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<div class="title">NetworkExecutionUtils.hpp</div> </div>
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<a href="_network_execution_utils_8hpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="preprocessor">#include <<a class="code" href="_arm_n_n_8hpp.xhtml">armnn/ArmNN.hpp</a>></span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include <<a class="code" href="_types_utils_8hpp.xhtml">armnn/TypesUtils.hpp</a>></span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#if defined(ARMNN_SERIALIZER)</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include "<a class="code" href="_i_deserializer_8hpp.xhtml">armnnDeserializer/IDeserializer.hpp</a>"</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#if defined(ARMNN_CAFFE_PARSER)</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include "<a class="code" href="_i_caffe_parser_8hpp.xhtml">armnnCaffeParser/ICaffeParser.hpp</a>"</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#if defined(ARMNN_TF_PARSER)</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include "<a class="code" href="_i_tf_parser_8hpp.xhtml">armnnTfParser/ITfParser.hpp</a>"</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#if defined(ARMNN_TF_LITE_PARSER)</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include "<a class="code" href="_i_tf_lite_parser_8hpp.xhtml">armnnTfLiteParser/ITfLiteParser.hpp</a>"</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#if defined(ARMNN_ONNX_PARSER)</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include "<a class="code" href="_i_onnx_parser_8hpp.xhtml">armnnOnnxParser/IOnnxParser.hpp</a>"</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="preprocessor">#include "<a class="code" href="_csv_reader_8hpp.xhtml">CsvReader.hpp</a>"</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "../InferenceTest.hpp"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include <<a class="code" href="_profiling_8hpp.xhtml">Profiling.hpp</a>></span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include <<a class="code" href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a>></span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> </div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include <boost/algorithm/string/trim.hpp></span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include <boost/algorithm/string/split.hpp></span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include <boost/algorithm/string/classification.hpp></span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include <boost/program_options.hpp></span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include <boost/variant.hpp></span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include <iostream></span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor">#include <fstream></span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#include <functional></span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="preprocessor">#include <future></span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="preprocessor">#include <algorithm></span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="preprocessor">#include <iterator></span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> </div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> {</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> </div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="comment">// Configure boost::program_options for command-line parsing and validation.</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="keyword">namespace </span>po = boost::program_options;</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="keyword">template</span><<span class="keyword">typename</span> T, <span class="keyword">typename</span> TParseElementFunc></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> std::vector<T> ParseArrayImpl(std::istream& stream, TParseElementFunc parseElementFunc, <span class="keyword">const</span> <span class="keywordtype">char</span> * chars = <span class="stringliteral">"\t ,:"</span>)</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> {</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  std::vector<T> result;</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="comment">// Processes line-by-line.</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  std::string line;</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordflow">while</span> (std::getline(stream, line))</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  {</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  std::vector<std::string> tokens;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  {</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="comment">// Coverity fix: boost::split() may throw an exception of type boost::bad_function_call.</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  boost::split(tokens, line, boost::algorithm::is_any_of(chars), boost::token_compress_on);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  }</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> std::exception& e)</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  {</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(error) << <span class="stringliteral">"An error occurred when splitting tokens: "</span> << e.what();</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordflow">continue</span>;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  }</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> std::string& token : tokens)</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  {</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keywordflow">if</span> (!token.empty()) <span class="comment">// See https://stackoverflow.com/questions/10437406/</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  {</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  {</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  result.push_back(parseElementFunc(token));</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  }</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> std::exception&)</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  {</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(error) << <span class="stringliteral">"'"</span> << token << <span class="stringliteral">"' is not a valid number. It has been ignored."</span>;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  }</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  }</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  }</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  }</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> </div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> }</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> <span class="keywordtype">bool</span> CheckOption(<span class="keyword">const</span> po::variables_map& vm,</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* option)</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> {</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="comment">// Check that the given option is valid.</span></div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keywordflow">if</span> (option == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  {</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  }</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="comment">// Check whether 'option' is provided.</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordflow">return</span> vm.find(option) != vm.end();</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> }</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> </div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> <span class="keywordtype">void</span> CheckOptionDependency(<span class="keyword">const</span> po::variables_map& vm,</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* option,</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* required)</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> {</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="comment">// Check that the given options are valid.</span></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keywordflow">if</span> (option == <span class="keyword">nullptr</span> || required == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  {</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keywordflow">throw</span> po::error(<span class="stringliteral">"Invalid option to check dependency for"</span>);</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  }</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> </div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="comment">// Check that if 'option' is provided, 'required' is also provided.</span></div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keywordflow">if</span> (CheckOption(vm, option) && !vm[option].defaulted())</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  {</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keywordflow">if</span> (CheckOption(vm, required) == 0 || vm[required].defaulted())</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  {</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keywordflow">throw</span> po::error(std::string(<span class="stringliteral">"Option '"</span>) + option + <span class="stringliteral">"' requires option '"</span> + required + <span class="stringliteral">"'."</span>);</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  }</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  }</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> }</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> <span class="keywordtype">void</span> CheckOptionDependencies(<span class="keyword">const</span> po::variables_map& vm)</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> {</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  CheckOptionDependency(vm, <span class="stringliteral">"model-path"</span>, <span class="stringliteral">"model-format"</span>);</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  CheckOptionDependency(vm, <span class="stringliteral">"model-path"</span>, <span class="stringliteral">"input-name"</span>);</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  CheckOptionDependency(vm, <span class="stringliteral">"model-path"</span>, <span class="stringliteral">"output-name"</span>);</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  CheckOptionDependency(vm, <span class="stringliteral">"input-tensor-shape"</span>, <span class="stringliteral">"model-path"</span>);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> }</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> </div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> <span class="keyword">template</span><armnn::DataType NonQuantizedType></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> <span class="keyword">auto</span> ParseDataArray(std::istream & stream);</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> </div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> <span class="keyword">template</span><armnn::DataType QuantizedType></div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> <span class="keyword">auto</span> ParseDataArray(std::istream& stream,</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keyword">const</span> <span class="keywordtype">float</span>& quantizationScale,</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keyword">const</span> int32_t& quantizationOffset);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> </div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> <span class="keyword">template</span><></div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> <span class="keyword">auto</span> ParseDataArray<armnn::DataType::Float32>(std::istream & stream)</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> {</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keywordflow">return</span> ParseArrayImpl<float>(stream, [](<span class="keyword">const</span> std::string& s) { <span class="keywordflow">return</span> std::stof(s); });</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> }</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> <span class="keyword">template</span><></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> <span class="keyword">auto</span> ParseDataArray<armnn::DataType::Signed32>(std::istream & stream)</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> {</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keywordflow">return</span> ParseArrayImpl<int>(stream, [](<span class="keyword">const</span> std::string & s) { <span class="keywordflow">return</span> std::stoi(s); });</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> }</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> </div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> <span class="keyword">template</span><></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="keyword">auto</span> ParseDataArray<armnn::DataType::QAsymmU8>(std::istream& stream)</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> {</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keywordflow">return</span> ParseArrayImpl<uint8_t>(stream,</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  [](<span class="keyword">const</span> std::string& s) { <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><uint8_t>(std::stoi(s)); });</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> }</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> </div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> <span class="keyword">template</span><></div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> <span class="keyword">auto</span> ParseDataArray<armnn::DataType::QAsymmU8>(std::istream& stream,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keyword">const</span> <span class="keywordtype">float</span>& quantizationScale,</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keyword">const</span> int32_t& quantizationOffset)</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> {</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keywordflow">return</span> ParseArrayImpl<uint8_t>(stream,</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  [&quantizationScale, &quantizationOffset](<span class="keyword">const</span> std::string & s)</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  {</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><uint8_t>(</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  armnn::Quantize<uint8_t>(std::stof(s),</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  quantizationScale,</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  quantizationOffset));</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  });</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> }</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> std::vector<unsigned int> ParseArray(std::istream& stream)</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> {</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordflow">return</span> ParseArrayImpl<unsigned int>(stream,</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  [](<span class="keyword">const</span> std::string& s) { <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(std::stoi(s)); });</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> }</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> </div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> std::vector<std::string> ParseStringList(<span class="keyword">const</span> std::string & inputString, <span class="keyword">const</span> <span class="keywordtype">char</span> * delimiter)</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> {</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  std::stringstream stream(inputString);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keywordflow">return</span> ParseArrayImpl<std::string>(stream, [](<span class="keyword">const</span> std::string& s) { <span class="keywordflow">return</span> boost::trim_copy(s); }, delimiter);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> }</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> </div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> <span class="keywordtype">void</span> RemoveDuplicateDevices(std::vector<armnn::BackendId>& computeDevices)</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> {</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="comment">// Mark the duplicate devices as 'Undefined'.</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = computeDevices.begin(); i != computeDevices.end(); ++i)</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  {</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> j = std::next(i); j != computeDevices.end(); ++j)</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  {</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keywordflow">if</span> (*j == *i)</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  {</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  *j = <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">armnn::Compute::Undefined</a>;</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  }</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  }</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  }</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="comment">// Remove 'Undefined' devices.</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  computeDevices.erase(std::remove(computeDevices.begin(), computeDevices.end(), <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">armnn::Compute::Undefined</a>),</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  computeDevices.end());</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> }</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> </div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> <span class="keyword">struct </span>TensorPrinter : <span class="keyword">public</span> boost::static_visitor<></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> {</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  TensorPrinter(<span class="keyword">const</span> std::string& binding,</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& info,</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keyword">const</span> std::string& outputTensorFile,</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keywordtype">bool</span> dequantizeOutput)</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  : m_OutputBinding(binding)</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  , m_Scale(info.GetQuantizationScale())</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  , m_Offset(info.GetQuantizationOffset())</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  , m_OutputTensorFile(outputTensorFile)</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  , m_DequantizeOutput(dequantizeOutput)</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  {}</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> </div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="keywordtype">void</span> operator()(<span class="keyword">const</span> std::vector<float>& values)</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  {</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  ForEachValue(values, [](<span class="keywordtype">float</span> value)</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  {</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  printf(<span class="stringliteral">"%f "</span>, value);</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  });</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  WriteToFile(values);</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  }</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> </div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="keywordtype">void</span> operator()(<span class="keyword">const</span> std::vector<uint8_t>& values)</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  {</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keywordflow">if</span>(m_DequantizeOutput)</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  {</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keyword">auto</span>& scale = m_Scale;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keyword">auto</span>& offset = m_Offset;</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  std::vector<float> dequantizedValues;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  ForEachValue(values, [&scale, &offset, &dequantizedValues](uint8_t value)</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  {</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keyword">auto</span> dequantizedValue = <a class="code" href="namespacearmnn.xhtml#a855293b1be0581fb61ef6a1c5b027d0f">armnn::Dequantize</a>(value, scale, offset);</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  printf(<span class="stringliteral">"%f "</span>, dequantizedValue);</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  dequantizedValues.push_back(dequantizedValue);</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  });</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  WriteToFile(dequantizedValues);</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  }</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  {</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keyword">const</span> std::vector<int> intValues(values.begin(), values.end());</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  operator()(intValues);</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  }</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  }</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span> </div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="keywordtype">void</span> operator()(<span class="keyword">const</span> std::vector<int>& values)</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  {</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  ForEachValue(values, [](<span class="keywordtype">int</span> value)</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  {</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  printf(<span class="stringliteral">"%d "</span>, value);</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  });</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  WriteToFile(values);</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  }</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> </div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keyword">template</span><<span class="keyword">typename</span> Container, <span class="keyword">typename</span> Delegate></div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keywordtype">void</span> ForEachValue(<span class="keyword">const</span> Container& c, Delegate delegate)</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  {</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  std::cout << m_OutputBinding << <span class="stringliteral">": "</span>;</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& value : c)</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  {</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  delegate(value);</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  }</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  printf(<span class="stringliteral">"\n"</span>);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  }</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> </div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keyword">template</span><<span class="keyword">typename</span> T></div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="keywordtype">void</span> WriteToFile(<span class="keyword">const</span> std::vector<T>& values)</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  {</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keywordflow">if</span> (!m_OutputTensorFile.empty())</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  {</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  std::ofstream outputTensorFile;</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  outputTensorFile.open(m_OutputTensorFile, std::ofstream::out | std::ofstream::trunc);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keywordflow">if</span> (outputTensorFile.is_open())</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  {</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  outputTensorFile << m_OutputBinding << <span class="stringliteral">": "</span>;</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  std::copy(values.begin(), values.end(), std::ostream_iterator<T>(outputTensorFile, <span class="stringliteral">" "</span>));</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  }</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  {</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(info) << <span class="stringliteral">"Output Tensor File: "</span> << m_OutputTensorFile << <span class="stringliteral">" could not be opened!"</span>;</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  }</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  outputTensorFile.close();</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  }</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  }</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> </div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  std::string m_OutputBinding;</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="keywordtype">float</span> m_Scale=0.0f;</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="keywordtype">int</span> m_Offset=0;</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  std::string m_OutputTensorFile;</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keywordtype">bool</span> m_DequantizeOutput = <span class="keyword">false</span>;</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> };</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> </div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span> </div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span> </div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span> std::vector<T> GenerateDummyTensorData(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numElements)</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span> {</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <span class="keywordflow">return</span> std::vector<T>(numElements, <span class="keyword">static_cast<</span>T<span class="keyword">></span>(0));</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span> }</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span> </div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span> <span class="keyword">using</span> <a class="code" href="_model_accuracy_checker_test_8cpp.xhtml#a9eb69ebdaf4ceb8014e7c8a540266100">TContainer</a> = boost::variant<std::vector<float>, std::vector<int>, std::vector<unsigned char>>;</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span> <span class="keyword">using</span> <a class="code" href="namespace_inference_model_internal.xhtml#a6e713a319588c57fc854bc478f5ee13a">QuantizationParams</a> = std::pair<float, int32_t>;</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> </div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span> <span class="keywordtype">void</span> PopulateTensorWithData(<a class="code" href="_model_accuracy_checker_test_8cpp.xhtml#a9eb69ebdaf4ceb8014e7c8a540266100">TContainer</a>& tensorData,</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numElements,</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keyword">const</span> std::string& dataTypeStr,</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<QuantizationParams></a>& qParams,</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<std::string></a>& dataFile)</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span> {</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> readFromFile = dataFile.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() && !dataFile.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>().empty();</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> quantizeData = qParams.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>();</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span> </div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  std::ifstream inputTensorFile;</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keywordflow">if</span> (readFromFile)</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  {</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  inputTensorFile = std::ifstream(dataFile.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>());</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  }</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span> </div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keywordflow">if</span> (dataTypeStr.compare(<span class="stringliteral">"float"</span>) == 0)</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  {</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="keywordflow">if</span> (quantizeData)</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  {</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> qScale = qParams.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>().first;</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> qOffset = qParams.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>().second;</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> </div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  tensorData = readFromFile ?</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  ParseDataArray<armnn::DataType::QAsymmU8>(inputTensorFile, qScale, qOffset) :</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  GenerateDummyTensorData<armnn::DataType::QAsymmU8>(numElements);</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  }</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  {</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  tensorData = readFromFile ?</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  ParseDataArray<armnn::DataType::Float32>(inputTensorFile) :</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  GenerateDummyTensorData<armnn::DataType::Float32>(numElements);</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  }</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  }</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (dataTypeStr.compare(<span class="stringliteral">"int"</span>) == 0)</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  {</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  tensorData = readFromFile ?</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  ParseDataArray<armnn::DataType::Signed32>(inputTensorFile) :</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  GenerateDummyTensorData<armnn::DataType::Signed32>(numElements);</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  }</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (dataTypeStr.compare(<span class="stringliteral">"qasymm8"</span>) == 0)</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  {</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  tensorData = readFromFile ?</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  ParseDataArray<armnn::DataType::QAsymmU8>(inputTensorFile) :</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  GenerateDummyTensorData<armnn::DataType::QAsymmU8>(numElements);</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  }</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  {</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  std::string errorMessage = <span class="stringliteral">"Unsupported tensor data type "</span> + dataTypeStr;</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << errorMessage;</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span> </div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  inputTensorFile.close();</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(errorMessage);</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  }</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span> </div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  inputTensorFile.close();</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> }</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span> </div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span> } <span class="comment">// anonymous namespace</span></div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span> </div><div class="line"><a name="l00361"></a><span class="lineno"><a class="line" href="_network_execution_utils_8hpp.xhtml#ad7abdfb6c0cc99eb356c1eefdc6ff696"> 361</a></span> <span class="keywordtype">bool</span> <a class="code" href="_network_execution_utils_8hpp.xhtml#ad7abdfb6c0cc99eb356c1eefdc6ff696">generateTensorData</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span> </div><div class="line"><a name="l00363"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml"> 363</a></span> <span class="keyword">struct </span><a class="code" href="struct_execute_network_params.xhtml">ExecuteNetworkParams</a></div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> {</div><div class="line"><a name="l00365"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a29ca0d7f3ce1cb59535aa4d09f7d1b37"> 365</a></span>  <span class="keyword">using</span> <a class="code" href="struct_execute_network_params.xhtml#a29ca0d7f3ce1cb59535aa4d09f7d1b37">TensorShapePtr</a> = std::unique_ptr<armnn::TensorShape>;</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span> </div><div class="line"><a name="l00367"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a44c7d128c41b2717fe425cf6fdc32936"> 367</a></span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="struct_execute_network_params.xhtml#a44c7d128c41b2717fe425cf6fdc32936">m_ModelPath</a>;</div><div class="line"><a name="l00368"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a6bf2f586c403977d31c7d32d371918cf"> 368</a></span>  <span class="keywordtype">bool</span> <a class="code" href="struct_execute_network_params.xhtml#a6bf2f586c403977d31c7d32d371918cf">m_IsModelBinary</a>;</div><div class="line"><a name="l00369"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a2d54e6252c1c9a0e29f7706ba03b2b74"> 369</a></span>  std::vector<armnn::BackendId> <a class="code" href="struct_execute_network_params.xhtml#a2d54e6252c1c9a0e29f7706ba03b2b74">m_ComputeDevices</a>;</div><div class="line"><a name="l00370"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#ae43cf4b5df0068ee6a9151c98947248b"> 370</a></span>  std::string <a class="code" href="struct_execute_network_params.xhtml#ae43cf4b5df0068ee6a9151c98947248b">m_DynamicBackendsPath</a>;</div><div class="line"><a name="l00371"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a155de45be27135e4c9b6b7df277d0b8f"> 371</a></span>  std::vector<string> <a class="code" href="struct_execute_network_params.xhtml#a155de45be27135e4c9b6b7df277d0b8f">m_InputNames</a>;</div><div class="line"><a name="l00372"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a091cda9098c6f03f91f477a22327892d"> 372</a></span>  std::vector<TensorShapePtr> <a class="code" href="struct_execute_network_params.xhtml#a091cda9098c6f03f91f477a22327892d">m_InputTensorShapes</a>;</div><div class="line"><a name="l00373"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a924e9ee22f0bf39f05e0c1b45e5c637b"> 373</a></span>  std::vector<string> <a class="code" href="struct_execute_network_params.xhtml#a924e9ee22f0bf39f05e0c1b45e5c637b">m_InputTensorDataFilePaths</a>;</div><div class="line"><a name="l00374"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a08c2e205fcf14f0caa44388f8314e7b5"> 374</a></span>  std::vector<string> <a class="code" href="struct_execute_network_params.xhtml#a08c2e205fcf14f0caa44388f8314e7b5">m_InputTypes</a>;</div><div class="line"><a name="l00375"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a077f6963fc555d9d42f98cf9ed3e7e03"> 375</a></span>  <span class="keywordtype">bool</span> <a class="code" href="struct_execute_network_params.xhtml#a077f6963fc555d9d42f98cf9ed3e7e03">m_QuantizeInput</a>;</div><div class="line"><a name="l00376"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a71c32d9b0334a1561bb6d2db677755d6"> 376</a></span>  std::vector<string> <a class="code" href="struct_execute_network_params.xhtml#a71c32d9b0334a1561bb6d2db677755d6">m_OutputTypes</a>;</div><div class="line"><a name="l00377"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a1a08c6c34dd3ce290b4bc62a715bb810"> 377</a></span>  std::vector<string> <a class="code" href="struct_execute_network_params.xhtml#a1a08c6c34dd3ce290b4bc62a715bb810">m_OutputNames</a>;</div><div class="line"><a name="l00378"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a64c0a99d553c42d215c4b2f1a2f1c7d4"> 378</a></span>  std::vector<string> <a class="code" href="struct_execute_network_params.xhtml#a64c0a99d553c42d215c4b2f1a2f1c7d4">m_OutputTensorFiles</a>;</div><div class="line"><a name="l00379"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a85929a48c5e7b16af8f5bc637e45a37f"> 379</a></span>  <span class="keywordtype">bool</span> <a class="code" href="struct_execute_network_params.xhtml#a85929a48c5e7b16af8f5bc637e45a37f">m_DequantizeOutput</a>;</div><div class="line"><a name="l00380"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a26d42007440bb01a1a6d0ab3b5a657ee"> 380</a></span>  <span class="keywordtype">bool</span> <a class="code" href="struct_execute_network_params.xhtml#a26d42007440bb01a1a6d0ab3b5a657ee">m_EnableProfiling</a>;</div><div class="line"><a name="l00381"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a513151623e8d448951a0b94ad1946fbe"> 381</a></span>  <span class="keywordtype">bool</span> <a class="code" href="struct_execute_network_params.xhtml#a513151623e8d448951a0b94ad1946fbe">m_EnableFp16TurboMode</a>;</div><div class="line"><a name="l00382"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a318999172ae5197f56326b12d29104b7"> 382</a></span>  <span class="keywordtype">double</span> <a class="code" href="struct_execute_network_params.xhtml#a318999172ae5197f56326b12d29104b7">m_ThresholdTime</a>;</div><div class="line"><a name="l00383"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a5acc5b4076604db15ee13ee19fa623c4"> 383</a></span>  <span class="keywordtype">bool</span> <a class="code" href="struct_execute_network_params.xhtml#a5acc5b4076604db15ee13ee19fa623c4">m_PrintIntermediate</a>;</div><div class="line"><a name="l00384"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a2d4582aa74998c397bd064ae73745b62"> 384</a></span>  <span class="keywordtype">size_t</span> <a class="code" href="struct_execute_network_params.xhtml#a2d4582aa74998c397bd064ae73745b62">m_SubgraphId</a>;</div><div class="line"><a name="l00385"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a7efc68309e76bfefbfa16fe94501b060"> 385</a></span>  <span class="keywordtype">bool</span> m_EnableLayerDetails = <span class="keyword">false</span>;</div><div class="line"><a name="l00386"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a99c7360a4d4b248b3f10607bc5d2fe7b"> 386</a></span>  <span class="keywordtype">bool</span> <a class="code" href="struct_execute_network_params.xhtml#a99c7360a4d4b248b3f10607bc5d2fe7b">m_GenerateTensorData</a>;</div><div class="line"><a name="l00387"></a><span class="lineno"><a class="line" href="struct_execute_network_params.xhtml#a5c7f0c083da98e7b6e9ba79d2fcd985d"> 387</a></span>  <span class="keywordtype">bool</span> m_ParseUnsupported = <span class="keyword">false</span>;</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> };</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> </div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> <span class="keyword">template</span><<span class="keyword">typename</span> TParser, <span class="keyword">typename</span> TDataType></div><div class="line"><a name="l00391"></a><span class="lineno"><a class="line" href="_network_execution_utils_8hpp.xhtml#afc1c3398fd2de1051edf23a171cfa01b"> 391</a></span> <span class="keywordtype">int</span> <a class="code" href="_network_execution_utils_8hpp.xhtml#afc1c3398fd2de1051edf23a171cfa01b">MainImpl</a>(<span class="keyword">const</span> <a class="code" href="struct_execute_network_params.xhtml">ExecuteNetworkParams</a>& params,</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keyword">const</span> std::shared_ptr<armnn::IRuntime>& runtime = <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span> {</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keyword">using</span> <a class="code" href="_model_accuracy_checker_test_8cpp.xhtml#a9eb69ebdaf4ceb8014e7c8a540266100">TContainer</a> = boost::variant<std::vector<float>, std::vector<int>, std::vector<unsigned char>>;</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span> </div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  std::vector<TContainer> inputDataContainers;</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span> </div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  {</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="comment">// Creates an InferenceModel, which will parse the model and load it into an IRuntime.</span></div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="keyword">typename</span> <a class="code" href="struct_inference_model_internal_1_1_params.xhtml">InferenceModel<TParser, TDataType>::Params</a> inferenceModelParams;</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  inferenceModelParams.<a class="code" href="struct_inference_model_internal_1_1_params.xhtml#a4fa312cf0d60fbd3988a7c76ab8e2980">m_ModelPath</a> = params.<a class="code" href="struct_execute_network_params.xhtml#a44c7d128c41b2717fe425cf6fdc32936">m_ModelPath</a>;</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  inferenceModelParams.<a class="code" href="struct_inference_model_internal_1_1_params.xhtml#a6bf2f586c403977d31c7d32d371918cf">m_IsModelBinary</a> = params.<a class="code" href="struct_execute_network_params.xhtml#a6bf2f586c403977d31c7d32d371918cf">m_IsModelBinary</a>;</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  inferenceModelParams.<a class="code" href="struct_inference_model_internal_1_1_params.xhtml#a2d54e6252c1c9a0e29f7706ba03b2b74">m_ComputeDevices</a> = params.<a class="code" href="struct_execute_network_params.xhtml#a2d54e6252c1c9a0e29f7706ba03b2b74">m_ComputeDevices</a>;</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  inferenceModelParams.<a class="code" href="struct_inference_model_internal_1_1_params.xhtml#ae43cf4b5df0068ee6a9151c98947248b">m_DynamicBackendsPath</a> = params.<a class="code" href="struct_execute_network_params.xhtml#ae43cf4b5df0068ee6a9151c98947248b">m_DynamicBackendsPath</a>;</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  inferenceModelParams.<a class="code" href="struct_inference_model_internal_1_1_params.xhtml#acde2af8cbbd224a9f94e509ca538a775">m_PrintIntermediateLayers</a> = params.<a class="code" href="struct_execute_network_params.xhtml#a5acc5b4076604db15ee13ee19fa623c4">m_PrintIntermediate</a>;</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  inferenceModelParams.<a class="code" href="struct_inference_model_internal_1_1_params.xhtml#aaae50a6c0f73e4c210c2e4331c439482">m_VisualizePostOptimizationModel</a> = params.<a class="code" href="struct_execute_network_params.xhtml#a7efc68309e76bfefbfa16fe94501b060">m_EnableLayerDetails</a>;</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  inferenceModelParams.<a class="code" href="struct_inference_model_internal_1_1_params.xhtml#a5c7f0c083da98e7b6e9ba79d2fcd985d">m_ParseUnsupported</a> = params.<a class="code" href="struct_execute_network_params.xhtml#a5c7f0c083da98e7b6e9ba79d2fcd985d">m_ParseUnsupported</a>;</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span> </div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="keywordflow">for</span>(<span class="keyword">const</span> std::string& inputName: params.<a class="code" href="struct_execute_network_params.xhtml#a155de45be27135e4c9b6b7df277d0b8f">m_InputNames</a>)</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  {</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  inferenceModelParams.<a class="code" href="struct_inference_model_internal_1_1_params.xhtml#aad2ac35d4cb83ee4da9fad5fbcb907e0">m_InputBindings</a>.push_back(inputName);</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  }</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span> </div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < params.<a class="code" href="struct_execute_network_params.xhtml#a091cda9098c6f03f91f477a22327892d">m_InputTensorShapes</a>.size(); ++i)</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  {</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  inferenceModelParams.<a class="code" href="struct_inference_model_internal_1_1_params.xhtml#ad69aa6b4967ce55ee4a915c52c71bf2e">m_InputShapes</a>.push_back(*params.<a class="code" href="struct_execute_network_params.xhtml#a091cda9098c6f03f91f477a22327892d">m_InputTensorShapes</a>[i]);</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  }</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> </div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keywordflow">for</span>(<span class="keyword">const</span> std::string& outputName: params.<a class="code" href="struct_execute_network_params.xhtml#a1a08c6c34dd3ce290b4bc62a715bb810">m_OutputNames</a>)</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  {</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  inferenceModelParams.<a class="code" href="struct_inference_model_internal_1_1_params.xhtml#abeacb4ed1ca9256ee0e8aea73185a0cc">m_OutputBindings</a>.push_back(outputName);</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  }</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span> </div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  inferenceModelParams.<a class="code" href="struct_inference_model_internal_1_1_params.xhtml#a2d4582aa74998c397bd064ae73745b62">m_SubgraphId</a> = params.<a class="code" href="struct_execute_network_params.xhtml#a2d4582aa74998c397bd064ae73745b62">m_SubgraphId</a>;</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  inferenceModelParams.<a class="code" href="struct_inference_model_internal_1_1_params.xhtml#a513151623e8d448951a0b94ad1946fbe">m_EnableFp16TurboMode</a> = params.<a class="code" href="struct_execute_network_params.xhtml#a513151623e8d448951a0b94ad1946fbe">m_EnableFp16TurboMode</a>;</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span> </div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <a class="code" href="class_inference_model.xhtml">InferenceModel<TParser, TDataType></a> model(inferenceModelParams,</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a26d42007440bb01a1a6d0ab3b5a657ee">m_EnableProfiling</a>,</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  params.<a class="code" href="struct_execute_network_params.xhtml#ae43cf4b5df0068ee6a9151c98947248b">m_DynamicBackendsPath</a>,</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  runtime);</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span> </div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> numInputs = inferenceModelParams.<a class="code" href="struct_inference_model_internal_1_1_params.xhtml#aad2ac35d4cb83ee4da9fad5fbcb907e0">m_InputBindings</a>.size();</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < numInputs; ++i)</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  {</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<QuantizationParams></a> qParams = params.<a class="code" href="struct_execute_network_params.xhtml#a077f6963fc555d9d42f98cf9ed3e7e03">m_QuantizeInput</a> ?</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  armnn::MakeOptional<QuantizationParams>(model.<a class="code" href="class_inference_model.xhtml#a066580d185559e2efdcb6cedd1709b9c">GetInputQuantizationParams</a>()) :</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a>();</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span> </div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<std::string></a> dataFile = params.<a class="code" href="struct_execute_network_params.xhtml#a99c7360a4d4b248b3f10607bc5d2fe7b">m_GenerateTensorData</a> ?</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a>() :</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  armnn::MakeOptional<std::string>(params.<a class="code" href="struct_execute_network_params.xhtml#a924e9ee22f0bf39f05e0c1b45e5c637b">m_InputTensorDataFilePaths</a>[i]);</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span> </div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numElements = model.<a class="code" href="class_inference_model.xhtml#a679e4b22a845c8d7f58f6ca6a5df625f">GetInputSize</a>(i);</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <span class="keywordflow">if</span> (params.<a class="code" href="struct_execute_network_params.xhtml#a091cda9098c6f03f91f477a22327892d">m_InputTensorShapes</a>.size() > i && params.<a class="code" href="struct_execute_network_params.xhtml#a091cda9098c6f03f91f477a22327892d">m_InputTensorShapes</a>[i])</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  {</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <span class="comment">// If the user has provided a tensor shape for the current input,</span></div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <span class="comment">// override numElements</span></div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  numElements = params.<a class="code" href="struct_execute_network_params.xhtml#a091cda9098c6f03f91f477a22327892d">m_InputTensorShapes</a>[i]->GetNumElements();</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  }</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span> </div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <a class="code" href="_model_accuracy_checker_test_8cpp.xhtml#a9eb69ebdaf4ceb8014e7c8a540266100">TContainer</a> tensorData;</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  PopulateTensorWithData(tensorData,</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  numElements,</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a08c2e205fcf14f0caa44388f8314e7b5">m_InputTypes</a>[i],</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  qParams,</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  dataFile);</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span> </div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  inputDataContainers.push_back(tensorData);</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  }</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span> </div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> numOutputs = inferenceModelParams.<a class="code" href="struct_inference_model_internal_1_1_params.xhtml#abeacb4ed1ca9256ee0e8aea73185a0cc">m_OutputBindings</a>.size();</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  std::vector<TContainer> outputDataContainers;</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span> </div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < numOutputs; ++i)</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  {</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <span class="keywordflow">if</span> (params.<a class="code" href="struct_execute_network_params.xhtml#a71c32d9b0334a1561bb6d2db677755d6">m_OutputTypes</a>[i].compare(<span class="stringliteral">"float"</span>) == 0)</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  {</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  outputDataContainers.push_back(std::vector<float>(model.<a class="code" href="class_inference_model.xhtml#a8282dddf88e0deb3c414235e20a6cb2c">GetOutputSize</a>(i)));</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  }</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (params.<a class="code" href="struct_execute_network_params.xhtml#a71c32d9b0334a1561bb6d2db677755d6">m_OutputTypes</a>[i].compare(<span class="stringliteral">"int"</span>) == 0)</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  {</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  outputDataContainers.push_back(std::vector<int>(model.<a class="code" href="class_inference_model.xhtml#a8282dddf88e0deb3c414235e20a6cb2c">GetOutputSize</a>(i)));</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  }</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (params.<a class="code" href="struct_execute_network_params.xhtml#a71c32d9b0334a1561bb6d2db677755d6">m_OutputTypes</a>[i].compare(<span class="stringliteral">"qasymm8"</span>) == 0)</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  {</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  outputDataContainers.push_back(std::vector<uint8_t>(model.<a class="code" href="class_inference_model.xhtml#a8282dddf88e0deb3c414235e20a6cb2c">GetOutputSize</a>(i)));</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  }</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  {</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"Unsupported tensor data type \""</span> << params.<a class="code" href="struct_execute_network_params.xhtml#a71c32d9b0334a1561bb6d2db677755d6">m_OutputTypes</a>[i] << <span class="stringliteral">"\". "</span>;</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  }</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  }</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span> </div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <span class="comment">// model.Run returns the inference time elapsed in EnqueueWorkload (in milliseconds)</span></div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="keyword">auto</span> inference_duration = model.<a class="code" href="class_inference_model.xhtml#a7af4f6c4d5f8720a6ea093a825722227">Run</a>(inputDataContainers, outputDataContainers);</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span> </div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="keywordflow">if</span> (params.<a class="code" href="struct_execute_network_params.xhtml#a99c7360a4d4b248b3f10607bc5d2fe7b">m_GenerateTensorData</a>)</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  {</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(warning) << <span class="stringliteral">"The input data was generated, note that the output will not be useful"</span>;</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  }</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span> </div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <span class="comment">// Print output tensors</span></div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& infosOut = model.<a class="code" href="class_inference_model.xhtml#ac0b73049e00e7013f5cc6ae7fcaedcd4">GetOutputBindingInfos</a>();</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < numOutputs; i++)</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  {</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& infoOut = infosOut[i].second;</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  <span class="keyword">auto</span> outputTensorFile = params.<a class="code" href="struct_execute_network_params.xhtml#a64c0a99d553c42d215c4b2f1a2f1c7d4">m_OutputTensorFiles</a>.empty() ? <span class="stringliteral">""</span> : params.<a class="code" href="struct_execute_network_params.xhtml#a64c0a99d553c42d215c4b2f1a2f1c7d4">m_OutputTensorFiles</a>[i];</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span> </div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  TensorPrinter printer(inferenceModelParams.<a class="code" href="struct_inference_model_internal_1_1_params.xhtml#abeacb4ed1ca9256ee0e8aea73185a0cc">m_OutputBindings</a>[i],</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  infoOut,</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  outputTensorFile,</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a85929a48c5e7b16af8f5bc637e45a37f">m_DequantizeOutput</a>);</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  boost::apply_visitor(printer, outputDataContainers[i]);</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  }</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span> </div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(info) << <span class="stringliteral">"\nInference time: "</span> << std::setprecision(2)</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  << std::fixed << inference_duration.count() << <span class="stringliteral">" ms"</span>;</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span> </div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <span class="comment">// If thresholdTime == 0.0 (default), then it hasn't been supplied at command line</span></div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  <span class="keywordflow">if</span> (params.<a class="code" href="struct_execute_network_params.xhtml#a318999172ae5197f56326b12d29104b7">m_ThresholdTime</a> != 0.0)</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  {</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(info) << <span class="stringliteral">"Threshold time: "</span> << std::setprecision(2)</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  << std::fixed << params.<a class="code" href="struct_execute_network_params.xhtml#a318999172ae5197f56326b12d29104b7">m_ThresholdTime</a> << <span class="stringliteral">" ms"</span>;</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <span class="keyword">auto</span> thresholdMinusInference = params.<a class="code" href="struct_execute_network_params.xhtml#a318999172ae5197f56326b12d29104b7">m_ThresholdTime</a> - inference_duration.count();</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(info) << <span class="stringliteral">"Threshold time - Inference time: "</span> << std::setprecision(2)</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  << std::fixed << thresholdMinusInference << <span class="stringliteral">" ms"</span> << <span class="stringliteral">"\n"</span>;</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span> </div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  <span class="keywordflow">if</span> (thresholdMinusInference < 0)</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  {</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  std::string errorMessage = <span class="stringliteral">"Elapsed inference time is greater than provided threshold time."</span>;</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << errorMessage;</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  }</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  }</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  }</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <span class="keywordflow">catch</span> (<a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a> <span class="keyword">const</span>& e)</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  {</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"Armnn Error: "</span> << e.<a class="code" href="classarmnn_1_1_exception.xhtml#abf843cbb29dec939d0731e491bab6f70">what</a>();</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  }</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span> </div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="keywordflow">return</span> EXIT_SUCCESS;</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span> }</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span> </div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span> <span class="comment">// This will run a test</span></div><div class="line"><a name="l00537"></a><span class="lineno"><a class="line" href="_network_execution_utils_8hpp.xhtml#ab182729acbd2161a0358d85906d30703"> 537</a></span> <span class="keywordtype">int</span> <a class="code" href="_network_execution_utils_8hpp.xhtml#ab182729acbd2161a0358d85906d30703">RunTest</a>(<span class="keyword">const</span> std::string& format,</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <span class="keyword">const</span> std::string& inputTensorShapesStr,</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <span class="keyword">const</span> vector<armnn::BackendId>& computeDevices,</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  <span class="keyword">const</span> std::string& dynamicBackendsPath,</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  <span class="keyword">const</span> std::string& path,</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keyword">const</span> std::string& inputNames,</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="keyword">const</span> std::string& inputTensorDataFilePaths,</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <span class="keyword">const</span> std::string& inputTypes,</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <span class="keywordtype">bool</span> quantizeInput,</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  <span class="keyword">const</span> std::string& outputTypes,</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <span class="keyword">const</span> std::string& outputNames,</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  <span class="keyword">const</span> std::string& outputTensorFiles,</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <span class="keywordtype">bool</span> dequantizeOuput,</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <span class="keywordtype">bool</span> enableProfiling,</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  <span class="keywordtype">bool</span> enableFp16TurboMode,</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <span class="keyword">const</span> <span class="keywordtype">double</span>& thresholdTime,</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <span class="keywordtype">bool</span> printIntermediate,</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> subgraphId,</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  <span class="keywordtype">bool</span> enableLayerDetails = <span class="keyword">false</span>,</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <span class="keywordtype">bool</span> parseUnsupported = <span class="keyword">false</span>,</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <span class="keyword">const</span> std::shared_ptr<armnn::IRuntime>& runtime = <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span> {</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  std::string modelFormat = boost::trim_copy(format);</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  std::string modelPath = boost::trim_copy(path);</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  std::vector<std::string> inputNamesVector = ParseStringList(inputNames, <span class="stringliteral">","</span>);</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  std::vector<std::string> inputTensorShapesVector = ParseStringList(inputTensorShapesStr, <span class="stringliteral">":"</span>);</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  std::vector<std::string> inputTensorDataFilePathsVector = ParseStringList(</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  inputTensorDataFilePaths, <span class="stringliteral">","</span>);</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  std::vector<std::string> outputNamesVector = ParseStringList(outputNames, <span class="stringliteral">","</span>);</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  std::vector<std::string> inputTypesVector = ParseStringList(inputTypes, <span class="stringliteral">","</span>);</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  std::vector<std::string> outputTypesVector = ParseStringList(outputTypes, <span class="stringliteral">","</span>);</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  std::vector<std::string> outputTensorFilesVector = ParseStringList(outputTensorFiles, <span class="stringliteral">","</span>);</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span> </div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  <span class="comment">// Parse model binary flag from the model-format string we got from the command-line</span></div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <span class="keywordtype">bool</span> isModelBinary;</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="keywordflow">if</span> (modelFormat.find(<span class="stringliteral">"bin"</span>) != std::string::npos)</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  {</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  isModelBinary = <span class="keyword">true</span>;</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  }</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (modelFormat.find(<span class="stringliteral">"txt"</span>) != std::string::npos || modelFormat.find(<span class="stringliteral">"text"</span>) != std::string::npos)</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  {</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  isModelBinary = <span class="keyword">false</span>;</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  }</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  {</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"Unknown model format: '"</span> << modelFormat << <span class="stringliteral">"'. Please include 'binary' or 'text'"</span>;</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  }</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span> </div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  <span class="keywordflow">if</span> ((inputTensorShapesVector.size() != 0) && (inputTensorShapesVector.size() != inputNamesVector.size()))</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  {</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"input-name and input-tensor-shape must have the same amount of elements."</span>;</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  }</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span> </div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  <span class="keywordflow">if</span> ((inputTensorDataFilePathsVector.size() != 0) &&</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  (inputTensorDataFilePathsVector.size() != inputNamesVector.size()))</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  {</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"input-name and input-tensor-data must have the same amount of elements."</span>;</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  }</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span> </div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <span class="keywordflow">if</span> ((outputTensorFilesVector.size() != 0) &&</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  (outputTensorFilesVector.size() != outputNamesVector.size()))</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  {</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"output-name and write-outputs-to-file must have the same amount of elements."</span>;</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  }</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span> </div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <span class="keywordflow">if</span> (inputTypesVector.size() == 0)</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  {</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  <span class="comment">//Defaults the value of all inputs to "float"</span></div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  inputTypesVector.assign(inputNamesVector.size(), <span class="stringliteral">"float"</span>);</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  }</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> ((inputTypesVector.size() != 0) && (inputTypesVector.size() != inputNamesVector.size()))</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  {</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"input-name and input-type must have the same amount of elements."</span>;</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  }</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span> </div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <span class="keywordflow">if</span> (outputTypesVector.size() == 0)</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  {</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <span class="comment">//Defaults the value of all outputs to "float"</span></div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  outputTypesVector.assign(outputNamesVector.size(), <span class="stringliteral">"float"</span>);</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  }</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> ((outputTypesVector.size() != 0) && (outputTypesVector.size() != outputNamesVector.size()))</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  {</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"output-name and output-type must have the same amount of elements."</span>;</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  }</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span> </div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  <span class="comment">// Parse input tensor shape from the string we got from the command-line.</span></div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  std::vector<std::unique_ptr<armnn::TensorShape>> inputTensorShapes;</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span> </div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <span class="keywordflow">if</span> (!inputTensorShapesVector.empty())</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  {</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  inputTensorShapes.reserve(inputTensorShapesVector.size());</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span> </div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  <span class="keywordflow">for</span>(<span class="keyword">const</span> std::string& shape : inputTensorShapesVector)</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  {</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  std::stringstream ss(shape);</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  std::vector<unsigned int> dims = ParseArray(ss);</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span> </div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  {</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <span class="comment">// Coverity fix: An exception of type armnn::InvalidArgumentException is thrown and never caught.</span></div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  inputTensorShapes.push_back(std::make_unique<armnn::TensorShape>(dims.size(), dims.data()));</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  }</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>& e)</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  {</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"Cannot create tensor shape: "</span> << e.<a class="code" href="classarmnn_1_1_exception.xhtml#abf843cbb29dec939d0731e491bab6f70">what</a>();</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  }</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  }</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  }</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span> </div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <span class="comment">// Check that threshold time is not less than zero</span></div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  <span class="keywordflow">if</span> (thresholdTime < 0)</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  {</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"Threshold time supplied as a command line argument is less than zero."</span>;</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  }</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span> </div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <a class="code" href="struct_execute_network_params.xhtml">ExecuteNetworkParams</a> params;</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a44c7d128c41b2717fe425cf6fdc32936">m_ModelPath</a> = modelPath.c_str();</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a6bf2f586c403977d31c7d32d371918cf">m_IsModelBinary</a> = isModelBinary;</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a2d54e6252c1c9a0e29f7706ba03b2b74">m_ComputeDevices</a> = computeDevices;</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  params.<a class="code" href="struct_execute_network_params.xhtml#ae43cf4b5df0068ee6a9151c98947248b">m_DynamicBackendsPath</a> = dynamicBackendsPath;</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a155de45be27135e4c9b6b7df277d0b8f">m_InputNames</a> = inputNamesVector;</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a091cda9098c6f03f91f477a22327892d">m_InputTensorShapes</a> = std::move(inputTensorShapes);</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a924e9ee22f0bf39f05e0c1b45e5c637b">m_InputTensorDataFilePaths</a> = inputTensorDataFilePathsVector;</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a08c2e205fcf14f0caa44388f8314e7b5">m_InputTypes</a> = inputTypesVector;</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a077f6963fc555d9d42f98cf9ed3e7e03">m_QuantizeInput</a> = quantizeInput;</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a71c32d9b0334a1561bb6d2db677755d6">m_OutputTypes</a> = outputTypesVector;</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a1a08c6c34dd3ce290b4bc62a715bb810">m_OutputNames</a> = outputNamesVector;</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a64c0a99d553c42d215c4b2f1a2f1c7d4">m_OutputTensorFiles</a> = outputTensorFilesVector;</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a85929a48c5e7b16af8f5bc637e45a37f">m_DequantizeOutput</a> = dequantizeOuput;</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a26d42007440bb01a1a6d0ab3b5a657ee">m_EnableProfiling</a> = enableProfiling;</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a513151623e8d448951a0b94ad1946fbe">m_EnableFp16TurboMode</a> = enableFp16TurboMode;</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a318999172ae5197f56326b12d29104b7">m_ThresholdTime</a> = thresholdTime;</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a5acc5b4076604db15ee13ee19fa623c4">m_PrintIntermediate</a> = printIntermediate;</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a2d4582aa74998c397bd064ae73745b62">m_SubgraphId</a> = subgraphId;</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a7efc68309e76bfefbfa16fe94501b060">m_EnableLayerDetails</a> = enableLayerDetails;</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a99c7360a4d4b248b3f10607bc5d2fe7b">m_GenerateTensorData</a> = inputTensorDataFilePathsVector.empty();</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a5c7f0c083da98e7b6e9ba79d2fcd985d">m_ParseUnsupported</a> = parseUnsupported;</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span> </div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  <span class="comment">// Warn if ExecuteNetwork will generate dummy input data</span></div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  <span class="keywordflow">if</span> (params.<a class="code" href="struct_execute_network_params.xhtml#a99c7360a4d4b248b3f10607bc5d2fe7b">m_GenerateTensorData</a>)</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  {</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(warning) << <span class="stringliteral">"No input files provided, input tensors will be filled with 0s."</span>;</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  }</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span> </div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  <span class="comment">// Forward to implementation based on the parser type</span></div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  <span class="keywordflow">if</span> (modelFormat.find(<span class="stringliteral">"armnn"</span>) != std::string::npos)</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  {</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span> <span class="preprocessor">#if defined(ARMNN_SERIALIZER)</span></div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  <span class="keywordflow">return</span> MainImpl<armnnDeserializer::IDeserializer, float>(params, runtime);</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span> <span class="preprocessor">#else</span></div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"Not built with serialization support."</span>;</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  }</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (modelFormat.find(<span class="stringliteral">"caffe"</span>) != std::string::npos)</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  {</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span> <span class="preprocessor">#if defined(ARMNN_CAFFE_PARSER)</span></div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  <span class="keywordflow">return</span> MainImpl<armnnCaffeParser::ICaffeParser, float>(params, runtime);</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span> <span class="preprocessor">#else</span></div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"Not built with Caffe parser support."</span>;</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  }</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (modelFormat.find(<span class="stringliteral">"onnx"</span>) != std::string::npos)</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span> {</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span> <span class="preprocessor">#if defined(ARMNN_ONNX_PARSER)</span></div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  <span class="keywordflow">return</span> MainImpl<armnnOnnxParser::IOnnxParser, float>(params, runtime);</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span> <span class="preprocessor">#else</span></div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"Not built with Onnx parser support."</span>;</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  }</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (modelFormat.find(<span class="stringliteral">"tensorflow"</span>) != std::string::npos)</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  {</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span> <span class="preprocessor">#if defined(ARMNN_TF_PARSER)</span></div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  <span class="keywordflow">return</span> MainImpl<armnnTfParser::ITfParser, float>(params, runtime);</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span> <span class="preprocessor">#else</span></div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"Not built with Tensorflow parser support."</span>;</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  }</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(modelFormat.find(<span class="stringliteral">"tflite"</span>) != std::string::npos)</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  {</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span> <span class="preprocessor">#if defined(ARMNN_TF_LITE_PARSER)</span></div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  <span class="keywordflow">if</span> (! isModelBinary)</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  {</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"Unknown model format: '"</span> << modelFormat << <span class="stringliteral">"'. Only 'binary' format supported \</span></div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span> <span class="stringliteral"> for tflite files"</span>;</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  }</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  <span class="keywordflow">return</span> MainImpl<armnnTfLiteParser::ITfLiteParser, float>(params, runtime);</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span> <span class="preprocessor">#else</span></div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"Unknown model format: '"</span> << modelFormat <<</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  <span class="stringliteral">"'. Please include 'caffe', 'tensorflow', 'tflite' or 'onnx'"</span>;</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  }</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  {</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"Unknown model format: '"</span> << modelFormat <<</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  <span class="stringliteral">"'. Please include 'caffe', 'tensorflow', 'tflite' or 'onnx'"</span>;</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  }</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span> }</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span> </div><div class="line"><a name="l00750"></a><span class="lineno"><a class="line" href="_network_execution_utils_8hpp.xhtml#a31556a7bc4fe615f51dd68af510a9947"> 750</a></span> <span class="keywordtype">int</span> <a class="code" href="_network_execution_utils_8hpp.xhtml#a31556a7bc4fe615f51dd68af510a9947">RunCsvTest</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_utils_1_1_csv_row.xhtml">armnnUtils::CsvRow</a> &csvRow, <span class="keyword">const</span> std::shared_ptr<armnn::IRuntime>& runtime,</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> enableProfiling, <span class="keyword">const</span> <span class="keywordtype">bool</span> enableFp16TurboMode, <span class="keyword">const</span> <span class="keywordtype">double</span>& thresholdTime,</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> printIntermediate, <span class="keywordtype">bool</span> enableLayerDetails = <span class="keyword">false</span>, <span class="keywordtype">bool</span> parseUnuspported = <span class="keyword">false</span>)</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span> {</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(runtime);</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  std::string modelFormat;</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  std::string modelPath;</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  std::string inputNames;</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  std::string inputTensorShapes;</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  std::string inputTensorDataFilePaths;</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  std::string outputNames;</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  std::string inputTypes;</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  std::string outputTypes;</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  std::string dynamicBackendsPath;</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  std::string outputTensorFiles;</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span> </div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  <span class="keywordtype">size_t</span> subgraphId = 0;</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span> </div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  <span class="keyword">const</span> std::string backendsMessage = std::string(<span class="stringliteral">"The preferred order of devices to run layers on by default. "</span>)</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  + std::string(<span class="stringliteral">"Possible choices: "</span>)</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  + <a class="code" href="namespacearmnn.xhtml#ac2807505b850738bc8a1991ce669dd47">armnn::BackendRegistryInstance</a>().<a class="code" href="classarmnn_1_1_backend_registry.xhtml#ae1de2f7ca1db17f45f97155e239b8b45">GetBackendIdsAsString</a>();</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span> </div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  po::options_description desc(<span class="stringliteral">"Options"</span>);</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  {</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  desc.add_options()</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  (<span class="stringliteral">"model-format,f"</span>, po::value(&modelFormat),</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  <span class="stringliteral">"armnn-binary, caffe-binary, caffe-text, tflite-binary, onnx-binary, onnx-text, tensorflow-binary or "</span></div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  <span class="stringliteral">"tensorflow-text."</span>)</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  (<span class="stringliteral">"model-path,m"</span>, po::value(&modelPath), <span class="stringliteral">"Path to model file, e.g. .armnn, .caffemodel, .prototxt, "</span></div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  <span class="stringliteral">".tflite, .onnx"</span>)</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  (<span class="stringliteral">"compute,c"</span>, po::value<std::vector<armnn::BackendId>>()->multitoken(),</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  backendsMessage.c_str())</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  (<span class="stringliteral">"dynamic-backends-path,b"</span>, po::value(&dynamicBackendsPath),</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  <span class="stringliteral">"Path where to load any available dynamic backend from. "</span></div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  <span class="stringliteral">"If left empty (the default), dynamic backends will not be used."</span>)</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  (<span class="stringliteral">"input-name,i"</span>, po::value(&inputNames), <span class="stringliteral">"Identifier of the input tensors in the network separated by comma."</span>)</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  (<span class="stringliteral">"subgraph-number,n"</span>, po::value<size_t>(&subgraphId)->default_value(0), <span class="stringliteral">"Id of the subgraph to be "</span></div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  <span class="stringliteral">"executed. Defaults to 0."</span>)</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  (<span class="stringliteral">"input-tensor-shape,s"</span>, po::value(&inputTensorShapes),</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  <span class="stringliteral">"The shape of the input tensors in the network as a flat array of integers separated by comma. "</span></div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  <span class="stringliteral">"Several shapes can be passed separating them by semicolon. "</span></div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  <span class="stringliteral">"This parameter is optional, depending on the network."</span>)</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  (<span class="stringliteral">"input-tensor-data,d"</span>, po::value(&inputTensorDataFilePaths)->default_value(<span class="stringliteral">""</span>),</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  <span class="stringliteral">"Path to files containing the input data as a flat array separated by whitespace. "</span></div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  <span class="stringliteral">"Several paths can be passed separating them by comma. If not specified, the network will be run with dummy "</span></div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  <span class="stringliteral">"data (useful for profiling)."</span>)</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  (<span class="stringliteral">"input-type,y"</span>,po::value(&inputTypes), <span class="stringliteral">"The type of the input tensors in the network separated by comma. "</span></div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  <span class="stringliteral">"If unset, defaults to \"float\" for all defined inputs. "</span></div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  <span class="stringliteral">"Accepted values (float, int or qasymm8)."</span>)</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  (<span class="stringliteral">"quantize-input,q"</span>,po::bool_switch()->default_value(<span class="keyword">false</span>),</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  <span class="stringliteral">"If this option is enabled, all float inputs will be quantized to qasymm8. "</span></div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  <span class="stringliteral">"If unset, default to not quantized. "</span></div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  <span class="stringliteral">"Accepted values (true or false)"</span>)</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  (<span class="stringliteral">"output-type,z"</span>,po::value(&outputTypes), <span class="stringliteral">"The type of the output tensors in the network separated by comma. "</span></div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  <span class="stringliteral">"If unset, defaults to \"float\" for all defined outputs. "</span></div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  <span class="stringliteral">"Accepted values (float, int or qasymm8)."</span>)</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  (<span class="stringliteral">"output-name,o"</span>, po::value(&outputNames),</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <span class="stringliteral">"Identifier of the output tensors in the network separated by comma."</span>)</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  (<span class="stringliteral">"dequantize-output,l"</span>,po::bool_switch()->default_value(<span class="keyword">false</span>),</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  <span class="stringliteral">"If this option is enabled, all quantized outputs will be dequantized to float. "</span></div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  <span class="stringliteral">"If unset, default to not get dequantized. "</span></div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  <span class="stringliteral">"Accepted values (true or false)"</span>)</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  (<span class="stringliteral">"write-outputs-to-file,w"</span>, po::value(&outputTensorFiles),</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  <span class="stringliteral">"Comma-separated list of output file paths keyed with the binding-id of the output slot. "</span></div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  <span class="stringliteral">"If left empty (the default), the output tensors will not be written to a file."</span>);</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  }</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> std::exception& e)</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  {</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  <span class="comment">// Coverity points out that default_value(...) can throw a bad_lexical_cast,</span></div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  <span class="comment">// and that desc.add_options() can throw boost::io::too_few_args.</span></div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  <span class="comment">// They really won't in any of these cases.</span></div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  BOOST_ASSERT_MSG(<span class="keyword">false</span>, <span class="stringliteral">"Caught unexpected exception"</span>);</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"Fatal internal error: "</span> << e.what();</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  }</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span> </div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  std::vector<const char*> clOptions;</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  clOptions.reserve(csvRow.<a class="code" href="structarmnn_utils_1_1_csv_row.xhtml#af80d24ad6806a497ec21dc835c28b7ce">values</a>.size());</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> std::string& value : csvRow.<a class="code" href="structarmnn_utils_1_1_csv_row.xhtml#af80d24ad6806a497ec21dc835c28b7ce">values</a>)</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  {</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  clOptions.push_back(value.c_str());</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  }</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span> </div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  po::variables_map vm;</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  {</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  po::store(po::parse_command_line(static_cast<int>(clOptions.size()), clOptions.data(), desc), vm);</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span> </div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  po::notify(vm);</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span> </div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  CheckOptionDependencies(vm);</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  }</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> po::error& e)</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  {</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  std::cerr << e.what() << std::endl << std::endl;</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  std::cerr << desc << std::endl;</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>  }</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span> </div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  <span class="comment">// Get the value of the switch arguments.</span></div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  <span class="keywordtype">bool</span> quantizeInput = vm[<span class="stringliteral">"quantize-input"</span>].as<<span class="keywordtype">bool</span>>();</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  <span class="keywordtype">bool</span> dequantizeOutput = vm[<span class="stringliteral">"dequantize-output"</span>].as<<span class="keywordtype">bool</span>>();</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span> </div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  <span class="comment">// Get the preferred order of compute devices.</span></div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  std::vector<armnn::BackendId> computeDevices = vm[<span class="stringliteral">"compute"</span>].as<std::vector<armnn::BackendId>>();</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span> </div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  <span class="comment">// Remove duplicates from the list of compute devices.</span></div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  RemoveDuplicateDevices(computeDevices);</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span> </div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  <span class="comment">// Check that the specified compute devices are valid.</span></div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  std::string invalidBackends;</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  <span class="keywordflow">if</span> (!CheckRequestedBackendsAreValid(computeDevices, <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<std::string&></a>(invalidBackends)))</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  {</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(fatal) << <span class="stringliteral">"The list of preferred devices contains invalid backend IDs: "</span></div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  << invalidBackends;</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  <span class="keywordflow">return</span> EXIT_FAILURE;</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  }</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span> </div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  <span class="keywordflow">return</span> <a class="code" href="_network_execution_utils_8hpp.xhtml#ab182729acbd2161a0358d85906d30703">RunTest</a>(modelFormat, inputTensorShapes, computeDevices, dynamicBackendsPath, modelPath, inputNames,</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  inputTensorDataFilePaths, inputTypes, quantizeInput, outputTypes, outputNames, outputTensorFiles,</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  dequantizeOutput, enableProfiling, enableFp16TurboMode, thresholdTime, printIntermediate, subgraphId,</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  enableLayerDetails, parseUnuspported);</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span> }</div><div class="ttc" id="struct_execute_network_params_xhtml_a85929a48c5e7b16af8f5bc637e45a37f"><div class="ttname"><a href="struct_execute_network_params.xhtml#a85929a48c5e7b16af8f5bc637e45a37f">ExecuteNetworkParams::m_DequantizeOutput</a></div><div class="ttdeci">bool m_DequantizeOutput</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00379">NetworkExecutionUtils.hpp:379</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a71c32d9b0334a1561bb6d2db677755d6"><div class="ttname"><a href="struct_execute_network_params.xhtml#a71c32d9b0334a1561bb6d2db677755d6">ExecuteNetworkParams::m_OutputTypes</a></div><div class="ttdeci">std::vector< string > m_OutputTypes</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00376">NetworkExecutionUtils.hpp:376</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a855293b1be0581fb61ef6a1c5b027d0f"><div class="ttname"><a href="namespacearmnn.xhtml#a855293b1be0581fb61ef6a1c5b027d0f">armnn::Dequantize</a></div><div class="ttdeci">float Dequantize(QuantizedType value, float scale, int32_t offset)</div><div class="ttdoc">Dequantize an 8-bit data type into a floating point data type. </div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8cpp_source.xhtml#l00047">TypesUtils.cpp:47</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a64c0a99d553c42d215c4b2f1a2f1c7d4"><div class="ttname"><a href="struct_execute_network_params.xhtml#a64c0a99d553c42d215c4b2f1a2f1c7d4">ExecuteNetworkParams::m_OutputTensorFiles</a></div><div class="ttdeci">std::vector< string > m_OutputTensorFiles</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00378">NetworkExecutionUtils.hpp:378</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a29ca0d7f3ce1cb59535aa4d09f7d1b37"><div class="ttname"><a href="struct_execute_network_params.xhtml#a29ca0d7f3ce1cb59535aa4d09f7d1b37">ExecuteNetworkParams::TensorShapePtr</a></div><div class="ttdeci">std::unique_ptr< armnn::TensorShape > TensorShapePtr</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00365">NetworkExecutionUtils.hpp:365</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="_network_execution_utils_8hpp_xhtml_ab182729acbd2161a0358d85906d30703"><div class="ttname"><a href="_network_execution_utils_8hpp.xhtml#ab182729acbd2161a0358d85906d30703">RunTest</a></div><div class="ttdeci">int RunTest(const std::string &format, const std::string &inputTensorShapesStr, const vector< armnn::BackendId > &computeDevices, const std::string &dynamicBackendsPath, const std::string &path, const std::string &inputNames, const std::string &inputTensorDataFilePaths, const std::string &inputTypes, bool quantizeInput, const std::string &outputTypes, const std::string &outputNames, const std::string &outputTensorFiles, bool dequantizeOuput, bool enableProfiling, bool enableFp16TurboMode, const double &thresholdTime, bool printIntermediate, const size_t subgraphId, bool enableLayerDetails=false, bool parseUnsupported=false, const std::shared_ptr< armnn::IRuntime > &runtime=nullptr)</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00537">NetworkExecutionUtils.hpp:537</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a091cda9098c6f03f91f477a22327892d"><div class="ttname"><a href="struct_execute_network_params.xhtml#a091cda9098c6f03f91f477a22327892d">ExecuteNetworkParams::m_InputTensorShapes</a></div><div class="ttdeci">std::vector< TensorShapePtr > m_InputTensorShapes</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00372">NetworkExecutionUtils.hpp:372</a></div></div>
<div class="ttc" id="class_inference_model_xhtml_a066580d185559e2efdcb6cedd1709b9c"><div class="ttname"><a href="class_inference_model.xhtml#a066580d185559e2efdcb6cedd1709b9c">InferenceModel::GetInputQuantizationParams</a></div><div class="ttdeci">QuantizationParams GetInputQuantizationParams(unsigned int inputIndex=0u) const</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00543">InferenceModel.hpp:543</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="class_inference_model_xhtml_ac0b73049e00e7013f5cc6ae7fcaedcd4"><div class="ttname"><a href="class_inference_model.xhtml#ac0b73049e00e7013f5cc6ae7fcaedcd4">InferenceModel::GetOutputBindingInfos</a></div><div class="ttdeci">const std::vector< armnn::BindingPointInfo > & GetOutputBindingInfos() const</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00531">InferenceModel.hpp:531</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a513151623e8d448951a0b94ad1946fbe"><div class="ttname"><a href="struct_execute_network_params.xhtml#a513151623e8d448951a0b94ad1946fbe">ExecuteNetworkParams::m_EnableFp16TurboMode</a></div><div class="ttdeci">bool m_EnableFp16TurboMode</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00381">NetworkExecutionUtils.hpp:381</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_ae43cf4b5df0068ee6a9151c98947248b"><div class="ttname"><a href="struct_execute_network_params.xhtml#ae43cf4b5df0068ee6a9151c98947248b">ExecuteNetworkParams::m_DynamicBackendsPath</a></div><div class="ttdeci">std::string m_DynamicBackendsPath</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00370">NetworkExecutionUtils.hpp:370</a></div></div>
<div class="ttc" id="structarmnn_utils_1_1_csv_row_xhtml_af80d24ad6806a497ec21dc835c28b7ce"><div class="ttname"><a href="structarmnn_utils_1_1_csv_row.xhtml#af80d24ad6806a497ec21dc835c28b7ce">armnnUtils::CsvRow::values</a></div><div class="ttdeci">std::vector< std::string > values</div><div class="ttdef"><b>Definition:</b> <a href="_csv_reader_8hpp_source.xhtml#l00015">CsvReader.hpp:15</a></div></div>
<div class="ttc" id="_network_execution_utils_8hpp_xhtml_a31556a7bc4fe615f51dd68af510a9947"><div class="ttname"><a href="_network_execution_utils_8hpp.xhtml#a31556a7bc4fe615f51dd68af510a9947">RunCsvTest</a></div><div class="ttdeci">int RunCsvTest(const armnnUtils::CsvRow &csvRow, const std::shared_ptr< armnn::IRuntime > &runtime, const bool enableProfiling, const bool enableFp16TurboMode, const double &thresholdTime, const bool printIntermediate, bool enableLayerDetails=false, bool parseUnuspported=false)</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00750">NetworkExecutionUtils.hpp:750</a></div></div>
<div class="ttc" id="struct_inference_model_internal_1_1_params_xhtml_ae43cf4b5df0068ee6a9151c98947248b"><div class="ttname"><a href="struct_inference_model_internal_1_1_params.xhtml#ae43cf4b5df0068ee6a9151c98947248b">InferenceModelInternal::Params::m_DynamicBackendsPath</a></div><div class="ttdeci">std::string m_DynamicBackendsPath</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00089">InferenceModel.hpp:89</a></div></div>
<div class="ttc" id="class_inference_model_xhtml"><div class="ttname"><a href="class_inference_model.xhtml">InferenceModel</a></div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00316">InferenceModel.hpp:316</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="_i_tf_parser_8hpp_xhtml"><div class="ttname"><a href="_i_tf_parser_8hpp.xhtml">ITfParser.hpp</a></div></div>
<div class="ttc" id="_arm_n_n_8hpp_xhtml"><div class="ttname"><a href="_arm_n_n_8hpp.xhtml">ArmNN.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ac2807505b850738bc8a1991ce669dd47"><div class="ttname"><a href="namespacearmnn.xhtml#ac2807505b850738bc8a1991ce669dd47">armnn::BackendRegistryInstance</a></div><div class="ttdeci">BackendRegistry & BackendRegistryInstance()</div><div class="ttdef"><b>Definition:</b> <a href="_backend_registry_8cpp_source.xhtml#l00013">BackendRegistry.cpp:13</a></div></div>
<div class="ttc" id="_resolve_type_8hpp_xhtml"><div class="ttname"><a href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a></div></div>
<div class="ttc" id="struct_inference_model_internal_1_1_params_xhtml_a513151623e8d448951a0b94ad1946fbe"><div class="ttname"><a href="struct_inference_model_internal_1_1_params.xhtml#a513151623e8d448951a0b94ad1946fbe">InferenceModelInternal::Params::m_EnableFp16TurboMode</a></div><div class="ttdeci">bool m_EnableFp16TurboMode</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00093">InferenceModel.hpp:93</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a5acc5b4076604db15ee13ee19fa623c4"><div class="ttname"><a href="struct_execute_network_params.xhtml#a5acc5b4076604db15ee13ee19fa623c4">ExecuteNetworkParams::m_PrintIntermediate</a></div><div class="ttdeci">bool m_PrintIntermediate</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00383">NetworkExecutionUtils.hpp:383</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &&...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a5c7f0c083da98e7b6e9ba79d2fcd985d"><div class="ttname"><a href="struct_execute_network_params.xhtml#a5c7f0c083da98e7b6e9ba79d2fcd985d">ExecuteNetworkParams::m_ParseUnsupported</a></div><div class="ttdeci">bool m_ParseUnsupported</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00387">NetworkExecutionUtils.hpp:387</a></div></div>
<div class="ttc" id="classarmnn_1_1_backend_registry_xhtml_ae1de2f7ca1db17f45f97155e239b8b45"><div class="ttname"><a href="classarmnn_1_1_backend_registry.xhtml#ae1de2f7ca1db17f45f97155e239b8b45">armnn::BackendRegistry::GetBackendIdsAsString</a></div><div class="ttdeci">std::string GetBackendIdsAsString() const</div><div class="ttdef"><b>Definition:</b> <a href="_backend_registry_8cpp_source.xhtml#l00076">BackendRegistry.cpp:76</a></div></div>
<div class="ttc" id="classarmnn_1_1_optional_reference_switch_xhtml_a77c7d528ac063d870b8c8426ec81c1c3"><div class="ttname"><a href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">armnn::OptionalReferenceSwitch< std::is_reference< T >::value, T >::value</a></div><div class="ttdeci">const T & value() const</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00146">Optional.hpp:146</a></div></div>
<div class="ttc" id="struct_inference_model_internal_1_1_params_xhtml_aaae50a6c0f73e4c210c2e4331c439482"><div class="ttname"><a href="struct_inference_model_internal_1_1_params.xhtml#aaae50a6c0f73e4c210c2e4331c439482">InferenceModelInternal::Params::m_VisualizePostOptimizationModel</a></div><div class="ttdeci">bool m_VisualizePostOptimizationModel</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00092">InferenceModel.hpp:92</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a2d4582aa74998c397bd064ae73745b62"><div class="ttname"><a href="struct_execute_network_params.xhtml#a2d4582aa74998c397bd064ae73745b62">ExecuteNetworkParams::m_SubgraphId</a></div><div class="ttdeci">size_t m_SubgraphId</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00384">NetworkExecutionUtils.hpp:384</a></div></div>
<div class="ttc" id="class_inference_model_xhtml_a8282dddf88e0deb3c414235e20a6cb2c"><div class="ttname"><a href="class_inference_model.xhtml#a8282dddf88e0deb3c414235e20a6cb2c">InferenceModel::GetOutputSize</a></div><div class="ttdeci">unsigned int GetOutputSize(unsigned int outputIndex=0u) const</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00455">InferenceModel.hpp:455</a></div></div>
<div class="ttc" id="_model_accuracy_checker_test_8cpp_xhtml_a9eb69ebdaf4ceb8014e7c8a540266100"><div class="ttname"><a href="_model_accuracy_checker_test_8cpp.xhtml#a9eb69ebdaf4ceb8014e7c8a540266100">TContainer</a></div><div class="ttdeci">boost::variant< std::vector< float >, std::vector< int >, std::vector< unsigned char > > TContainer</div><div class="ttdef"><b>Definition:</b> <a href="_model_accuracy_checker_test_8cpp_source.xhtml#l00059">ModelAccuracyCheckerTest.cpp:59</a></div></div>
<div class="ttc" id="struct_inference_model_internal_1_1_params_xhtml_aad2ac35d4cb83ee4da9fad5fbcb907e0"><div class="ttname"><a href="struct_inference_model_internal_1_1_params.xhtml#aad2ac35d4cb83ee4da9fad5fbcb907e0">InferenceModelInternal::Params::m_InputBindings</a></div><div class="ttdeci">std::vector< std::string > m_InputBindings</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00085">InferenceModel.hpp:85</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a2d54e6252c1c9a0e29f7706ba03b2b74"><div class="ttname"><a href="struct_execute_network_params.xhtml#a2d54e6252c1c9a0e29f7706ba03b2b74">ExecuteNetworkParams::m_ComputeDevices</a></div><div class="ttdeci">std::vector< armnn::BackendId > m_ComputeDevices</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00369">NetworkExecutionUtils.hpp:369</a></div></div>
<div class="ttc" id="_i_tf_lite_parser_8hpp_xhtml"><div class="ttname"><a href="_i_tf_lite_parser_8hpp.xhtml">ITfLiteParser.hpp</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a155de45be27135e4c9b6b7df277d0b8f"><div class="ttname"><a href="struct_execute_network_params.xhtml#a155de45be27135e4c9b6b7df277d0b8f">ExecuteNetworkParams::m_InputNames</a></div><div class="ttdeci">std::vector< string > m_InputNames</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00371">NetworkExecutionUtils.hpp:371</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a6bf2f586c403977d31c7d32d371918cf"><div class="ttname"><a href="struct_execute_network_params.xhtml#a6bf2f586c403977d31c7d32d371918cf">ExecuteNetworkParams::m_IsModelBinary</a></div><div class="ttdeci">bool m_IsModelBinary</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00368">NetworkExecutionUtils.hpp:368</a></div></div>
<div class="ttc" id="struct_inference_model_internal_1_1_params_xhtml_ad69aa6b4967ce55ee4a915c52c71bf2e"><div class="ttname"><a href="struct_inference_model_internal_1_1_params.xhtml#ad69aa6b4967ce55ee4a915c52c71bf2e">InferenceModelInternal::Params::m_InputShapes</a></div><div class="ttdeci">std::vector< armnn::TensorShape > m_InputShapes</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00086">InferenceModel.hpp:86</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="struct_execute_network_params_xhtml_a99c7360a4d4b248b3f10607bc5d2fe7b"><div class="ttname"><a href="struct_execute_network_params.xhtml#a99c7360a4d4b248b3f10607bc5d2fe7b">ExecuteNetworkParams::m_GenerateTensorData</a></div><div class="ttdeci">bool m_GenerateTensorData</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00386">NetworkExecutionUtils.hpp:386</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml"><div class="ttname"><a href="struct_execute_network_params.xhtml">ExecuteNetworkParams</a></div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00363">NetworkExecutionUtils.hpp:363</a></div></div>
<div class="ttc" id="struct_inference_model_internal_1_1_params_xhtml_abeacb4ed1ca9256ee0e8aea73185a0cc"><div class="ttname"><a href="struct_inference_model_internal_1_1_params.xhtml#abeacb4ed1ca9256ee0e8aea73185a0cc">InferenceModelInternal::Params::m_OutputBindings</a></div><div class="ttdeci">std::vector< std::string > m_OutputBindings</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00087">InferenceModel.hpp:87</a></div></div>
<div class="ttc" id="struct_inference_model_internal_1_1_params_xhtml_a2d54e6252c1c9a0e29f7706ba03b2b74"><div class="ttname"><a href="struct_inference_model_internal_1_1_params.xhtml#a2d54e6252c1c9a0e29f7706ba03b2b74">InferenceModelInternal::Params::m_ComputeDevices</a></div><div class="ttdeci">std::vector< armnn::BackendId > m_ComputeDevices</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00088">InferenceModel.hpp:88</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="struct_execute_network_params_xhtml_a318999172ae5197f56326b12d29104b7"><div class="ttname"><a href="struct_execute_network_params.xhtml#a318999172ae5197f56326b12d29104b7">ExecuteNetworkParams::m_ThresholdTime</a></div><div class="ttdeci">double m_ThresholdTime</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00382">NetworkExecutionUtils.hpp:382</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">armnn::Compute::Undefined</a></div></div>
<div class="ttc" id="_network_execution_utils_8hpp_xhtml_ad7abdfb6c0cc99eb356c1eefdc6ff696"><div class="ttname"><a href="_network_execution_utils_8hpp.xhtml#ad7abdfb6c0cc99eb356c1eefdc6ff696">generateTensorData</a></div><div class="ttdeci">bool generateTensorData</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00361">NetworkExecutionUtils.hpp:361</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a44c7d128c41b2717fe425cf6fdc32936"><div class="ttname"><a href="struct_execute_network_params.xhtml#a44c7d128c41b2717fe425cf6fdc32936">ExecuteNetworkParams::m_ModelPath</a></div><div class="ttdeci">const char * m_ModelPath</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00367">NetworkExecutionUtils.hpp:367</a></div></div>
<div class="ttc" id="structarmnn_utils_1_1_csv_row_xhtml"><div class="ttname"><a href="structarmnn_utils_1_1_csv_row.xhtml">armnnUtils::CsvRow</a></div><div class="ttdef"><b>Definition:</b> <a href="_csv_reader_8hpp_source.xhtml#l00013">CsvReader.hpp:13</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a1a08c6c34dd3ce290b4bc62a715bb810"><div class="ttname"><a href="struct_execute_network_params.xhtml#a1a08c6c34dd3ce290b4bc62a715bb810">ExecuteNetworkParams::m_OutputNames</a></div><div class="ttdeci">std::vector< string > m_OutputNames</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00377">NetworkExecutionUtils.hpp:377</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< std::is_unsigned< Source >::value &&std::is_unsigned< Dest >::value, Dest > numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00033">NumericCast.hpp:33</a></div></div>
<div class="ttc" id="namespace_inference_model_internal_xhtml_a6e713a319588c57fc854bc478f5ee13a"><div class="ttname"><a href="namespace_inference_model_internal.xhtml#a6e713a319588c57fc854bc478f5ee13a">InferenceModelInternal::QuantizationParams</a></div><div class="ttdeci">std::pair< float, int32_t > QuantizationParams</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00080">InferenceModel.hpp:80</a></div></div>
<div class="ttc" id="struct_inference_model_internal_1_1_params_xhtml_a5c7f0c083da98e7b6e9ba79d2fcd985d"><div class="ttname"><a href="struct_inference_model_internal_1_1_params.xhtml#a5c7f0c083da98e7b6e9ba79d2fcd985d">InferenceModelInternal::Params::m_ParseUnsupported</a></div><div class="ttdeci">bool m_ParseUnsupported</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00095">InferenceModel.hpp:95</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="_i_onnx_parser_8hpp_xhtml"><div class="ttname"><a href="_i_onnx_parser_8hpp.xhtml">IOnnxParser.hpp</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a924e9ee22f0bf39f05e0c1b45e5c637b"><div class="ttname"><a href="struct_execute_network_params.xhtml#a924e9ee22f0bf39f05e0c1b45e5c637b">ExecuteNetworkParams::m_InputTensorDataFilePaths</a></div><div class="ttdeci">std::vector< string > m_InputTensorDataFilePaths</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00373">NetworkExecutionUtils.hpp:373</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="struct_inference_model_internal_1_1_params_xhtml_acde2af8cbbd224a9f94e509ca538a775"><div class="ttname"><a href="struct_inference_model_internal_1_1_params.xhtml#acde2af8cbbd224a9f94e509ca538a775">InferenceModelInternal::Params::m_PrintIntermediateLayers</a></div><div class="ttdeci">bool m_PrintIntermediateLayers</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00094">InferenceModel.hpp:94</a></div></div>
<div class="ttc" id="_i_deserializer_8hpp_xhtml"><div class="ttname"><a href="_i_deserializer_8hpp.xhtml">IDeserializer.hpp</a></div></div>
<div class="ttc" id="class_inference_model_xhtml_a7af4f6c4d5f8720a6ea093a825722227"><div class="ttname"><a href="class_inference_model.xhtml#a7af4f6c4d5f8720a6ea093a825722227">InferenceModel::Run</a></div><div class="ttdeci">std::chrono::duration< double, std::milli > Run(const std::vector< TContainer > &inputContainers, std::vector< TContainer > &outputContainers)</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00461">InferenceModel.hpp:461</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a7efc68309e76bfefbfa16fe94501b060"><div class="ttname"><a href="struct_execute_network_params.xhtml#a7efc68309e76bfefbfa16fe94501b060">ExecuteNetworkParams::m_EnableLayerDetails</a></div><div class="ttdeci">bool m_EnableLayerDetails</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00385">NetworkExecutionUtils.hpp:385</a></div></div>
<div class="ttc" id="classarmnn_1_1_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_exception.xhtml">armnn::Exception</a></div><div class="ttdoc">Base class for all ArmNN exceptions so that users can filter to just those. </div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00046">Exceptions.hpp:46</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a08c2e205fcf14f0caa44388f8314e7b5"><div class="ttname"><a href="struct_execute_network_params.xhtml#a08c2e205fcf14f0caa44388f8314e7b5">ExecuteNetworkParams::m_InputTypes</a></div><div class="ttdeci">std::vector< string > m_InputTypes</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00374">NetworkExecutionUtils.hpp:374</a></div></div>
<div class="ttc" id="class_inference_model_xhtml_a679e4b22a845c8d7f58f6ca6a5df625f"><div class="ttname"><a href="class_inference_model.xhtml#a679e4b22a845c8d7f58f6ca6a5df625f">InferenceModel::GetInputSize</a></div><div class="ttdeci">unsigned int GetInputSize(unsigned int inputIndex=0u) const</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00449">InferenceModel.hpp:449</a></div></div>
<div class="ttc" id="struct_inference_model_internal_1_1_params_xhtml_a4fa312cf0d60fbd3988a7c76ab8e2980"><div class="ttname"><a href="struct_inference_model_internal_1_1_params.xhtml#a4fa312cf0d60fbd3988a7c76ab8e2980">InferenceModelInternal::Params::m_ModelPath</a></div><div class="ttdeci">std::string m_ModelPath</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00084">InferenceModel.hpp:84</a></div></div>
<div class="ttc" id="struct_inference_model_internal_1_1_params_xhtml_a6bf2f586c403977d31c7d32d371918cf"><div class="ttname"><a href="struct_inference_model_internal_1_1_params.xhtml#a6bf2f586c403977d31c7d32d371918cf">InferenceModelInternal::Params::m_IsModelBinary</a></div><div class="ttdeci">bool m_IsModelBinary</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00091">InferenceModel.hpp:91</a></div></div>
<div class="ttc" id="struct_inference_model_internal_1_1_params_xhtml_a2d4582aa74998c397bd064ae73745b62"><div class="ttname"><a href="struct_inference_model_internal_1_1_params.xhtml#a2d4582aa74998c397bd064ae73745b62">InferenceModelInternal::Params::m_SubgraphId</a></div><div class="ttdeci">size_t m_SubgraphId</div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00090">InferenceModel.hpp:90</a></div></div>
<div class="ttc" id="_network_execution_utils_8hpp_xhtml_afc1c3398fd2de1051edf23a171cfa01b"><div class="ttname"><a href="_network_execution_utils_8hpp.xhtml#afc1c3398fd2de1051edf23a171cfa01b">MainImpl</a></div><div class="ttdeci">int MainImpl(const ExecuteNetworkParams &params, const std::shared_ptr< armnn::IRuntime > &runtime=nullptr)</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00391">NetworkExecutionUtils.hpp:391</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a077f6963fc555d9d42f98cf9ed3e7e03"><div class="ttname"><a href="struct_execute_network_params.xhtml#a077f6963fc555d9d42f98cf9ed3e7e03">ExecuteNetworkParams::m_QuantizeInput</a></div><div class="ttdeci">bool m_QuantizeInput</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00375">NetworkExecutionUtils.hpp:375</a></div></div>
<div class="ttc" id="_csv_reader_8hpp_xhtml"><div class="ttname"><a href="_csv_reader_8hpp.xhtml">CsvReader.hpp</a></div></div>
<div class="ttc" id="_profiling_8hpp_xhtml"><div class="ttname"><a href="_profiling_8hpp.xhtml">Profiling.hpp</a></div></div>
<div class="ttc" id="struct_inference_model_internal_1_1_params_xhtml"><div class="ttname"><a href="struct_inference_model_internal_1_1_params.xhtml">InferenceModelInternal::Params</a></div><div class="ttdef"><b>Definition:</b> <a href="_inference_model_8hpp_source.xhtml#l00082">InferenceModel.hpp:82</a></div></div>
<div class="ttc" id="_i_caffe_parser_8hpp_xhtml"><div class="ttname"><a href="_i_caffe_parser_8hpp.xhtml">ICaffeParser.hpp</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a26d42007440bb01a1a6d0ab3b5a657ee"><div class="ttname"><a href="struct_execute_network_params.xhtml#a26d42007440bb01a1a6d0ab3b5a657ee">ExecuteNetworkParams::m_EnableProfiling</a></div><div class="ttdeci">bool m_EnableProfiling</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00380">NetworkExecutionUtils.hpp:380</a></div></div>
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