="keyword">auto</span> tensorAndData = CreateConstTensor(tensorPtr,</div><div class="line"><a name="l02867"></a><span class="lineno"> 2867</span>&#160;                                                       tensorInfo,</div><div class="line"><a name="l02868"></a><span class="lineno"> 2868</span>&#160;                                                       <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::PermutationVector&amp;&gt;</a>());</div><div class="line"><a name="l02869"></a><span class="lineno"> 2869</span>&#160;</div><div class="line"><a name="l02870"></a><span class="lineno"> 2870</span>&#160;                std::string layerName = boost::str(boost::format(<span class="stringliteral">&quot;Constant:%1%&quot;</span>) % tensorPtr-&gt;name);</div><div class="line"><a name="l02871"></a><span class="lineno"> 2871</span>&#160;                <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a> *layer =</div><div class="line"><a name="l02872"></a><span class="lineno"> 2872</span>&#160;                    m_Network-&gt;AddConstantLayer(tensorAndData.first, layerName.c_str());</div><div class="line"><a name="l02873"></a><span class="lineno"> 2873</span>&#160;</div><div class="line"><a name="l02874"></a><span class="lineno"> 2874</span>&#160;                layer-&gt;<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="l02875"></a><span class="lineno"> 2875</span>&#160;                RegisterOutputSlots(subgraphIndex,</div><div class="line"><a name="l02876"></a><span class="lineno"> 2876</span>&#160;                                    VIRTUAL_OPERATOR_ID,</div><div class="line"><a name="l02877"></a><span class="lineno"> 2877</span>&#160;                                    layer,</div><div class="line"><a name="l02878"></a><span class="lineno"> 2878</span>&#160;                                    { tensorIndex });</div><div class="line"><a name="l02879"></a><span class="lineno"> 2879</span>&#160;</div><div class="line"><a name="l02880"></a><span class="lineno"> 2880</span>&#160;            }</div><div class="line"><a name="l02881"></a><span class="lineno"> 2881</span>&#160;        }</div><div class="line"><a name="l02882"></a><span class="lineno"> 2882</span>&#160;    }</div><div class="line"><a name="l02883"></a><span class="lineno"> 2883</span>&#160;}</div><div class="line"><a name="l02884"></a><span class="lineno"> 2884</span>&#160;</div><div class="line"><a name="l02885"></a><span class="lineno"> 2885</span>&#160;<span class="comment">// example usage: BufferRawPtr bufferPtr = GetBuffer(m_Model, inputs[0]-&gt;buffer);</span></div><div class="line"><a name="l02886"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222"> 2886</a></span>&#160;<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">TfLiteParser::BufferRawPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">TfLiteParser::GetBuffer</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a>&amp; model, <span class="keywordtype">size_t</span> bufferIndex)</div><div class="line"><a name="l02887"></a><span class="lineno"> 2887</span>&#160;{</div><div class="line"><a name="l02888"></a><span class="lineno"> 2888</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#a7c88d54e3f895030c70330a4c9d76a7a">CHECK_BUFFER</a>(model, bufferIndex);</div><div class="line"><a name="l02889"></a><span class="lineno"> 2889</span>&#160;    <span class="keywordflow">return</span> model-&gt;buffers[bufferIndex].get();</div><div class="line"><a name="l02890"></a><span class="lineno"> 2890</span>&#160;}</div><div class="line"><a name="l02891"></a><span class="lineno"> 2891</span>&#160;</div><div class="line"><a name="l02892"></a><span class="lineno"> 2892</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l02893"></a><span class="lineno"> 2893</span>&#160;std::pair&lt;armnn::ConstTensor, TfLiteParser::SupportedDataStorage&gt;</div><div class="line"><a name="l02894"></a><span class="lineno"> 2894</span>&#160;TfLiteParser::CreateConstTensorAndStoreData(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">TfLiteParser::BufferRawPtr</a> bufferPtr,</div><div class="line"><a name="l02895"></a><span class="lineno"> 2895</span>&#160;                                            <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TfLiteParser::TensorRawPtr</a> tensorPtr,</div><div class="line"><a name="l02896"></a><span class="lineno"> 2896</span>&#160;                                            <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>&amp; tensorInfo,</div><div class="line"><a name="l02897"></a><span class="lineno"> 2897</span>&#160;                                            <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::PermutationVector&amp;&gt;</a> permutationVector)</div><div class="line"><a name="l02898"></a><span class="lineno"> 2898</span>&#160;{</div><div class="line"><a name="l02899"></a><span class="lineno"> 2899</span>&#160;    <span class="keyword">auto</span> constData = CreateConstTensorImpl&lt;T&gt;(bufferPtr,</div><div class="line"><a name="l02900"></a><span class="lineno"> 2900</span>&#160;                                              tensorPtr,</div><div class="line"><a name="l02901"></a><span class="lineno"> 2901</span>&#160;                                              tensorInfo,</div><div class="line"><a name="l02902"></a><span class="lineno"> 2902</span>&#160;                                              permutationVector);</div><div class="line"><a name="l02903"></a><span class="lineno"> 2903</span>&#160;    TfLiteParser::SupportedDataStorage storage(std::move(constData.second));</div><div class="line"><a name="l02904"></a><span class="lineno"> 2904</span>&#160;    <span class="keywordflow">return</span> std::make_pair(constData.first, std::move(storage));</div><div class="line"><a name="l02905"></a><span class="lineno"> 2905</span>&#160;}</div><div class="line"><a name="l02906"></a><span class="lineno"> 2906</span>&#160;</div><div class="line"><a name="l02907"></a><span class="lineno"> 2907</span>&#160;std::pair&lt;armnn::ConstTensor, TfLiteParser::SupportedDataStorage&gt;</div><div class="line"><a name="l02908"></a><span class="lineno"> 2908</span>&#160;TfLiteParser::CreateConstTensor(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TensorRawPtr</a> tensorPtr,</div><div class="line"><a name="l02909"></a><span class="lineno"> 2909</span>&#160;                                <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>&amp; tensorInfo,</div><div class="line"><a name="l02910"></a><span class="lineno"> 2910</span>&#160;                                <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional&lt;armnn::PermutationVector&amp;&gt;</a> permutationVector)</div><div class="line"><a name="l02911"></a><span class="lineno"> 2911</span>&#160;{</div><div class="line"><a name="l02912"></a><span class="lineno"> 2912</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#ae38d96fe05581ea025713b3e781c5a43">CHECK_TENSOR_PTR</a>(tensorPtr);</div><div class="line"><a name="l02913"></a><span class="lineno"> 2913</span>&#160;    <span class="keyword">auto</span> bufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, tensorPtr-&gt;buffer);</div><div class="line"><a name="l02914"></a><span class="lineno"> 2914</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#ae6440b8ea95cf981cd7bbffa52c22fe1">CHECK_BUFFER_SIZE</a>(bufferPtr, tensorInfo, tensorPtr-&gt;buffer);</div><div class="line"><a name="l02915"></a><span class="lineno"> 2915</span>&#160;</div><div class="line"><a name="l02916"></a><span class="lineno"> 2916</span>&#160;    <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="l02917"></a><span class="lineno"> 2917</span>&#160;    {</div><div class="line"><a name="l02918"></a><span class="lineno"> 2918</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</div><div class="line"><a name="l02919"></a><span class="lineno"> 2919</span>&#160;            <span class="keywordflow">return</span> CreateConstTensorAndStoreData&lt;float&gt;(bufferPtr,</div><div class="line"><a name="l02920"></a><span class="lineno"> 2920</span>&#160;                                                        tensorPtr,</div><div class="line"><a name="l02921"></a><span class="lineno"> 2921</span>&#160;                                                        tensorInfo,</div><div class="line"><a name="l02922"></a><span class="lineno"> 2922</span>&#160;                                                        permutationVector);</div><div class="line"><a name="l02923"></a><span class="lineno"> 2923</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</div><div class="line"><a name="l02924"></a><span class="lineno"> 2924</span>&#160;            <span class="keywordflow">return</span> CreateConstTensorAndStoreData&lt;uint8_t&gt;(bufferPtr,</div><div class="line"><a name="l02925"></a><span class="lineno"> 2925</span>&#160;                                                          tensorPtr,</div><div class="line"><a name="l02926"></a><span class="lineno"> 2926</span>&#160;                                                          tensorInfo,</div><div class="line"><a name="l02927"></a><span class="lineno"> 2927</span>&#160;                                                          permutationVector);</div><div class="line"><a name="l02928"></a><span class="lineno"> 2928</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>:</div><div class="line"><a name="l02929"></a><span class="lineno"> 2929</span>&#160;            <span class="keywordflow">return</span> CreateConstTensorAndStoreData&lt;int8_t&gt;(bufferPtr,</div><div class="line"><a name="l02930"></a><span class="lineno"> 2930</span>&#160;                                                         tensorPtr,</div><div class="line"><a name="l02931"></a><span class="lineno"> 2931</span>&#160;                                                         tensorInfo,</div><div class="line"><a name="l02932"></a><span class="lineno"> 2932</span>&#160;                                                         permutationVector);</div><div class="line"><a name="l02933"></a><span class="lineno"> 2933</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>:</div><div class="line"><a name="l02934"></a><span class="lineno"> 2934</span>&#160;            <span class="keywordflow">return</span> CreateConstTensorAndStoreData&lt;int8_t&gt;(bufferPtr,</div><div class="line"><a name="l02935"></a><span class="lineno"> 2935</span>&#160;                                                         tensorPtr,</div><div class="line"><a name="l02936"></a><span class="lineno"> 2936</span>&#160;                                                         tensorInfo,</div><div class="line"><a name="l02937"></a><span class="lineno"> 2937</span>&#160;                                                         permutationVector);</div><div class="line"><a name="l02938"></a><span class="lineno"> 2938</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>:</div><div class="line"><a name="l02939"></a><span class="lineno"> 2939</span>&#160;            <span class="keywordflow">return</span> CreateConstTensorAndStoreData&lt;int32_t&gt;(bufferPtr,</div><div class="line"><a name="l02940"></a><span class="lineno"> 2940</span>&#160;                                                          tensorPtr,</div><div class="line"><a name="l02941"></a><span class="lineno"> 2941</span>&#160;                                                          tensorInfo,</div><div class="line"><a name="l02942"></a><span class="lineno"> 2942</span>&#160;                                                          permutationVector);</div><div class="line"><a name="l02943"></a><span class="lineno"> 2943</span>&#160;        <span class="keywordflow">default</span>:</div><div class="line"><a name="l02944"></a><span class="lineno"> 2944</span>&#160;        {</div><div class="line"><a name="l02945"></a><span class="lineno"> 2945</span>&#160;            std::stringstream errString;</div><div class="line"><a name="l02946"></a><span class="lineno"> 2946</span>&#160;            errString &lt;&lt; <span class="stringliteral">&quot;Unexpected datatype when creating const tensor: &quot;</span></div><div class="line"><a name="l02947"></a><span class="lineno"> 2947</span>&#160;                        &lt;&lt; <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">armnn::GetDataTypeName</a>(tensorInfo.GetDataType())</div><div class="line"><a name="l02948"></a><span class="lineno"> 2948</span>&#160;                        &lt;&lt; <span class="stringliteral">&quot; shape:&quot;</span> &lt;&lt; tensorInfo.GetShape()</div><div class="line"><a name="l02949"></a><span class="lineno"> 2949</span>&#160;                        &lt;&lt; <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString();</div><div class="line"><a name="l02950"></a><span class="lineno"> 2950</span>&#160;            <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="l02951"></a><span class="lineno"> 2951</span>&#160;        }</div><div class="line"><a name="l02952"></a><span class="lineno"> 2952</span>&#160;    }</div><div class="line"><a name="l02953"></a><span class="lineno"> 2953</span>&#160;}</div><div class="line"><a name="l02954"></a><span class="lineno"> 2954</span>&#160;</div><div class="line"><a name="l02955"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a91ba75587a31033088cb4f156e847efb"> 2955</a></span>&#160;<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.xhtml#a91ba75587a31033088cb4f156e847efb">TfLiteParser::GetNetworkInputBindingInfo</a>(<span class="keywordtype">size_t</span> subgraphId,</div><div class="line"><a name="l02956"></a><span class="lineno"> 2956</span>&#160;                                                          <span class="keyword">const</span> std::string&amp; name)<span class="keyword"> const</span></div><div class="line"><a name="l02957"></a><span class="lineno"> 2957</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02958"></a><span class="lineno"> 2958</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphId);</div><div class="line"><a name="l02959"></a><span class="lineno"> 2959</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abda91b07a94d0f498b76655e03647d9a">GetSubgraphInputs</a>(m_Model, subgraphId);</div><div class="line"><a name="l02960"></a><span class="lineno"> 2960</span>&#160;    <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span> &amp; input : inputs)</div><div class="line"><a name="l02961"></a><span class="lineno"> 2961</span>&#160;    {</div><div class="line"><a name="l02962"></a><span class="lineno"> 2962</span>&#160;        <span class="keywordflow">if</span> (input.second-&gt;name == name)</div><div class="line"><a name="l02963"></a><span class="lineno"> 2963</span>&#160;        {</div><div class="line"><a name="l02964"></a><span class="lineno"> 2964</span>&#160;            <span class="keyword">auto</span> bindingId = GenerateLayerBindingId(subgraphId, input.first);</div><div class="line"><a name="l02965"></a><span class="lineno"> 2965</span>&#160;            <span class="keywordflow">return</span> std::make_pair(bindingId, <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(input.second));</div><div class="line"><a name="l02966"></a><span class="lineno"> 2966</span>&#160;        }</div><div class="line"><a name="l02967"></a><span class="lineno"> 2967</span>&#160;    }</div><div class="line"><a name="l02968"></a><span class="lineno"> 2968</span>&#160;</div><div class="line"><a name="l02969"></a><span class="lineno"> 2969</span>&#160;    std::stringstream bindings;</div><div class="line"><a name="l02970"></a><span class="lineno"> 2970</span>&#160;    <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span> &amp; input : inputs)</div><div class="line"><a name="l02971"></a><span class="lineno"> 2971</span>&#160;    {</div><div class="line"><a name="l02972"></a><span class="lineno"> 2972</span>&#160;        bindings &lt;&lt; <span class="stringliteral">&quot;&#39;&quot;</span> &lt;&lt; input.second-&gt;name &lt;&lt; <span class="stringliteral">&quot;&#39; &quot;</span>;</div><div class="line"><a name="l02973"></a><span class="lineno"> 2973</span>&#160;    }</div><div class="line"><a name="l02974"></a><span class="lineno"> 2974</span>&#160;</div><div class="line"><a name="l02975"></a><span class="lineno"> 2975</span>&#160;    <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02976"></a><span class="lineno"> 2976</span>&#160;        boost::str(</div><div class="line"><a name="l02977"></a><span class="lineno"> 2977</span>&#160;            boost::format(<span class="stringliteral">&quot;No input binding found for subgraph:%1% and name:%2%. &quot;</span></div><div class="line"><a name="l02978"></a><span class="lineno"> 2978</span>&#160;                          <span class="stringliteral">&quot;Possible inputs are: [%3%] %4%&quot;</span>) %</div><div class="line"><a name="l02979"></a><span class="lineno"> 2979</span>&#160;            subgraphId %</div><div class="line"><a name="l02980"></a><span class="lineno"> 2980</span>&#160;            name %</div><div class="line"><a name="l02981"></a><span class="lineno"> 2981</span>&#160;            bindings.str() %</div><div class="line"><a name="l02982"></a><span class="lineno"> 2982</span>&#160;            <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02983"></a><span class="lineno"> 2983</span>&#160;}</div><div class="line"><a name="l02984"></a><span class="lineno"> 2984</span>&#160;</div><div class="line"><a name="l02985"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a475b7cb5db683bb6fbb1c3fae40cb2b3"> 2985</a></span>&#160;<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.xhtml#a475b7cb5db683bb6fbb1c3fae40cb2b3">TfLiteParser::GetNetworkOutputBindingInfo</a>(<span class="keywordtype">size_t</span> subgraphId,</div><div class="line"><a name="l02986"></a><span class="lineno"> 2986</span>&#160;                                                           <span class="keyword">const</span> std::string&amp; name)<span class="keyword"> const</span></div><div class="line"><a name="l02987"></a><span class="lineno"> 2987</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l02988"></a><span class="lineno"> 2988</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphId);</div><div class="line"><a name="l02989"></a><span class="lineno"> 2989</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afba7f99227765786c4ed9cb2acc09739">GetSubgraphOutputs</a>(m_Model, subgraphId);</div><div class="line"><a name="l02990"></a><span class="lineno"> 2990</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; outputs.size(); ++i)</div><div class="line"><a name="l02991"></a><span class="lineno"> 2991</span>&#160;    {</div><div class="line"><a name="l02992"></a><span class="lineno"> 2992</span>&#160;        <span class="keyword">auto</span> <span class="keyword">const</span> output = outputs[i];</div><div class="line"><a name="l02993"></a><span class="lineno"> 2993</span>&#160;        <span class="keywordflow">if</span> (output.second-&gt;name == name)</div><div class="line"><a name="l02994"></a><span class="lineno"> 2994</span>&#160;        {</div><div class="line"><a name="l02995"></a><span class="lineno"> 2995</span>&#160;            <span class="keyword">auto</span> bindingId = GenerateLayerBindingId(subgraphId, output.first);</div><div class="line"><a name="l02996"></a><span class="lineno"> 2996</span>&#160;            std::vector&lt;unsigned int&gt; shape = m_OverridenOutputShapes.size() &gt; 0 ?</div><div class="line"><a name="l02997"></a><span class="lineno"> 2997</span>&#160;                                                m_OverridenOutputShapes[i] : AsUnsignedVector(output.second-&gt;shape);</div><div class="line"><a name="l02998"></a><span class="lineno"> 2998</span>&#160;            <span class="keywordflow">return</span> std::make_pair(bindingId, <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(output.second, shape));</div><div class="line"><a name="l02999"></a><span class="lineno"> 2999</span>&#160;        }</div><div class="line"><a name="l03000"></a><span class="lineno"> 3000</span>&#160;    }</div><div class="line"><a name="l03001"></a><span class="lineno"> 3001</span>&#160;</div><div class="line"><a name="l03002"></a><span class="lineno"> 3002</span>&#160;    std::stringstream bindings;</div><div class="line"><a name="l03003"></a><span class="lineno"> 3003</span>&#160;    <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span> &amp; output : outputs)</div><div class="line"><a name="l03004"></a><span class="lineno"> 3004</span>&#160;    {</div><div class="line"><a name="l03005"></a><span class="lineno"> 3005</span>&#160;        bindings &lt;&lt; <span class="stringliteral">&quot;&#39;&quot;</span> &lt;&lt; output.second-&gt;name &lt;&lt; <span class="stringliteral">&quot;&#39; &quot;</span>;</div><div class="line"><a name="l03006"></a><span class="lineno"> 3006</span>&#160;    }</div><div class="line"><a name="l03007"></a><span class="lineno"> 3007</span>&#160;</div><div class="line"><a name="l03008"></a><span class="lineno"> 3008</span>&#160;    <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03009"></a><span class="lineno"> 3009</span>&#160;        boost::str(</div><div class="line"><a name="l03010"></a><span class="lineno"> 3010</span>&#160;            boost::format(<span class="stringliteral">&quot;No output binding found for subgraph:%1% and name:%2%. &quot;</span></div><div class="line"><a name="l03011"></a><span class="lineno"> 3011</span>&#160;                          <span class="stringliteral">&quot;Possible outputs are: [%3%] %4%&quot;</span>) %</div><div class="line"><a name="l03012"></a><span class="lineno"> 3012</span>&#160;            subgraphId %</div><div class="line"><a name="l03013"></a><span class="lineno"> 3013</span>&#160;            name %</div><div class="line"><a name="l03014"></a><span class="lineno"> 3014</span>&#160;            bindings.str() %</div><div class="line"><a name="l03015"></a><span class="lineno"> 3015</span>&#160;            <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03016"></a><span class="lineno"> 3016</span>&#160;}</div><div class="line"><a name="l03017"></a><span class="lineno"> 3017</span>&#160;</div><div class="line"><a name="l03018"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ae18739116b52dbab31bbf490450beb90"> 3018</a></span>&#160;<span class="keywordtype">size_t</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ae18739116b52dbab31bbf490450beb90">TfLiteParser::GetSubgraphCount</a>()<span class="keyword"> const</span></div><div class="line"><a name="l03019"></a><span class="lineno"> 3019</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l03020"></a><span class="lineno"> 3020</span>&#160;    <span class="keywordflow">return</span> m_Model-&gt;subgraphs.size();</div><div class="line"><a name="l03021"></a><span class="lineno"> 3021</span>&#160;}</div><div class="line"><a name="l03022"></a><span class="lineno"> 3022</span>&#160;</div><div class="line"><a name="l03023"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a58501692772880e7ef55485a4c95aab9"> 3023</a></span>&#160;std::vector&lt;std::string&gt; <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a58501692772880e7ef55485a4c95aab9">TfLiteParser::GetSubgraphInputTensorNames</a>(<span class="keywordtype">size_t</span> subgraphId)<span class="keyword"> const</span></div><div class="line"><a name="l03024"></a><span class="lineno"> 3024</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l03025"></a><span class="lineno"> 3025</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphId);</div><div class="line"><a name="l03026"></a><span class="lineno"> 3026</span>&#160;    <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abda91b07a94d0f498b76655e03647d9a">GetSubgraphInputs</a>(m_Model, subgraphId);</div><div class="line"><a name="l03027"></a><span class="lineno"> 3027</span>&#160;    std::vector&lt;std::string&gt; result;</div><div class="line"><a name="l03028"></a><span class="lineno"> 3028</span>&#160;    result.reserve(inputs.size());</div><div class="line"><a name="l03029"></a><span class="lineno"> 3029</span>&#160;    <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span> &amp; input : inputs)</div><div class="line"><a name="l03030"></a><span class="lineno"> 3030</span>&#160;    {</div><div class="line"><a name="l03031"></a><span class="lineno"> 3031</span>&#160;        result.push_back(input.second-&gt;name);</div><div class="line"><a name="l03032"></a><span class="lineno"> 3032</span>&#160;    }</div><div class="line"><a name="l03033"></a><span class="lineno"> 3033</span>&#160;    <span class="keywordflow">return</span> result;</div><div class="line"><a name="l03034"></a><span class="lineno"> 3034</span>&#160;}</div><div class="line"><a name="l03035"></a><span class="lineno"> 3035</span>&#160;</div><div class="line"><a name="l03036"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ab97e69d07f06e392bd0cc2e5bcbf1be6"> 3036</a></span>&#160;std::vector&lt;std::string&gt; <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ab97e69d07f06e392bd0cc2e5bcbf1be6">TfLiteParser::GetSubgraphOutputTensorNames</a>(<span class="keywordtype">size_t</span> subgraphId)<span class="keyword"> const</span></div><div class="line"><a name="l03037"></a><span class="lineno"> 3037</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l03038"></a><span class="lineno"> 3038</span>&#160;    <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphId);</div><div class="line"><a name="l03039"></a><span class="lineno"> 3039</span>&#160;    <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afba7f99227765786c4ed9cb2acc09739">GetSubgraphOutputs</a>(m_Model, subgraphId);</div><div class="line"><a name="l03040"></a><span class="lineno"> 3040</span>&#160;    std::vector&lt;std::string&gt; result;</div><div class="line"><a name="l03041"></a><span class="lineno"> 3041</span>&#160;    result.reserve(outputs.size());</div><div class="line"><a name="l03042"></a><span class="lineno"> 3042</span>&#160;    <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span> &amp; output : outputs)</div><div class="line"><a name="l03043"></a><span class="lineno"> 3043</span>&#160;    {</div><div class="line"><a name="l03044"></a><span class="lineno"> 3044</span>&#160;        result.push_back(output.second-&gt;name);</div><div class="line"><a name="l03045"></a><span class="lineno"> 3045</span>&#160;    }</div><div class="line"><a name="l03046"></a><span class="lineno"> 3046</span>&#160;    <span class="keywordflow">return</span> result;</div><div class="line"><a name="l03047"></a><span class="lineno"> 3047</span>&#160;}</div><div class="line"><a name="l03048"></a><span class="lineno"> 3048</span>&#160;</div><div class="line"><a name="l03049"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#acf8cc929eadbabf197b36f7364d3d2cb"> 3049</a></span>&#160;<a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml">ITfLiteParser</a>* <a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#acf8cc929eadbabf197b36f7364d3d2cb">ITfLiteParser::CreateRaw</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ITfLiteParser::TfLiteParserOptions&gt;</a>&amp; <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>)</div><div class="line"><a name="l03050"></a><span class="lineno"> 3050</span>&#160;{</div><div class="line"><a name="l03051"></a><span class="lineno"> 3051</span>&#160;    <span class="keywordflow">return</span> <span class="keyword">new</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a2ed4853234865d838da50085da99b2a6">TfLiteParser</a>(options);</div><div class="line"><a name="l03052"></a><span class="lineno"> 3052</span>&#160;}</div><div class="line"><a name="l03053"></a><span class="lineno"> 3053</span>&#160;</div><div class="line"><a name="l03054"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a9932449a89a62cfcfd72a4fedbee1ab7"> 3054</a></span>&#160;<a class="code" href="namespacearmnn_tf_lite_parser.xhtml#af69bedce3c37be895f75146016ba8a17">ITfLiteParserPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a9932449a89a62cfcfd72a4fedbee1ab7">ITfLiteParser::Create</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">Optional&lt;ITfLiteParser::TfLiteParserOptions&gt;</a>&amp; <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>)</div><div class="line"><a name="l03055"></a><span class="lineno"> 3055</span>&#160;{</div><div class="line"><a name="l03056"></a><span class="lineno"> 3056</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_tf_lite_parser.xhtml#af69bedce3c37be895f75146016ba8a17">ITfLiteParserPtr</a>(<a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#acf8cc929eadbabf197b36f7364d3d2cb">CreateRaw</a>(options), &amp;<a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a29a2a153962a701843d5c8ae953cb032">ITfLiteParser::Destroy</a>);</div><div class="line"><a name="l03057"></a><span class="lineno"> 3057</span>&#160;}</div><div class="line"><a name="l03058"></a><span class="lineno"> 3058</span>&#160;</div><div class="line"><a name="l03059"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a29a2a153962a701843d5c8ae953cb032"> 3059</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a29a2a153962a701843d5c8ae953cb032">ITfLiteParser::Destroy</a>(<a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml">ITfLiteParser</a>* parser)</div><div class="line"><a name="l03060"></a><span class="lineno"> 3060</span>&#160;{</div><div class="line"><a name="l03061"></a><span class="lineno"> 3061</span>&#160;    <span class="keyword">delete</span> parser;</div><div class="line"><a name="l03062"></a><span class="lineno"> 3062</span>&#160;}</div><div class="line"><a name="l03063"></a><span class="lineno"> 3063</span>&#160;</div><div class="line"><a name="l03064"></a><span class="lineno"> 3064</span>&#160;TfLiteParser::SupportedDataStorage::SupportedDataStorage(std::unique_ptr&lt;<span class="keywordtype">float</span>[]&gt; &amp;&amp; data)</div><div class="line"><a name="l03065"></a><span class="lineno"> 3065</span>&#160;: m_FloatData(std::move(data))</div><div class="line"><a name="l03066"></a><span class="lineno"> 3066</span>&#160;, m_Uint8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03067"></a><span class="lineno"> 3067</span>&#160;, m_Int8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03068"></a><span class="lineno"> 3068</span>&#160;, m_Int32Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03069"></a><span class="lineno"> 3069</span>&#160;{</div><div class="line"><a name="l03070"></a><span class="lineno"> 3070</span>&#160;}</div><div class="line"><a name="l03071"></a><span class="lineno"> 3071</span>&#160;</div><div class="line"><a name="l03072"></a><span class="lineno"> 3072</span>&#160;TfLiteParser::SupportedDataStorage::SupportedDataStorage(std::unique_ptr&lt;uint8_t[]&gt; &amp;&amp; data)</div><div class="line"><a name="l03073"></a><span class="lineno"> 3073</span>&#160;: m_FloatData(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03074"></a><span class="lineno"> 3074</span>&#160;, m_Uint8Data(std::move(data))</div><div class="line"><a name="l03075"></a><span class="lineno"> 3075</span>&#160;, m_Int8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03076"></a><span class="lineno"> 3076</span>&#160;, m_Int32Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03077"></a><span class="lineno"> 3077</span>&#160;{</div><div class="line"><a name="l03078"></a><span class="lineno"> 3078</span>&#160;}</div><div class="line"><a name="l03079"></a><span class="lineno"> 3079</span>&#160;</div><div class="line"><a name="l03080"></a><span class="lineno"> 3080</span>&#160;TfLiteParser::SupportedDataStorage::SupportedDataStorage(std::unique_ptr&lt;int8_t[]&gt; &amp;&amp; data)</div><div class="line"><a name="l03081"></a><span class="lineno"> 3081</span>&#160;: m_FloatData(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03082"></a><span class="lineno"> 3082</span>&#160;, m_Uint8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03083"></a><span class="lineno"> 3083</span>&#160;, m_Int8Data(std::move(data))</div><div class="line"><a name="l03084"></a><span class="lineno"> 3084</span>&#160;, m_Int32Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03085"></a><span class="lineno"> 3085</span>&#160;{</div><div class="line"><a name="l03086"></a><span class="lineno"> 3086</span>&#160;}</div><div class="line"><a name="l03087"></a><span class="lineno"> 3087</span>&#160;</div><div class="line"><a name="l03088"></a><span class="lineno"> 3088</span>&#160;TfLiteParser::SupportedDataStorage::SupportedDataStorage(std::unique_ptr&lt;int32_t[]&gt; &amp;&amp; data)</div><div class="line"><a name="l03089"></a><span class="lineno"> 3089</span>&#160;: m_FloatData(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03090"></a><span class="lineno"> 3090</span>&#160;, m_Uint8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03091"></a><span class="lineno"> 3091</span>&#160;, m_Int8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03092"></a><span class="lineno"> 3092</span>&#160;, m_Int32Data(std::move(data))</div><div class="line"><a name="l03093"></a><span class="lineno"> 3093</span>&#160;{</div><div class="line"><a name="l03094"></a><span class="lineno"> 3094</span>&#160;}</div><div class="line"><a name="l03095"></a><span class="lineno"> 3095</span>&#160;</div><div class="line"><a name="l03096"></a><span class="lineno"> 3096</span>&#160;} <span class="comment">// armnnTfLiteParser</span></div><div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a012b24cafd443425314d4f9e06cec6c1"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a012b24cafd443425314d4f9e06cec6c1">armnnTfLiteParser::TfLiteParser::CreateNetworkFromBinaryFile</a></div><div class="ttdeci">virtual armnn::INetworkPtr CreateNetworkFromBinaryFile(const char *graphFile) override</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#l00598">TfLiteParser.cpp:598</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Convolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00428">Descriptors.hpp:428</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#l00438">Descriptors.hpp:438</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#l00124">TfLiteParser.cpp:124</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_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_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Convolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00440">Descriptors.hpp:440</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a475b7cb5db683bb6fbb1c3fae40cb2b3"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a475b7cb5db683bb6fbb1c3fae40cb2b3">armnnTfLiteParser::TfLiteParser::GetNetworkOutputBindingInfo</a></div><div class="ttdeci">virtual BindingPointInfo GetNetworkOutputBindingInfo(size_t subgraphId, const std::string &amp;name) const override</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#l02985">TfLiteParser.cpp:2985</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#l00972">Descriptors.hpp:972</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a988cb5e216eb87d854414c6a0282eeb4"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a988cb5e216eb87d854414c6a0282eeb4">armnnTfLiteParser::TfLiteParser::SubgraphPtr</a></div><div class="ttdeci">std::unique_ptr&lt; tflite::SubGraphT &gt; SubgraphPtr</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00024">TfLiteParser.hpp:24</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a5336d7700f4a5bcc272fbc9216541222"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">armnnTfLiteParser::TfLiteParser::GetBuffer</a></div><div class="ttdeci">static BufferRawPtr GetBuffer(const ModelPtr &amp;model, size_t bufferIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02886">TfLiteParser.cpp:2886</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#l00190">Descriptors.hpp:190</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00061">INetwork.hpp:61</a></div></div>
<div class="ttc" id="structarmnn_1_1_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#l00545">Descriptors.hpp:545</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#l00355">Descriptors.hpp:355</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#l00490">Descriptors.hpp:490</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a858104f225c302988fba35c1cb299066"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a858104f225c302988fba35c1cb299066">armnnTfLiteParser::TfLiteParser::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#l02648">TfLiteParser.cpp:2648</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_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#l01079">Descriptors.hpp:1079</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#l00036">TfLiteParser.cpp:36</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 &amp; GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00088">Tensor.hpp:88</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#l00480">Descriptors.hpp:480</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#l00349">Descriptors.hpp:349</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_ae8ee09f5e3e78ecfdf00acfdc37588dc"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ae8ee09f5e3e78ecfdf00acfdc37588dc">armnnTfLiteParser::TfLiteParser::CreateNetworkFromBinary</a></div><div class="ttdeci">virtual armnn::INetworkPtr CreateNetworkFromBinary(const std::vector&lt; uint8_t &gt; &amp;binaryContent) override</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#l00605">TfLiteParser.cpp:605</a></div></div>
<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional</a></div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00270">Optional.hpp:270</a></div></div>
<div class="ttc" id="structarmnn_1_1_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#l01054">Descriptors.hpp:1054</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#l00758">Descriptors.hpp:758</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&lt; int &gt; 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#l01041">Descriptors.hpp:1041</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_abd8bee7fb9b86485a60bc7ee05114270"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abd8bee7fb9b86485a60bc7ee05114270">armnnTfLiteParser::TfLiteParser::TensorRawPtrVector</a></div><div class="ttdeci">std::vector&lt; TensorRawPtr &gt; TensorRawPtrVector</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00029">TfLiteParser.hpp:29</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#l00492">Descriptors.hpp:492</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#l00053">Tensor.hpp:53</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#l01062">Descriptors.hpp:1062</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#l00541">Descriptors.hpp:541</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#l00976">Descriptors.hpp:976</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#l00388">Descriptors.hpp:388</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#l00357">Descriptors.hpp:357</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="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#l00392">Descriptors.hpp:392</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_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#l00474">Descriptors.hpp:474</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_ac5411554ab8c02ca286af52c98f6bd87"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac5411554ab8c02ca286af52c98f6bd87">armnnTfLiteParser::TfLiteParser::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#l02624">TfLiteParser.cpp:2624</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#l01117">Descriptors.hpp:1117</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#l00213">Tensor.cpp:213</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#l00749">Descriptors.hpp:749</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#l00136">Descriptors.hpp:136</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml">armnnTfLiteParser::TfLiteParser</a></div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00019">TfLiteParser.hpp:19</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#l00367">Descriptors.hpp:367</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#l00531">Descriptors.hpp:531</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#l00684">Descriptors.hpp:684</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#l00210">TfLiteParser.cpp:210</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a4b49afca01112a4f4d023726ccd38876"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">armnnTfLiteParser::TfLiteParser::GetInputs</a></div><div class="ttdeci">static TensorRawPtrVector GetInputs(const ModelPtr &amp;model, size_t subgraphIndex, size_t operatorIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02667">TfLiteParser.cpp:2667</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#l00025">ITfLiteParser.hpp:25</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#l00163">Logging.hpp:163</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#l00353">Descriptors.hpp:353</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="namespacearmnn_utils_xhtml_a523deabeb7d0a884028b35eebfd1cb6c"><div class="ttname"><a href="namespacearmnn_utils.xhtml#a523deabeb7d0a884028b35eebfd1cb6c">armnnUtils::ProcessConcatInputTensorInfo</a></div><div class="ttdeci">void ProcessConcatInputTensorInfo(armnn::TensorInfo &amp;inputTensorInfo, armnn::OriginsDescriptor &amp;concatDescriptor, const unsigned int &amp;concatAxis, unsigned int inputIndex, unsigned int &amp;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_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div>
<div class="ttc" id="namespacearmnn_tf_parser_xhtml_aa78bf8d20e213dcd13d48072dfa9cd1f"><div class="ttname"><a href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">armnnTfParser::CalcPadding</a></div><div class="ttdeci">void CalcPadding(uint32_t input, uint32_t kernel, uint32_t stride, uint32_t &amp;outPadHead, uint32_t &amp;outPadTail, bool samePadding)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8cpp_source.xhtml#l00421">TfParser.cpp:421</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Convolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00424">Descriptors.hpp:424</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&lt; std::pair&lt; unsigned int, unsigned int &gt; &gt; 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#l00920">Descriptors.hpp:920</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&lt; ITfLiteParser, void(*)(ITfLiteParser *parser)&gt; ITfLiteParserPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_tf_lite_parser_8hpp_source.xhtml#l00023">ITfLiteParser.hpp:23</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a91ba75587a31033088cb4f156e847efb"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a91ba75587a31033088cb4f156e847efb">armnnTfLiteParser::TfLiteParser::GetNetworkInputBindingInfo</a></div><div class="ttdeci">virtual BindingPointInfo GetNetworkInputBindingInfo(size_t subgraphId, const std::string &amp;name) const override</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#l02955">TfLiteParser.cpp:2955</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2020 ARM Limited. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00025">00_introduction.dox:25</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &amp;&amp;...)</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="_cl_layer_tests_8cpp_xhtml_a37f1c3ccc9fc906be85185350dd83d48"><div class="ttname"><a href="_cl_layer_tests_8cpp.xhtml#a37f1c3ccc9fc906be85185350dd83d48">true</a></div><div class="ttdeci">DataLayout::NCHW DataLayout::NCHW DataLayout::NHWC DataLayout::NHWC true</div><div class="ttdef"><b>Definition:</b> <a href="_cl_layer_tests_8cpp_source.xhtml#l00202">ClLayerTests.cpp:202</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#l01111">Descriptors.hpp:1111</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#l01049">Descriptors.hpp:1049</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_xhtml_ae18739116b52dbab31bbf490450beb90"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ae18739116b52dbab31bbf490450beb90">armnnTfLiteParser::TfLiteParser::GetSubgraphCount</a></div><div class="ttdeci">virtual size_t GetSubgraphCount() const override</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#l03018">TfLiteParser.cpp:3018</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#l00202">Types.hpp:202</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::Convolution2dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation along y axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00436">Descriptors.hpp:436</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#l01052">Descriptors.hpp:1052</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#l00096">Types.hpp:96</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&lt; std::is_reference&lt; T &gt;::value, T &gt;::value</a></div><div class="ttdeci">const T &amp; 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&lt; std::pair&lt; unsigned int, unsigned int &gt; &gt; 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#l00804">Descriptors.hpp:804</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#l00488">Descriptors.hpp:488</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#l00657">Descriptors.hpp:657</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="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_aaebfa9a01a0bb8a0935114ff0140cc45"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaebfa9a01a0bb8a0935114ff0140cc45">armnnTfLiteParser::TfLiteParser::OutputShapeOfReshape</a></div><div class="ttdeci">static armnn::TensorInfo OutputShapeOfReshape(const armnn::TensorInfo &amp;inputTensorInfo, const std::vector&lt; int32_t &gt; &amp;targetDimsIn)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l01898">TfLiteParser.cpp:1898</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser_xhtml_a9932449a89a62cfcfd72a4fedbee1ab7"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a9932449a89a62cfcfd72a4fedbee1ab7">armnnTfLiteParser::ITfLiteParser::Create</a></div><div class="ttdeci">static ITfLiteParserPtr Create(const armnn::Optional&lt; TfLiteParserOptions &gt; &amp;options=armnn::EmptyOptional())</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l03054">TfLiteParser.cpp:3054</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#l00361">Descriptors.hpp:361</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#l00171">Types.hpp:171</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 &amp;tensorInfo)=0</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#l00154">TfLiteParser.cpp:154</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#l00168">TypesUtils.hpp:168</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 &amp;newShape)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00090">Tensor.hpp:90</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#l00724">Descriptors.hpp:724</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#l00606">Descriptors.hpp:606</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#l00529">Descriptors.hpp:529</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&lt; unsigned int &gt; 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#l00895">Descriptors.hpp:895</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#l00950">Descriptors.hpp:950</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#l00806">Descriptors.hpp:806</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#l00774">Descriptors.hpp:774</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_abda91b07a94d0f498b76655e03647d9a"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abda91b07a94d0f498b76655e03647d9a">armnnTfLiteParser::TfLiteParser::GetSubgraphInputs</a></div><div class="ttdeci">static TensorIdRawPtrVector GetSubgraphInputs(const ModelPtr &amp;model, size_t subgraphIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02706">TfLiteParser.cpp:2706</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a84ad865bb9b5fa0e4841aa35a14a14d8"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">armnnTfLiteParser::TfLiteParser::GetOutputs</a></div><div class="ttdeci">static TensorRawPtrVector GetOutputs(const ModelPtr &amp;model, size_t subgraphIndex, size_t operatorIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02686">TfLiteParser.cpp:2686</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#l00359">Descriptors.hpp:359</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Convolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00426">Descriptors.hpp:426</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#l00527">Descriptors.hpp:527</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#l00901">Descriptors.hpp:901</a></div></div>
<div class="ttc" id="namespacearmnn_deserializer_xhtml_aa28868b7dc87dc4d957db6c775a591c1"><div class="ttname"><a href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">armnnDeserializer::ToTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo ToTensorInfo(Deserializer::TensorRawPtr tensorPtr)</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8cpp_source.xhtml#l00501">Deserializer.cpp:501</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00430">Descriptors.hpp:430</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&lt; onnx::ModelProto &gt; 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="_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#l00079">TfLiteParser.cpp:79</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_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#l00482">Descriptors.hpp:482</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="_tensor_test_8cpp_xhtml_ad80e179ec400af9d2547f172f3ca05f3"><div class="ttname"><a href="_tensor_test_8cpp.xhtml#ad80e179ec400af9d2547f172f3ca05f3">CheckTensor</a></div><div class="ttdeci">void CheckTensor(const ConstTensor &amp;t)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_test_8cpp_source.xhtml#l00100">TensorTest.cpp:100</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_afba7f99227765786c4ed9cb2acc09739"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afba7f99227765786c4ed9cb2acc09739">armnnTfLiteParser::TfLiteParser::GetSubgraphOutputs</a></div><div class="ttdeci">static TensorIdRawPtrVector GetSubgraphOutputs(const ModelPtr &amp;model, size_t subgraphIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02723">TfLiteParser.cpp:2723</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_deserializer_xhtml_a97b50f22cd99f0e09e6e48d20a35f6b2"><div class="ttname"><a href="namespacearmnn_deserializer.xhtml#a97b50f22cd99f0e09e6e48d20a35f6b2">armnnDeserializer::CheckShape</a></div><div class="ttdeci">bool CheckShape(const armnn::TensorShape &amp;actual, const std::vector&lt; uint32_t &gt; &amp;expected)</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8cpp_source.xhtml#l00167">Deserializer.cpp:167</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00032">Types.hpp:32</a></div></div>
<div class="ttc" id="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#l00535">Descriptors.hpp:535</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#l00351">Descriptors.hpp:351</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#l00486">Descriptors.hpp:486</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#l00478">Descriptors.hpp:478</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#l00307">Descriptors.cpp:307</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#l01059">Descriptors.hpp:1059</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#l00897">Descriptors.hpp:897</a></div></div>
<div class="ttc" id="_parser_flatbuffers_serialize_fixture_8hpp_xhtml_a15c20a0693cd3fc4d85565e2f920d8ef"><div class="ttname"><a href="_parser_flatbuffers_serialize_fixture_8hpp.xhtml#a15c20a0693cd3fc4d85565e2f920d8ef">TensorRawPtr</a></div><div class="ttdeci">armnnSerializer::TensorInfo * TensorRawPtr</div><div class="ttdef"><b>Definition:</b> <a href="_parser_flatbuffers_serialize_fixture_8hpp_source.xhtml#l00025">ParserFlatbuffersSerializeFixture.hpp:25</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&lt; unsigned int &gt; m_BlockShape</div><div class="ttdoc">Block shape values. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00680">Descriptors.hpp:680</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser_xhtml_a29a2a153962a701843d5c8ae953cb032"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a29a2a153962a701843d5c8ae953cb032">armnnTfLiteParser::ITfLiteParser::Destroy</a></div><div class="ttdeci">static void Destroy(ITfLiteParser *parser)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l03059">TfLiteParser.cpp:3059</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#l00037">INetwork.hpp:37</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#l00591">Descriptors.hpp:591</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#l00264">Tensor.cpp:264</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#l00247">Tensor.cpp:247</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_afe6c475f92d02dd1eb12acd746e4736f"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">armnnTfLiteParser::TfLiteParser::GetOutputTensorIds</a></div><div class="ttdeci">static std::vector&lt; int32_t &gt; &amp; GetOutputTensorIds(const ModelPtr &amp;model, size_t subgraphIndex, size_t operatorIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02749">TfLiteParser.cpp:2749</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#l00095">Tensor.hpp:95</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#l00147">Descriptors.hpp:147</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#l00373">Descriptors.hpp:373</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#l01056">Descriptors.hpp:1056</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#l00386">Descriptors.hpp:386</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#l00199">Tensor.hpp:199</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser_xhtml_acf8cc929eadbabf197b36f7364d3d2cb"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#acf8cc929eadbabf197b36f7364d3d2cb">armnnTfLiteParser::ITfLiteParser::CreateRaw</a></div><div class="ttdeci">static ITfLiteParser * CreateRaw(const armnn::Optional&lt; TfLiteParserOptions &gt; &amp;options=armnn::EmptyOptional())</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l03049">TfLiteParser.cpp:3049</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="_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="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#l00537">Descriptors.hpp:537</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="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#l01109">Descriptors.hpp:1109</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#l00980">Descriptors.hpp:980</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a37fa39012e90d568df7f774cd6d1e956"><div class="ttname"><a href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t&lt; std::is_unsigned&lt; Source &gt;::value &amp;&amp;std::is_unsigned&lt; Dest &gt;::value, Dest &gt; numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00033">NumericCast.hpp:33</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#l00539">Descriptors.hpp:539</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#l01119">Descriptors.hpp:1119</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&lt; unsigned int &gt; m_BlockShape</div><div class="ttdoc">Block shape value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00801">Descriptors.hpp:801</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&lt; int &gt; 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#l01045">Descriptors.hpp:1045</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a58bfb9626d373249745d78b95543116e"><div class="ttname"><a href="namespacearmnn.xhtml#a58bfb9626d373249745d78b95543116e">armnn::IsActivationSupported</a></div><div class="ttdeci">bool IsActivationSupported(const BackendId &amp;backend, const TensorInfo &amp;input, const TensorInfo &amp;output, const ActivationDescriptor &amp;descriptor, char *reasonIfUnsupported=nullptr, size_t reasonIfUnsupportedMaxLength=1024)</div><div class="ttdoc">Deprecated in favor of IBackend and ILayerSupport interfaces. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_support_8cpp_source.xhtml#l00069">LayerSupport.cpp:69</a></div></div>
<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a></div><div class="ttdoc">An ActivationDescriptor for the ActivationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00020">Descriptors.hpp:20</a></div></div>
<div class="ttc" id="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#l00192">Exceptions.hpp:192</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#l00974">Descriptors.hpp:974</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#l00927">Descriptors.hpp:927</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#l00173">Types.hpp:173</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00432">Descriptors.hpp:432</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a8b4b0b88a5e79a88b8b60db76398f575"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">armnnTfLiteParser::TfLiteParser::GetInputTensorIds</a></div><div class="ttdeci">static std::vector&lt; int32_t &gt; &amp; GetInputTensorIds(const ModelPtr &amp;model, size_t subgraphIndex, size_t operatorIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02739">TfLiteParser.cpp:2739</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#l00369">Descriptors.hpp:369</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#l00172">TfLiteParser.cpp:172</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_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#l00547">Descriptors.hpp:547</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&lt; int &gt; 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#l01043">Descriptors.hpp:1043</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#l00778">Descriptors.hpp:778</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). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00037">Descriptors.hpp:37</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::Convolution2dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation along x axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00434">Descriptors.hpp:434</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#l01105">Descriptors.hpp:1105</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="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_aa0a90d432c9c41f9846f41f11c9e54c9"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aa0a90d432c9c41f9846f41f11c9e54c9">armnnTfLiteParser::TfLiteParser::OutputShapeOfSqueeze</a></div><div class="ttdeci">static armnn::TensorInfo OutputShapeOfSqueeze(const std::vector&lt; uint32_t &gt; &amp;squeezeDims, const armnn::TensorInfo &amp;inputTensorInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l01463">TfLiteParser.cpp:1463</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#l01113">Descriptors.hpp:1113</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#l00259">TfLiteParser.cpp:259</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#l00347">Descriptors.hpp:347</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#l00484">Descriptors.hpp:484</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#l01115">Descriptors.hpp:1115</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&lt; std::pair&lt; unsigned int, unsigned int &gt; &gt; 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#l00682">Descriptors.hpp:682</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="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="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00043">Tensor.hpp:43</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#l00365">Descriptors.hpp:365</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#l00150">Descriptors.cpp:150</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_aaafbda6e6816876b3d7963cfe64dd2f8"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">armnnTfLiteParser::TfLiteParser::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#l00033">TfLiteParser.hpp:33</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 &amp; 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#l00103">Types.hpp:103</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#l00877">Descriptors.hpp:877</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a58501692772880e7ef55485a4c95aab9"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a58501692772880e7ef55485a4c95aab9">armnnTfLiteParser::TfLiteParser::GetSubgraphInputTensorNames</a></div><div class="ttdeci">virtual std::vector&lt; std::string &gt; GetSubgraphInputTensorNames(size_t subgraphId) const override</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#l03023">TfLiteParser.cpp:3023</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="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#l01107">Descriptors.hpp:1107</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#l01123">Descriptors.hpp:1123</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#l01002">Descriptors.hpp:1002</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot &amp; GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index. </div></div>
<div class="ttc" id="classarmnn_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="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_ab97e69d07f06e392bd0cc2e5bcbf1be6"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ab97e69d07f06e392bd0cc2e5bcbf1be6">armnnTfLiteParser::TfLiteParser::GetSubgraphOutputTensorNames</a></div><div class="ttdeci">virtual std::vector&lt; std::string &gt; GetSubgraphOutputTensorNames(size_t subgraphId) const override</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#l03036">TfLiteParser.cpp:3036</a></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="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#l00543">Descriptors.hpp:543</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#l00533">Descriptors.hpp:533</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&lt; INetwork, void(*)(INetwork *network)&gt; INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00101">INetwork.hpp:101</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &amp;destination)=0</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="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#l00313">Descriptors.hpp:313</a></div></div>
<div class="ttc" id="_file_only_profiling_decorator_tests_8cpp_xhtml_a6560146509197f3e197d8d36f76c1347"><div class="ttname"><a href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a></div><div class="ttdeci">armnn::Runtime::CreationOptions::ExternalProfilingOptions options</div><div class="ttdef"><b>Definition:</b> <a href="_file_only_profiling_decorator_tests_8cpp_source.xhtml#l00045">FileOnlyProfilingDecoratorTests.cpp:45</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#l00495">Descriptors.hpp:495</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_abfc86471394295357a23b3addd0b5b1c"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">armnnTfLiteParser::TfLiteParser::ModelPtr</a></div><div class="ttdeci">std::unique_ptr&lt; tflite::ModelT &gt; ModelPtr</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00023">TfLiteParser.hpp:23</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#l00092">Tensor.hpp:92</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#l00238">TfLiteParser.cpp:238</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#l00039">Descriptors.hpp:39</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 &amp;srcShape, const armnn::PermutationVector &amp;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#l00123">Descriptors.hpp:123</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_ac3486e6c1a291aa67efd8b280ffb83cc"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">armnnTfLiteParser::TfLiteParser::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#l00028">TfLiteParser.hpp:28</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#l00302">Descriptors.cpp:302</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). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00035">Descriptors.hpp:35</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_aadad81a95152fe5aad839db352d4012c"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aadad81a95152fe5aad839db352d4012c">armnnTfLiteParser::TfLiteParser::OperatorPtr</a></div><div class="ttdeci">std::unique_ptr&lt; tflite::OperatorT &gt; OperatorPtr</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00025">TfLiteParser.hpp:25</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#l00024">INetwork.hpp:24</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a2ed4853234865d838da50085da99b2a6"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a2ed4853234865d838da50085da99b2a6">armnnTfLiteParser::TfLiteParser::TfLiteParser</a></div><div class="ttdeci">TfLiteParser(const armnn::Optional&lt; ITfLiteParser::TfLiteParserOptions &gt; &amp;options=armnn::EmptyOptional())</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00488">TfLiteParser.cpp:488</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#l00363">Descriptors.hpp:363</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#l00444">Descriptors.hpp:444</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#l00018">Types.hpp:18</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Convolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00422">Descriptors.hpp:422</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#l00093">Tensor.hpp:93</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_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#l00055">Types.hpp:55</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a86428e0c674542488c7292dfbe2ce381"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a86428e0c674542488c7292dfbe2ce381">armnnTfLiteParser::TfLiteParser::TensorIdRawPtrVector</a></div><div class="ttdeci">std::vector&lt; TensorIdRawPtr &gt; TensorIdRawPtrVector</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_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#l00476">Descriptors.hpp:476</a></div></div>
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