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<div class="title">TfliteExecutor.cpp</div> </div>
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<a href="_tflite_executor_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_tflite_executor_8hpp.xhtml">TfliteExecutor.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"><a class="line" href="class_tf_lite_executor.xhtml#a4a9b0347a0c16183bc06a67d3dbb8e9a"> 8</a></span> <a class="code" href="class_tf_lite_executor.xhtml#a4a9b0347a0c16183bc06a67d3dbb8e9a">TfLiteExecutor::TfLiteExecutor</a>(<span class="keyword">const</span> <a class="code" href="struct_execute_network_params.xhtml">ExecuteNetworkParams</a>& params) : m_Params(params)</div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> {</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>  m_Model = tflite::FlatBufferModel::BuildFromFile(m_Params.<a class="code" href="struct_execute_network_params.xhtml#a4fa312cf0d60fbd3988a7c76ab8e2980">m_ModelPath</a>.c_str());</div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> </div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>  m_TfLiteInterpreter = std::make_unique<Interpreter>();</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>  tflite::ops::builtin::BuiltinOpResolver resolver;</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>  tflite::InterpreterBuilder builder(*m_Model, resolver);</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  builder(&m_TfLiteInterpreter);</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  m_TfLiteInterpreter->AllocateTensors();</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  <span class="keywordtype">int</span> status = kTfLiteError;</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  <span class="keywordflow">if</span> (m_Params.<a class="code" href="struct_execute_network_params.xhtml#a876462c8d3a74b51c890820df24f42f9">m_TfLiteExecutor</a> == <a class="code" href="struct_execute_network_params.xhtml#a721fa5f104533ca06589330accc2857da34feb52e49daeff4cae20f668187ec5c">ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteDelegate</a>)</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  {</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <span class="comment">// Create the Armnn Delegate</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <span class="comment">// Populate a DelegateOptions from the ExecuteNetworkParams.</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <a class="code" href="classarmnn_delegate_1_1_delegate_options.xhtml">armnnDelegate::DelegateOptions</a> delegateOptions = m_Params.ToDelegateOptions();</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  delegateOptions.<a class="code" href="classarmnn_delegate_1_1_delegate_options.xhtml#abfd26a0521c63aaf8857067e4b6b6e3e">SetExternalProfilingParams</a>(delegateOptions.<a class="code" href="classarmnn_delegate_1_1_delegate_options.xhtml#a0bababf4a76395e5a7edb0c598b53b90">GetExternalProfilingParams</a>());</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  theArmnnDelegate(<a class="code" href="namespacearmnn_delegate.xhtml#aaab840e387d9a4e7de223fbc8c969eb3">armnnDelegate::TfLiteArmnnDelegateCreate</a>(delegateOptions),</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <a class="code" href="namespacearmnn_delegate.xhtml#a29bbb05765039d65532d61301b56749e">armnnDelegate::TfLiteArmnnDelegateDelete</a>);</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="comment">// Register armnn_delegate to TfLiteInterpreter</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  status = m_TfLiteInterpreter->ModifyGraphWithDelegate(std::move(theArmnnDelegate));</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keywordflow">if</span> (status != kTfLiteOk)</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  {</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a>(<span class="stringliteral">"Could not register ArmNN TfLite Delegate to TfLiteInterpreter"</span>);</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  }</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  }</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  {</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  std::cout << <span class="stringliteral">"Running on TfLite without ArmNN delegate\n"</span>;</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</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">const</span> <span class="keywordtype">size_t</span> numInputs = m_Params.<a class="code" href="struct_execute_network_params.xhtml#aaf3c7f286030842a31025309ab6a8329">m_InputNames</a>.size();</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>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputIndex = 0; inputIndex < numInputs; ++inputIndex)</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  {</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<std::string></a> dataFile = m_Params.<a class="code" href="struct_execute_network_params.xhtml#a99c7360a4d4b248b3f10607bc5d2fe7b">m_GenerateTensorData</a></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  ? <a class="code" href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a>()</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  : armnn::MakeOptional<std::string>(m_Params.<a class="code" href="struct_execute_network_params.xhtml#ae3c3ae097395afea488cd3e0244269fa">m_InputTensorDataFilePaths</a>[inputIndex]);</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keywordtype">int</span> input = m_TfLiteInterpreter->inputs()[inputIndex];</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  TfLiteIntArray* inputDims = m_TfLiteInterpreter->tensor(input)->dims;</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> </div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 1;</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dim = 0; dim < static_cast<unsigned int>(inputDims->size); ++dim)</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  {</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  inputSize *= inputDims->data[dim];</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> </div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& inputName = m_TfLiteInterpreter->tensor(input)->name;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& dataType = m_TfLiteInterpreter->tensor(input)->type;</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> </div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordflow">switch</span> (dataType)</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  {</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordflow">case</span> kTfLiteFloat32:</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="keyword">auto</span> inputData = m_TfLiteInterpreter->typed_tensor<<span class="keywordtype">float</span>>(input);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  PopulateTensorWithData<float>(inputData, inputSize, dataFile, inputName);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keywordflow">break</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">case</span> kTfLiteInt32:</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>  <span class="keyword">auto</span> inputData = m_TfLiteInterpreter->typed_tensor<int32_t>(input);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  PopulateTensorWithData<int32_t>(inputData, inputSize, dataFile, inputName);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  }</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keywordflow">case</span> kTfLiteUInt8:</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  {</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keyword">auto</span> inputData = m_TfLiteInterpreter->typed_tensor<uint8_t>(input);</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  PopulateTensorWithData<uint8_t>(inputData, inputSize, dataFile, inputName);</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keywordflow">break</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">case</span> kTfLiteInt16:</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>  <span class="keyword">auto</span> inputData = m_TfLiteInterpreter->typed_tensor<int16_t>(input);</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  PopulateTensorWithData<int16_t>(inputData, inputSize, dataFile, inputName);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordflow">break</span>;</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="keywordflow">case</span> kTfLiteInt8:</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  {</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keyword">auto</span> inputData = m_TfLiteInterpreter->typed_tensor<int8_t>(input);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  PopulateTensorWithData<int8_t>(inputData, inputSize, dataFile, inputName);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordflow">break</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="keywordflow">default</span>:</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  {</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a>(<span class="stringliteral">"Unsupported input tensor data type"</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>  }</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  }</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> }</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> </div><div class="line"><a name="l00103"></a><span class="lineno"><a class="line" href="class_tf_lite_executor.xhtml#a7b274ddaba15738d696359ad327a88ca"> 103</a></span> std::vector<const void *> <a class="code" href="class_tf_lite_executor.xhtml#a7b274ddaba15738d696359ad327a88ca">TfLiteExecutor::Execute</a>()</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> {</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordtype">int</span> status = 0;</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  std::vector<const void*> results;</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> x = 0; x < m_Params.<a class="code" href="struct_execute_network_params.xhtml#a6e4eff6a5f40cb026ea76d3c13c96341">m_Iterations</a>; x++)</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">// Start timer to record inference time in milliseconds.</span></div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keyword">const</span> <span class="keyword">auto</span> start_time = <a class="code" href="namespacearmnn.xhtml#ac895f6f6897ce335b7b433201bae0b48">armnn::GetTimeNow</a>();</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="comment">// Run the inference</span></div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  status = m_TfLiteInterpreter->Invoke();</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keyword">const</span> <span class="keyword">auto</span> duration = <a class="code" href="namespacearmnn.xhtml#a441621f00fd5665898c81a5ae3473c6b">armnn::GetTimeDuration</a>(start_time);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> </div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keywordflow">if</span> (m_Params.<a class="code" href="struct_execute_network_params.xhtml#ac828647e0c753c3727c8c1f81939f7e4">m_DontPrintOutputs</a> || m_Params.<a class="code" href="struct_execute_network_params.xhtml#acb9376adc0f7174b7d4295e00315a084">m_ReuseBuffers</a>)</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>  <span class="keywordflow">break</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="comment">// Print out the output</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputIndex = 0; outputIndex < m_TfLiteInterpreter->outputs().size(); ++outputIndex)</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  {</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keyword">auto</span> tfLiteDelegateOutputId = m_TfLiteInterpreter->outputs()[outputIndex];</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  TfLiteIntArray* outputDims = m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->dims;</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="comment">// If we've been asked to write to a file then set a file output stream. Otherwise use stdout.</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  FILE* outputTensorFile = stdout;</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keywordflow">if</span> (!m_Params.<a class="code" href="struct_execute_network_params.xhtml#a74d346297c55b516908c541030adc88d">m_OutputTensorFiles</a>.empty())</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  {</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  outputTensorFile = fopen(m_Params.<a class="code" href="struct_execute_network_params.xhtml#a74d346297c55b516908c541030adc88d">m_OutputTensorFiles</a>[outputIndex].c_str(), <span class="stringliteral">"w"</span>);</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="keywordflow">if</span> (outputTensorFile == NULL)</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  {</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a>(<span class="stringliteral">"Specified output tensor file, \""</span> + m_Params.<a class="code" href="struct_execute_network_params.xhtml#a74d346297c55b516908c541030adc88d">m_OutputTensorFiles</a>[outputIndex] +</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="stringliteral">"\", cannot be created. Defaulting to stdout. Error was: "</span> + std::strerror(errno));</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  }</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  {</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(info) << <span class="stringliteral">"Writing output "</span> << outputIndex << <span class="stringliteral">"' of iteration: "</span> << x+1 << <span class="stringliteral">" to file: '"</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  << m_Params.<a class="code" href="struct_execute_network_params.xhtml#a74d346297c55b516908c541030adc88d">m_OutputTensorFiles</a>[outputIndex] << <span class="stringliteral">"'"</span>;</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  }</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  }</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keywordtype">long</span> outputSize = 1;</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dim = 0; dim < static_cast<unsigned int>(outputDims->size); ++dim)</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  {</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  outputSize *= outputDims->data[dim];</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  }</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>  std::cout << m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->name << <span class="stringliteral">": "</span>;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  results.push_back(m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->allocation);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="keywordflow">switch</span> (m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->type)</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  {</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> </div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keywordflow">case</span> kTfLiteFloat32:</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">auto</span> tfLiteDelegateOutputData = m_TfLiteInterpreter->typed_tensor<<span class="keywordtype">float</span>>(tfLiteDelegateOutputId);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> </div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < outputSize; ++i)</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  {</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  fprintf(outputTensorFile, <span class="stringliteral">"%f "</span>, tfLiteDelegateOutputData[i]);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  }</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keywordflow">break</span>;</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">case</span> kTfLiteInt32:</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  {</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="keyword">auto</span> tfLiteDelegateOutputData = m_TfLiteInterpreter->typed_tensor<int32_t>(tfLiteDelegateOutputId);</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < outputSize; ++i)</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>  fprintf(outputTensorFile, <span class="stringliteral">"%d "</span>, tfLiteDelegateOutputData[i]);</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  }</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  }</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keywordflow">case</span> kTfLiteUInt8:</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  {</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keyword">auto</span> tfLiteDelegateOutputData = m_TfLiteInterpreter->typed_tensor<uint8_t>(tfLiteDelegateOutputId);</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < outputSize; ++i)</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>  fprintf(outputTensorFile, <span class="stringliteral">"%u "</span>, tfLiteDelegateOutputData[i]);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  }</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordflow">break</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="keywordflow">case</span> kTfLiteInt8:</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="keyword">auto</span> tfLiteDelegateOutputData = m_TfLiteInterpreter->typed_tensor<int8_t>(tfLiteDelegateOutputId);</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < outputSize; ++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>  fprintf(outputTensorFile, <span class="stringliteral">"%d "</span>, tfLiteDelegateOutputData[i]);</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">break</span>;</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>  <span class="keywordflow">default</span>:</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>  <a class="code" href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a>(<span class="stringliteral">"Unsupported output type"</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> </div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  std::cout << std::endl;</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  }</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#ab9be7e320a1879b362298cb198250dae">CheckInferenceTimeThreshold</a>(duration, m_Params.<a class="code" href="struct_execute_network_params.xhtml#a318999172ae5197f56326b12d29104b7">m_ThresholdTime</a>);</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> </div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  std::cout << status;</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="keywordflow">return</span> results;</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> }</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span> </div><div class="line"><a name="l00204"></a><span class="lineno"><a class="line" href="class_tf_lite_executor.xhtml#ac65a3d900d923c4582e059c2281e70e3"> 204</a></span> <span class="keywordtype">void</span> <a class="code" href="class_tf_lite_executor.xhtml#ac65a3d900d923c4582e059c2281e70e3">TfLiteExecutor::CompareAndPrintResult</a>(std::vector<const void*> otherOutput)</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> {</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputIndex = 0; outputIndex < m_TfLiteInterpreter->outputs().size(); ++outputIndex)</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  {</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keyword">auto</span> tfLiteDelegateOutputId = m_TfLiteInterpreter->outputs()[outputIndex];</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="keywordtype">float</span> result = 0;</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keywordflow">switch</span> (m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->type)</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="keywordflow">case</span> kTfLiteFloat32:</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>  result = ComputeRMSE<float>(m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->allocation,</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  otherOutput[outputIndex],</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->bytes);</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  }</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keywordflow">case</span> kTfLiteInt32:</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  {</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  result = ComputeRMSE<int32_t>(m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->allocation,</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  otherOutput[outputIndex],</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->bytes);</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  }</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordflow">case</span> kTfLiteUInt8:</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  {</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  result = ComputeRMSE<uint8_t>(m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->allocation,</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  otherOutput[outputIndex],</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->bytes);</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  }</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">case</span> kTfLiteInt8:</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>  result = ComputeRMSE<int8_t>(m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->allocation,</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  otherOutput[outputIndex],</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->bytes);</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  }</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keywordflow">default</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>  }</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> </div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  std::cout << <span class="stringliteral">"RMSE of "</span></div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  << m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->name</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  << <span class="stringliteral">": "</span> << result << std::endl;</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  }</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> };</div><div class="ttc" id="struct_execute_network_params_xhtml_a4fa312cf0d60fbd3988a7c76ab8e2980"><div class="ttname"><a href="struct_execute_network_params.xhtml#a4fa312cf0d60fbd3988a7c76ab8e2980">ExecuteNetworkParams::m_ModelPath</a></div><div class="ttdeci">std::string m_ModelPath</div><div class="ttdef"><b>Definition:</b> <a href="_execute_network_params_8hpp_source.xhtml#l00045">ExecuteNetworkParams.hpp:45</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a441621f00fd5665898c81a5ae3473c6b"><div class="ttname"><a href="namespacearmnn.xhtml#a441621f00fd5665898c81a5ae3473c6b">armnn::GetTimeDuration</a></div><div class="ttdeci">std::chrono::duration< double, std::milli > GetTimeDuration(std::chrono::high_resolution_clock::time_point start_time)</div><div class="ttdef"><b>Definition:</b> <a href="_timer_8hpp_source.xhtml#l00019">Timer.hpp:19</a></div></div>
<div class="ttc" id="_network_execution_utils_8cpp_xhtml_a0d853d3a7b138f39cc775c26e2c0821a"><div class="ttname"><a href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a></div><div class="ttdeci">void LogAndThrow(std::string eMsg)</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8cpp_source.xhtml#l00075">NetworkExecutionUtils.cpp:75</a></div></div>
<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional< std::string ></a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a721fa5f104533ca06589330accc2857da34feb52e49daeff4cae20f668187ec5c"><div class="ttname"><a href="struct_execute_network_params.xhtml#a721fa5f104533ca06589330accc2857da34feb52e49daeff4cae20f668187ec5c">ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteDelegate</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_ac828647e0c753c3727c8c1f81939f7e4"><div class="ttname"><a href="struct_execute_network_params.xhtml#ac828647e0c753c3727c8c1f81939f7e4">ExecuteNetworkParams::m_DontPrintOutputs</a></div><div class="ttdeci">bool m_DontPrintOutputs</div><div class="ttdef"><b>Definition:</b> <a href="_execute_network_params_8hpp_source.xhtml#l00053">ExecuteNetworkParams.hpp:53</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#l00205">Logging.hpp:205</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ac895f6f6897ce335b7b433201bae0b48"><div class="ttname"><a href="namespacearmnn.xhtml#ac895f6f6897ce335b7b433201bae0b48">armnn::GetTimeNow</a></div><div class="ttdeci">std::chrono::high_resolution_clock::time_point GetTimeNow()</div><div class="ttdef"><b>Definition:</b> <a href="_timer_8hpp_source.xhtml#l00014">Timer.hpp:14</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a876462c8d3a74b51c890820df24f42f9"><div class="ttname"><a href="struct_execute_network_params.xhtml#a876462c8d3a74b51c890820df24f42f9">ExecuteNetworkParams::m_TfLiteExecutor</a></div><div class="ttdeci">TfLiteExecutor m_TfLiteExecutor</div><div class="ttdef"><b>Definition:</b> <a href="_execute_network_params_8hpp_source.xhtml#l00061">ExecuteNetworkParams.hpp:61</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a74d346297c55b516908c541030adc88d"><div class="ttname"><a href="struct_execute_network_params.xhtml#a74d346297c55b516908c541030adc88d">ExecuteNetworkParams::m_OutputTensorFiles</a></div><div class="ttdeci">std::vector< std::string > m_OutputTensorFiles</div><div class="ttdef"><b>Definition:</b> <a href="_execute_network_params_8hpp_source.xhtml#l00050">ExecuteNetworkParams.hpp:50</a></div></div>
<div class="ttc" id="classarmnn_delegate_1_1_delegate_options_xhtml_abfd26a0521c63aaf8857067e4b6b6e3e"><div class="ttname"><a href="classarmnn_delegate_1_1_delegate_options.xhtml#abfd26a0521c63aaf8857067e4b6b6e3e">armnnDelegate::DelegateOptions::SetExternalProfilingParams</a></div><div class="ttdeci">void SetExternalProfilingParams(const arm::pipe::ProfilingOptions &externalProfilingParams)</div><div class="ttdef"><b>Definition:</b> <a href="_delegate_options_8hpp_source.xhtml#l00244">DelegateOptions.hpp:244</a></div></div>
<div class="ttc" id="class_tf_lite_executor_xhtml_a4a9b0347a0c16183bc06a67d3dbb8e9a"><div class="ttname"><a href="class_tf_lite_executor.xhtml#a4a9b0347a0c16183bc06a67d3dbb8e9a">TfLiteExecutor::TfLiteExecutor</a></div><div class="ttdeci">TfLiteExecutor(const ExecuteNetworkParams &m_Params)</div><div class="ttdef"><b>Definition:</b> <a href="_tflite_executor_8cpp_source.xhtml#l00008">TfliteExecutor.cpp:8</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a6e4eff6a5f40cb026ea76d3c13c96341"><div class="ttname"><a href="struct_execute_network_params.xhtml#a6e4eff6a5f40cb026ea76d3c13c96341">ExecuteNetworkParams::m_Iterations</a></div><div class="ttdeci">size_t m_Iterations</div><div class="ttdef"><b>Definition:</b> <a href="_execute_network_params_8hpp_source.xhtml#l00044">ExecuteNetworkParams.hpp:44</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="_execute_network_params_8hpp_source.xhtml#l00037">ExecuteNetworkParams.hpp:37</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="ttdoc">Holds all parameters necessary to execute a network Check ExecuteNetworkProgramOptions.cpp for a description of each parameter. </div><div class="ttdef"><b>Definition:</b> <a href="_execute_network_params_8hpp_source.xhtml#l00017">ExecuteNetworkParams.hpp:17</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="_execute_network_params_8hpp_source.xhtml#l00057">ExecuteNetworkParams.hpp:57</a></div></div>
<div class="ttc" id="class_tf_lite_executor_xhtml_ac65a3d900d923c4582e059c2281e70e3"><div class="ttname"><a href="class_tf_lite_executor.xhtml#ac65a3d900d923c4582e059c2281e70e3">TfLiteExecutor::CompareAndPrintResult</a></div><div class="ttdeci">void CompareAndPrintResult(std::vector< const void *> otherOutput) override</div><div class="ttdoc">Compare the output with the result of another IExecutor. </div><div class="ttdef"><b>Definition:</b> <a href="_tflite_executor_8cpp_source.xhtml#l00204">TfliteExecutor.cpp:204</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_aaf3c7f286030842a31025309ab6a8329"><div class="ttname"><a href="struct_execute_network_params.xhtml#aaf3c7f286030842a31025309ab6a8329">ExecuteNetworkParams::m_InputNames</a></div><div class="ttdeci">std::vector< std::string > m_InputNames</div><div class="ttdef"><b>Definition:</b> <a href="_execute_network_params_8hpp_source.xhtml#l00041">ExecuteNetworkParams.hpp:41</a></div></div>
<div class="ttc" id="class_tf_lite_executor_xhtml_a7b274ddaba15738d696359ad327a88ca"><div class="ttname"><a href="class_tf_lite_executor.xhtml#a7b274ddaba15738d696359ad327a88ca">TfLiteExecutor::Execute</a></div><div class="ttdeci">std::vector< const void * > Execute() override</div><div class="ttdoc">Execute the given network. </div><div class="ttdef"><b>Definition:</b> <a href="_tflite_executor_8cpp_source.xhtml#l00103">TfliteExecutor.cpp:103</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_ae3c3ae097395afea488cd3e0244269fa"><div class="ttname"><a href="struct_execute_network_params.xhtml#ae3c3ae097395afea488cd3e0244269fa">ExecuteNetworkParams::m_InputTensorDataFilePaths</a></div><div class="ttdeci">std::vector< std::string > m_InputTensorDataFilePaths</div><div class="ttdef"><b>Definition:</b> <a href="_execute_network_params_8hpp_source.xhtml#l00042">ExecuteNetworkParams.hpp:42</a></div></div>
<div class="ttc" id="_tflite_executor_8hpp_xhtml"><div class="ttname"><a href="_tflite_executor_8hpp.xhtml">TfliteExecutor.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_delegate_xhtml_aaab840e387d9a4e7de223fbc8c969eb3"><div class="ttname"><a href="namespacearmnn_delegate.xhtml#aaab840e387d9a4e7de223fbc8c969eb3">armnnDelegate::TfLiteArmnnDelegateCreate</a></div><div class="ttdeci">TfLiteDelegate * TfLiteArmnnDelegateCreate(armnnDelegate::DelegateOptions options)</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="namespacearmnn_delegate_xhtml_a29bbb05765039d65532d61301b56749e"><div class="ttname"><a href="namespacearmnn_delegate.xhtml#a29bbb05765039d65532d61301b56749e">armnnDelegate::TfLiteArmnnDelegateDelete</a></div><div class="ttdeci">void TfLiteArmnnDelegateDelete(TfLiteDelegate *tfLiteDelegate)</div></div>
<div class="ttc" id="classarmnn_delegate_1_1_delegate_options_xhtml"><div class="ttname"><a href="classarmnn_delegate_1_1_delegate_options.xhtml">armnnDelegate::DelegateOptions</a></div><div class="ttdef"><b>Definition:</b> <a href="_delegate_options_8hpp_source.xhtml#l00021">DelegateOptions.hpp:21</a></div></div>
<div class="ttc" id="_network_execution_utils_8cpp_xhtml_ab9be7e320a1879b362298cb198250dae"><div class="ttname"><a href="_network_execution_utils_8cpp.xhtml#ab9be7e320a1879b362298cb198250dae">CheckInferenceTimeThreshold</a></div><div class="ttdeci">bool CheckInferenceTimeThreshold(const std::chrono::duration< double, std::milli > &duration, const double &thresholdTime)</div><div class="ttdoc">Given a measured duration and a threshold time tell the user whether we succeeded or not...</div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8cpp_source.xhtml#l00017">NetworkExecutionUtils.cpp:17</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_acb9376adc0f7174b7d4295e00315a084"><div class="ttname"><a href="struct_execute_network_params.xhtml#acb9376adc0f7174b7d4295e00315a084">ExecuteNetworkParams::m_ReuseBuffers</a></div><div class="ttdeci">bool m_ReuseBuffers</div><div class="ttdef"><b>Definition:</b> <a href="_execute_network_params_8hpp_source.xhtml#l00064">ExecuteNetworkParams.hpp:64</a></div></div>
<div class="ttc" id="classarmnn_delegate_1_1_delegate_options_xhtml_a0bababf4a76395e5a7edb0c598b53b90"><div class="ttname"><a href="classarmnn_delegate_1_1_delegate_options.xhtml#a0bababf4a76395e5a7edb0c598b53b90">armnnDelegate::DelegateOptions::GetExternalProfilingParams</a></div><div class="ttdeci">const arm::pipe::ProfilingOptions & GetExternalProfilingParams() const</div><div class="ttdef"><b>Definition:</b> <a href="_delegate_options_8hpp_source.xhtml#l00248">DelegateOptions.hpp:248</a></div></div>
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