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<a href="_arm_n_n_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> </div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "<a class="code" href="_arm_n_n_executor_8hpp.xhtml">ArmNNExecutor.hpp</a>"</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include "<a class="code" href="_network_execution_utils_8hpp.xhtml">NetworkExecutionUtils/NetworkExecutionUtils.hpp</a>"</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> </div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_i_async_execution_callback_8hpp.xhtml">armnn/IAsyncExecutionCallback.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_async_execution_callback_8hpp.xhtml">AsyncExecutionCallback.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> </div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="keyword">using namespace </span><a class="code" href="namespacestd_1_1chrono.xhtml">std::chrono</a>;</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"><a class="line" href="class_arm_n_n_executor.xhtml#aa9bcc5837a0ef0e503f56d634b1e7184"> 17</a></span> <a class="code" href="class_arm_n_n_executor.xhtml#aa9bcc5837a0ef0e503f56d634b1e7184">ArmNNExecutor::ArmNNExecutor</a>(<span class="keyword">const</span> <a class="code" href="struct_execute_network_params.xhtml">ExecuteNetworkParams</a>& params, <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a> runtimeOptions)</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> : m_Params(params)</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> {</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  runtimeOptions.<a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml#a2fe8c3eadf4f4f9c0c664a24a2a298f9">m_EnableGpuProfiling</a> = params.<a class="code" href="struct_execute_network_params.xhtml#a26d42007440bb01a1a6d0ab3b5a657ee">m_EnableProfiling</a>;</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  runtimeOptions.<a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.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="l00022"></a><span class="lineno"> 22</span>  m_Runtime = <a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(runtimeOptions);</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> </div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="keyword">auto</span> parser = CreateParser();</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keyword">auto</span> network = parser->CreateNetwork(m_Params);</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keyword">auto</span> optNet = OptimizeNetwork(network.get());</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> </div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  m_IOInfo = GetIOInfo(optNet.get());</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  SetupInputsAndOutputs();</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  std::string errorMsg;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <a class="code" href="namespacearmnn.xhtml#ae060224135f57f926cbda9d2732a2b1f">armnn::ProfilingDetailsMethod</a> profilingDetailsMethod = ProfilingDetailsMethod::Undefined;</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keywordflow">if</span> (params.<a class="code" href="struct_execute_network_params.xhtml#ab17deb382179697b4702cc4f909e71f8">m_OutputDetailsOnlyToStdOut</a>)</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>  profilingDetailsMethod = <a class="code" href="namespacearmnn.xhtml#ae060224135f57f926cbda9d2732a2b1fa566666dfc3a9a82da0d7b0816b19f278">armnn::ProfilingDetailsMethod::DetailsOnly</a>;</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  }</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (params.<a class="code" href="struct_execute_network_params.xhtml#ae96fc745917a3a0c0de7a818c9a05012">m_OutputDetailsToStdOut</a>)</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  {</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  profilingDetailsMethod = <a class="code" href="namespacearmnn.xhtml#ae060224135f57f926cbda9d2732a2b1fa497ab261a562e316736c2cb59b839d32">armnn::ProfilingDetailsMethod::DetailsWithEvents</a>;</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> </div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties{m_Params.<a class="code" href="struct_execute_network_params.xhtml#abf3cb45be3828b72b4ac08f87ac6c779">m_Concurrent</a>,</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  MemorySource::Undefined,</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  MemorySource::Undefined,</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  params.<a class="code" href="struct_execute_network_params.xhtml#a26d42007440bb01a1a6d0ab3b5a657ee">m_EnableProfiling</a>,</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  profilingDetailsMethod};</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  m_Runtime->LoadNetwork(m_NetworkId, std::move(optNet), errorMsg, networkProperties);</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>  <span class="keywordflow">if</span> (m_Params.<a class="code" href="struct_execute_network_params.xhtml#a6e4eff6a5f40cb026ea76d3c13c96341">m_Iterations</a> > 1)</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  {</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  std::stringstream msg;</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  msg << <span class="stringliteral">"Network will be executed "</span> << m_Params.<a class="code" href="struct_execute_network_params.xhtml#a6e4eff6a5f40cb026ea76d3c13c96341">m_Iterations</a>;</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">if</span> (m_Params.<a class="code" href="struct_execute_network_params.xhtml#abf3cb45be3828b72b4ac08f87ac6c779">m_Concurrent</a>)</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>  msg << <span class="stringliteral">" times in an asynchronous manner. "</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="keywordflow">else</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  {</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  msg << <span class="stringliteral">" times successively. "</span>;</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>  msg << <span class="stringliteral">"The input-tensor-data files will be reused recursively if the user didn't provide enough to "</span></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="stringliteral">"cover each execution."</span>;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>) << msg.str();</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> </div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keywordflow">if</span> (m_Params.<a class="code" href="struct_execute_network_params.xhtml#a99c7360a4d4b248b3f10607bc5d2fe7b">m_GenerateTensorData</a>)</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  {</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407a7b83d3f08fa392b79e3f553b585971cd">warning</a>) << <span class="stringliteral">"The input data was generated, note that the output will not be useful"</span>;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  }</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordflow">if</span> (m_Params.<a class="code" href="struct_execute_network_params.xhtml#ac828647e0c753c3727c8c1f81939f7e4">m_DontPrintOutputs</a>)</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>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>) << <span class="stringliteral">"Printing outputs to console is disabled."</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> }</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="keywordtype">void</span> ArmNNExecutor::ExecuteAsync()</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>  std::vector<std::shared_ptr<armnn::IWorkingMemHandle>> memHandles;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  std::unique_ptr<armnn::Threadpool> threadpool;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <a class="code" href="classarmnn_1_1experimental_1_1_async_callback_manager.xhtml">armnn::AsyncCallbackManager</a> callbackManager;</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  std::unordered_map<armnn::InferenceId, const armnn::OutputTensors*> inferenceOutputMap;</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="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < m_Params.<a class="code" href="struct_execute_network_params.xhtml#a7adc5dcfe3d76ac489f253c4d5f439c8">m_ThreadPoolSize</a>; ++i)</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  {</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  memHandles.emplace_back(m_Runtime->CreateWorkingMemHandle(m_NetworkId));</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  }</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  threadpool = std::make_unique<armnn::Threadpool>(m_Params.<a class="code" href="struct_execute_network_params.xhtml#a7adc5dcfe3d76ac489f253c4d5f439c8">m_ThreadPoolSize</a>,</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  m_Runtime.get(),</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  memHandles);</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>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>) << <span class="stringliteral">"Asynchronous Execution with Arm NN thread pool... \n"</span>;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="comment">// Declare the latest and earliest inference times here to be used when calculating overall time</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  std::chrono::high_resolution_clock::time_point earliestStartTime =</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  std::chrono::high_resolution_clock::time_point::max();</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  std::chrono::high_resolution_clock::time_point latestEndTime =</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  std::chrono::high_resolution_clock::now();</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>  <span class="comment">// For the asynchronous execution, we are adding a pool of working memory handles (1 per thread) in the</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="comment">// LoadedNetwork with each scheduled inference having a specific priority</span></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < m_Params.<a class="code" href="struct_execute_network_params.xhtml#a6e4eff6a5f40cb026ea76d3c13c96341">m_Iterations</a>; ++i)</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>  std::shared_ptr<armnn::IProfiler> profiler = m_Runtime->GetProfiler(m_NetworkId);</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>  std::shared_ptr<armnn::AsyncExecutionCallback> cb = callbackManager.<a class="code" href="classarmnn_1_1experimental_1_1_async_callback_manager.xhtml#a27aff9e4e5d9bce49988a2de6a1ebc59">GetNewCallback</a>();</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  inferenceOutputMap.insert({cb->GetInferenceId(), &m_OutputTensorsVec[i]});</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  threadpool->Schedule(m_NetworkId,</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  m_InputTensorsVec[i],</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  m_OutputTensorsVec[i],</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <a class="code" href="namespacearmnn.xhtml#a8e72227ebe5ac505cf44790f2e6eb488a87f8a6ab85c9ced3702b4ea641ad4bb5">armnn::QosExecPriority::Medium</a>,</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  cb);</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>  <span class="comment">// Check the results</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> iteration = 0; iteration < m_Params.<a class="code" href="struct_execute_network_params.xhtml#a6e4eff6a5f40cb026ea76d3c13c96341">m_Iterations</a>; ++iteration)</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  {</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keyword">auto</span> cb = callbackManager.<a class="code" href="classarmnn_1_1experimental_1_1_async_callback_manager.xhtml#a9ee5b1dd7d3a6f619d2ed3d97d75d9b1">GetNotifiedCallback</a>();</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="comment">// Get the results</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keywordflow">if</span> (earliestStartTime > cb->GetStartTime())</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  {</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  earliestStartTime = cb->GetStartTime();</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  }</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keywordflow">if</span> (latestEndTime < cb->GetEndTime())</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  {</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  latestEndTime = cb->GetEndTime();</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> </div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keyword">auto</span> startTime = time_point_cast<std::chrono::milliseconds>(cb->GetStartTime());</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keyword">auto</span> endTime = time_point_cast<std::chrono::milliseconds>(cb->GetEndTime());</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keyword">auto</span> inferenceDuration = endTime - startTime;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#ab9be7e320a1879b362298cb198250dae">CheckInferenceTimeThreshold</a>(inferenceDuration, m_Params.<a class="code" href="struct_execute_network_params.xhtml#a318999172ae5197f56326b12d29104b7">m_ThresholdTime</a>);</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="keywordflow">if</span>(!m_Params.<a class="code" href="struct_execute_network_params.xhtml#ac828647e0c753c3727c8c1f81939f7e4">m_DontPrintOutputs</a>)</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="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">armnn::OutputTensors</a>* out = inferenceOutputMap[cb->GetInferenceId()];</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  PrintOutputTensors(out, iteration);</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>  }</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> </div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="comment">// Print duration difference between overallStartTime and overallEndTime</span></div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keyword">auto</span> overallEndTime = time_point_cast<std::chrono::milliseconds>(latestEndTime);</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keyword">auto</span> overallStartTime = time_point_cast<std::chrono::milliseconds>(earliestStartTime);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keyword">auto</span> totalInferenceDuration = overallEndTime - overallStartTime;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>) << <span class="stringliteral">"Overall Inference time: "</span> << std::setprecision(2)</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  << std::fixed << totalInferenceDuration.count() << <span class="stringliteral">" ms\n"</span>;</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> }</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="keywordtype">void</span> ArmNNExecutor::ExecuteSync()</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="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="l00155"></a><span class="lineno"> 155</span>  {</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  std::shared_ptr<armnn::IProfiler> profiler = m_Runtime->GetProfiler(m_NetworkId);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="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="l00159"></a><span class="lineno"> 159</span>  <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">armnn::Status</a> ret;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keywordflow">if</span> (m_Params.<a class="code" href="struct_execute_network_params.xhtml#a1697b9751b4ba381f89b8c81cd9dfc3c">m_ImportInputsIfAligned</a>)</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>  ret = m_Runtime->EnqueueWorkload(m_NetworkId,</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  m_InputTensorsVec[x],</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  m_OutputTensorsVec[x],</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  m_ImportedInputIds[x],</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  m_ImportedOutputIds[x]);</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  }</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keywordflow">else</span></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>  ret = m_Runtime->EnqueueWorkload(m_NetworkId,</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  m_InputTensorsVec[x],</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  m_OutputTensorsVec[x]);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  }</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> </div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keyword">const</span> <span class="keyword">auto</span> inferenceDuration = <a class="code" href="namespacearmnn.xhtml#a441621f00fd5665898c81a5ae3473c6b">armnn::GetTimeDuration</a>(start_time);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> </div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="comment">// If profiling is enabled print out the results</span></div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordflow">if</span>(profiler && profiler->IsProfilingEnabled())</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>  profiler->Print(std::cout);</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  }</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> </div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keywordflow">if</span>(ret == <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70bae139a585510a502bbf1841cf589f5086">armnn::Status::Failure</a>)</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">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">"IRuntime::EnqueueWorkload failed"</span>);</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> </div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordflow">if</span>(!m_Params.<a class="code" href="struct_execute_network_params.xhtml#ac828647e0c753c3727c8c1f81939f7e4">m_DontPrintOutputs</a>)</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  {</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  PrintOutputTensors(&m_OutputTensorsVec[x], x);</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>  <span class="comment">// If thresholdTime == 0.0 (default), then it hasn't been supplied at command line</span></div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#ab9be7e320a1879b362298cb198250dae">CheckInferenceTimeThreshold</a>(inferenceDuration, m_Params.<a class="code" href="struct_execute_network_params.xhtml#a318999172ae5197f56326b12d29104b7">m_ThresholdTime</a>);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  }</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> }</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> </div><div class="line"><a name="l00198"></a><span class="lineno"><a class="line" href="class_arm_n_n_executor.xhtml#a7b274ddaba15738d696359ad327a88ca"> 198</a></span> std::vector<const void*> <a class="code" href="class_arm_n_n_executor.xhtml#a7b274ddaba15738d696359ad327a88ca">ArmNNExecutor::Execute</a>()</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> {</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keywordflow">if</span>(m_Params.<a class="code" href="struct_execute_network_params.xhtml#a7adc5dcfe3d76ac489f253c4d5f439c8">m_ThreadPoolSize</a> == 0)</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  {</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  ExecuteSync();</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  }</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keywordflow">else</span></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>  ExecuteAsync();</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>  std::vector<const void*> results;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>& output : m_OutputStorage)</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>  results.push_back(output.m_Mem);</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  }</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>  <span class="keywordflow">return</span> results;</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> </div><div class="line"><a name="l00217"></a><span class="lineno"><a class="line" href="class_arm_n_n_executor.xhtml#a21887e76c6b67797ada411a163d81a62"> 217</a></span> <span class="keywordtype">void</span> <a class="code" href="class_arm_n_n_executor.xhtml#a21887e76c6b67797ada411a163d81a62">ArmNNExecutor::PrintNetworkInfo</a>()</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> {</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keyword">const</span> std::vector<std::string>& inputNames = m_Params.<a class="code" href="struct_execute_network_params.xhtml#aaf3c7f286030842a31025309ab6a8329">m_InputNames</a>.size() != 0 ?</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  m_Params.<a class="code" href="struct_execute_network_params.xhtml#aaf3c7f286030842a31025309ab6a8329">m_InputNames</a> :</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  m_IOInfo.m_InputNames;</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  std::stringstream ss;</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  ss << <span class="stringliteral">"===== Network Info =====\n"</span>;</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  ss << <span class="stringliteral">"Inputs in order:\n"</span>;</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& inputName : inputNames)</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="keyword">const</span> <span class="keyword">auto</span> inputInfo = m_IOInfo.m_InputInfoMap[inputName].second;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  ss << inputName << <span class="stringliteral">", "</span> << inputInfo.GetShape() << <span class="stringliteral">", "</span> << <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(inputInfo.GetDataType());</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keywordflow">if</span> (inputInfo.IsQuantized())</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  {</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  ss << <span class="stringliteral">" Quantization Offset: "</span> << inputInfo.GetQuantizationOffset();</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keywordflow">if</span> (inputInfo.HasMultipleQuantizationScales())</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>  ss << <span class="stringliteral">" Quantization scales: "</span>;</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> scale: inputInfo.GetQuantizationScales())</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  {</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  ss << scale << <span class="stringliteral">", "</span>;</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  }</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  }</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordflow">else</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>  ss << <span class="stringliteral">" Quantization scale: "</span> << inputInfo.GetQuantizationScale();</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>  ss << <span class="stringliteral">"\n"</span>;</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> </div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  ss << <span class="stringliteral">"Outputs in order:\n"</span>;</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& outputName : m_IOInfo.m_OutputNames)</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  {</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keyword">const</span> <span class="keyword">auto</span> outputInfo = m_IOInfo.m_OutputInfoMap[outputName].second;</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  ss << outputName << <span class="stringliteral">", "</span> << outputInfo.GetShape() << <span class="stringliteral">", "</span> << <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(outputInfo.GetDataType());</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keywordflow">if</span> (outputInfo.IsQuantized())</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  {</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  ss << <span class="stringliteral">" Quantization Offset: "</span> << outputInfo.GetQuantizationOffset();</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keywordflow">if</span> (outputInfo.HasMultipleQuantizationScales())</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  {</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  ss << <span class="stringliteral">" Quantization scales: "</span>;</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> scale: outputInfo.GetQuantizationScales())</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>  ss << scale << <span class="stringliteral">", "</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="keywordflow">else</span></div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  {</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  ss << <span class="stringliteral">" Quantization scale: "</span> << outputInfo.GetQuantizationScale();</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  }</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  }</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  ss << <span class="stringliteral">"\n"</span>;</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  }</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span> </div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  std::cout << ss.str() << std::endl;</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span> }</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> </div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span> <span class="keywordtype">void</span> ArmNNExecutor::SetupInputsAndOutputs()</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span> {</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> noOfInputs = m_IOInfo.m_InputNames.size();</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> </div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keywordflow">if</span> (m_Params.<a class="code" href="struct_execute_network_params.xhtml#aaf3c7f286030842a31025309ab6a8329">m_InputNames</a>.size() != 0 && m_Params.<a class="code" href="struct_execute_network_params.xhtml#aaf3c7f286030842a31025309ab6a8329">m_InputNames</a>.size() != noOfInputs)</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  {</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a>(<span class="stringliteral">"Number of input names does not match number of inputs"</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>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputFilePaths = m_Params.<a class="code" href="struct_execute_network_params.xhtml#ae3c3ae097395afea488cd3e0244269fa">m_InputTensorDataFilePaths</a>.size();</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="keyword">const</span> std::vector<std::string>& inputNames = m_Params.<a class="code" href="struct_execute_network_params.xhtml#aaf3c7f286030842a31025309ab6a8329">m_InputNames</a>.size() != 0 ?</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  m_Params.<a class="code" href="struct_execute_network_params.xhtml#aaf3c7f286030842a31025309ab6a8329">m_InputNames</a> :</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  m_IOInfo.m_InputNames;</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> noInputSets = 1;</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> </div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keywordflow">if</span> (inputFilePaths != 0)</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>  <span class="keywordflow">if</span> (inputFilePaths % noOfInputs != 0)</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  {</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a>(<span class="stringliteral">"Number of input files: "</span> + std::to_string(inputFilePaths) +</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="stringliteral">" not compatible with number of inputs: "</span> + std::to_string(noOfInputs));</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  }</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  noInputSets = inputFilePaths / noOfInputs;</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="keywordflow">if</span> (noInputSets != 1 && m_Params.<a class="code" href="struct_execute_network_params.xhtml#acb9376adc0f7174b7d4295e00315a084">m_ReuseBuffers</a>)</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  {</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a>(<span class="stringliteral">"Specifying multiple sets of inputs not compatible with ReuseBuffers"</span>);</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  }</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  }</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> </div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> noOfOutputs = m_IOInfo.m_OutputNames.size();</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputFilePaths = m_Params.<a class="code" href="struct_execute_network_params.xhtml#a74d346297c55b516908c541030adc88d">m_OutputTensorFiles</a>.size();</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> noOutputSets = 1;</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="keywordflow">if</span> (outputFilePaths != 0)</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  {</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keywordflow">if</span> (outputFilePaths % noOfOutputs != 0)</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  {</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a>(<span class="stringliteral">"Number of output files: "</span> + std::to_string(outputFilePaths) +</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="stringliteral">", not compatible with number of outputs: "</span> + std::to_string(noOfOutputs));</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  }</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  noOutputSets = outputFilePaths / noOfOutputs;</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> (noOutputSets != 1 && m_Params.<a class="code" href="struct_execute_network_params.xhtml#acb9376adc0f7174b7d4295e00315a084">m_ReuseBuffers</a>)</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  {</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a>(<span class="stringliteral">"Specifying multiple sets of outputs not compatible with ReuseBuffers"</span>);</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  }</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  }</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span> </div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <span class="keywordflow">if</span> (m_Params.<a class="code" href="struct_execute_network_params.xhtml#a7adc5dcfe3d76ac489f253c4d5f439c8">m_ThreadPoolSize</a> != 0)</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  {</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <span class="comment">// The current implementation of the Threadpool does not allow binding of outputs to a thread</span></div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <span class="comment">// So to ensure no two threads write to the same output at the same time, no output can be reused</span></div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  noOutputSets = m_Params.<a class="code" href="struct_execute_network_params.xhtml#a6e4eff6a5f40cb026ea76d3c13c96341">m_Iterations</a>;</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</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>  <span class="keywordflow">if</span> (m_Params.<a class="code" href="struct_execute_network_params.xhtml#ae3c3ae097395afea488cd3e0244269fa">m_InputTensorDataFilePaths</a>.size() > noOfInputs)</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  {</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>) << <span class="stringliteral">"Given network has "</span> << noOfInputs << <span class="stringliteral">" input/s. One input-tensor-data file is required "</span></div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  << <span class="stringliteral">"for each input. The user provided "</span></div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  << m_Params.<a class="code" href="struct_execute_network_params.xhtml#ae3c3ae097395afea488cd3e0244269fa">m_InputTensorDataFilePaths</a>.size()</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  << <span class="stringliteral">" input-tensor-data file/s which will be used to fill the input/s.\n"</span>;</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> </div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputCount = 0;</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSet = 0; inputSet < noInputSets; ++inputSet)</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>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">armnn::InputTensors</a> inputTensors;</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& inputName: inputNames)</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  {</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <a class="code" href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">armnn::BindingPointInfo</a> bindingPointInfo;</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  {</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  bindingPointInfo = m_IOInfo.m_InputInfoMap.at(inputName);</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>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> std::out_of_range& e)</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  {</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a>(<span class="stringliteral">"Input with inputName: "</span> + inputName + <span class="stringliteral">" not found."</span>);</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  }</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span> </div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& tensorInfo = bindingPointInfo.second;</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keyword">auto</span> newInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>{tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>(),</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>(),</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="keyword">true</span>};</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span> </div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  m_InputStorage.emplace_back(IOStorage{tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>()});</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span> </div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> bindingId = bindingPointInfo.first;</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  inputTensors.emplace_back(bindingId, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>{newInfo, m_InputStorage.back().m_Mem});</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> </div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <span class="keyword">const</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="l00366"></a><span class="lineno"> 366</span>  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a>() :</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  armnn::MakeOptional<std::string>(</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  m_Params.<a class="code" href="struct_execute_network_params.xhtml#ae3c3ae097395afea488cd3e0244269fa">m_InputTensorDataFilePaths</a>.at(inputCount++));</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span> </div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="keywordflow">switch</span> (tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>())</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  {</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  {</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="keyword">auto</span> typedTensor = <span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span>*<span class="keyword">></span>(m_InputStorage.back().m_Mem);</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  PopulateTensorWithData<float>(typedTensor, tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(), dataFile, inputName);</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  }</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>:</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  {</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="keyword">auto</span> typedTensor = <span class="keyword">reinterpret_cast<</span>int16_t*<span class="keyword">></span>(m_InputStorage.back().m_Mem);</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  PopulateTensorWithData<int16_t>(typedTensor, tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(), dataFile, inputName);</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  }</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>:</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>:</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  {</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="keyword">auto</span> typedTensor = <span class="keyword">reinterpret_cast<</span>int8_t*<span class="keyword">></span>(m_InputStorage.back().m_Mem);</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  PopulateTensorWithData<int8_t>(typedTensor, tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(), dataFile, inputName);</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  }</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  {</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="keyword">auto</span> typedTensor = <span class="keyword">reinterpret_cast<</span>uint8_t*<span class="keyword">></span>(m_InputStorage.back().m_Mem);</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  PopulateTensorWithData<uint8_t>(typedTensor, tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(), dataFile, inputName);</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  }</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>:</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  {</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="keyword">auto</span> typedTensor = <span class="keyword">reinterpret_cast<</span>int32_t*<span class="keyword">></span>(m_InputStorage.back().m_Mem);</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  PopulateTensorWithData<int32_t>(typedTensor, tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(), dataFile, inputName);</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  }</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  {</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a>(<span class="stringliteral">"Unexpected DataType"</span>);</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  }</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  }</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span> </div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="keywordflow">if</span> (m_Params.<a class="code" href="struct_execute_network_params.xhtml#a1697b9751b4ba381f89b8c81cd9dfc3c">m_ImportInputsIfAligned</a>)</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  {</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  m_ImportedInputIds.push_back(</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  m_Runtime->ImportInputs(m_NetworkId, m_InputTensorsVec.back(), <a class="code" href="namespacearmnn.xhtml#a14fcd7f88d11cea0a018269dca5f9277a1131a914388fac73e5f07b0ba0aad523">armnn::MemorySource::Malloc</a>));</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>  m_InputTensorsVec.emplace_back(inputTensors);</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> </div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSet = 0; outputSet < noOutputSets; ++outputSet)</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>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">armnn::OutputTensors</a> outputTensors;</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& output: m_IOInfo.m_OutputInfoMap)</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  {</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">armnn::BindingPointInfo</a>& bindingPointInfo = output.second;</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& tensorInfo = bindingPointInfo.second;</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span> </div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  m_OutputStorage.emplace_back(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  outputTensors.emplace_back(bindingPointInfo.first, <a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>{tensorInfo, m_OutputStorage.back().m_Mem});</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  }</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  m_OutputTensorsVec.emplace_back(outputTensors);</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="keywordflow">if</span> (m_Params.<a class="code" href="struct_execute_network_params.xhtml#a1697b9751b4ba381f89b8c81cd9dfc3c">m_ImportInputsIfAligned</a>)</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  {</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  m_ImportedOutputIds.push_back(</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  m_Runtime->ImportOutputs(m_NetworkId, m_OutputTensorsVec.back(), <a class="code" href="namespacearmnn.xhtml#a14fcd7f88d11cea0a018269dca5f9277a1131a914388fac73e5f07b0ba0aad523">armnn::MemorySource::Malloc</a>));</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  }</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  }</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span> </div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <span class="comment">// Fill the remaining iterations with copies</span></div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> remainingInputSets = m_Params.<a class="code" href="struct_execute_network_params.xhtml#a6e4eff6a5f40cb026ea76d3c13c96341">m_Iterations</a> - noInputSets;</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 1; i <= remainingInputSets; i++)</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  {</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  m_InputTensorsVec.push_back(m_InputTensorsVec[noInputSets % i]);</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <span class="keywordflow">if</span> (m_Params.<a class="code" href="struct_execute_network_params.xhtml#a1697b9751b4ba381f89b8c81cd9dfc3c">m_ImportInputsIfAligned</a>)</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>  m_ImportedInputIds.push_back(m_ImportedInputIds[noInputSets % i]);</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  }</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  }</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span> </div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> remainingOutputSets = m_Params.<a class="code" href="struct_execute_network_params.xhtml#a6e4eff6a5f40cb026ea76d3c13c96341">m_Iterations</a> - noOutputSets;</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 1; i <= remainingOutputSets; i++)</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>  m_OutputTensorsVec.push_back(m_OutputTensorsVec[noOutputSets % i]);</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="keywordflow">if</span> (m_Params.<a class="code" href="struct_execute_network_params.xhtml#a1697b9751b4ba381f89b8c81cd9dfc3c">m_ImportInputsIfAligned</a>)</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  {</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  m_ImportedOutputIds.push_back(m_ImportedOutputIds[noOutputSets % i]);</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  }</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  }</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> }</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span> </div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span> ArmNNExecutor::IOInfo ArmNNExecutor::GetIOInfo(<a class="code" href="classarmnn_1_1_i_optimized_network.xhtml">armnn::IOptimizedNetwork</a>* optNet)</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>  <span class="keyword">struct </span>IOStrategy : <a class="code" href="classarmnn_1_1_i_strategy.xhtml">armnn::IStrategy</a></div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  {</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="keywordtype">void</span> ExecuteStrategy(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span> = 0)<span class="keyword"> override</span></div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(descriptor, constants, <span class="keywordtype">id</span>);</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <span class="keywordflow">switch</span> (layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">GetType</a>())</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  {</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a>:</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  {</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  m_IOInfo.m_InputNames.emplace_back(name);</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  m_IOInfo.m_InputInfoMap[name] = {id, layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>()};</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  }</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>:</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  {</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  m_IOInfo.m_OutputNames.emplace_back(name);</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  m_IOInfo.m_OutputInfoMap[name] = {id, layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">GetConnection</a>()-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>()};</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  }</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="keywordflow">default</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>  }</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  IOInfo m_IOInfo;</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> </div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  IOStrategy ioStrategy;</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  optNet-><a class="code" href="classarmnn_1_1_i_optimized_network.xhtml#a72032c65bf8b8acf09b564b7d80078c5">ExecuteStrategy</a>(ioStrategy);</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span> </div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  <span class="keywordflow">return</span> ioStrategy.m_IOInfo;</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span> }</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span> </div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span> <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a> ArmNNExecutor::OptimizeNetwork(<a class="code" href="classarmnn_1_1_i_network.xhtml">armnn::INetwork</a>* network)</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>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a> optNet{<span class="keyword">nullptr</span>, [](<a class="code" href="classarmnn_1_1_i_optimized_network.xhtml">armnn::IOptimizedNetwork</a>*){}};</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span> </div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <a class="code" href="structarmnn_1_1_optimizer_options.xhtml">armnn::OptimizerOptions</a> options;</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  options.<a class="code" href="structarmnn_1_1_optimizer_options.xhtml#a6e1a42622ca43dafc7ba8e684c016eb4">m_ReduceFp32ToFp16</a> = m_Params.m_EnableFp16TurboMode;</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  options.<a class="code" href="structarmnn_1_1_optimizer_options.xhtml#a11f463726addcc1d2845266997d79e9c">m_ReduceFp32ToBf16</a> = m_Params.m_EnableBf16TurboMode;</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  options.<a class="code" href="structarmnn_1_1_optimizer_options.xhtml#a69eb14082d40fa0a3cff50457344a5e0">m_Debug</a> = m_Params.m_PrintIntermediate;</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  options.<a class="code" href="structarmnn_1_1_optimizer_options.xhtml#a9416d94a8aad7cdfa47eb35e825cbda5">m_shapeInferenceMethod</a> = m_Params.m_InferOutputShape ?</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  <a class="code" href="namespacearmnn.xhtml#adf57837d00e8352d9b5cc5ab1fb5fee9a5dd7c525cb1500a2181fd4cc079d7acb">armnn::ShapeInferenceMethod::InferAndValidate</a> :</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  <a class="code" href="namespacearmnn.xhtml#adf57837d00e8352d9b5cc5ab1fb5fee9af6486a22a9bb11959bfae60a3e5174b1">armnn::ShapeInferenceMethod::ValidateOnly</a>;</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  options.<a class="code" href="structarmnn_1_1_optimizer_options.xhtml#a1b1892da2aaf7eaedaa38671d56b7f19">m_ProfilingEnabled</a> = m_Params.m_EnableProfiling;</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span> </div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <a class="code" href="structarmnn_1_1_backend_options.xhtml">armnn::BackendOptions</a> gpuAcc(<span class="stringliteral">"GpuAcc"</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="stringliteral">"FastMathEnabled"</span>, m_Params.m_EnableFastMath },</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  { <span class="stringliteral">"SaveCachedNetwork"</span>, m_Params.m_SaveCachedNetwork },</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  { <span class="stringliteral">"CachedNetworkFilePath"</span>, m_Params.m_CachedNetworkFilePath },</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  { <span class="stringliteral">"MLGOTuningFilePath"</span>, m_Params.m_MLGOTuningFilePath }</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  });</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span> </div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <a class="code" href="structarmnn_1_1_backend_options.xhtml">armnn::BackendOptions</a> cpuAcc(<span class="stringliteral">"CpuAcc"</span>,</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  {</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  { <span class="stringliteral">"FastMathEnabled"</span>, m_Params.m_EnableFastMath },</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  { <span class="stringliteral">"NumberOfThreads"</span>, m_Params.m_NumberOfThreads }</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>  options.<a class="code" href="structarmnn_1_1_optimizer_options.xhtml#a71194277c60153a5f86539f5d39f01db">m_ModelOptions</a>.push_back(gpuAcc);</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  options.<a class="code" href="structarmnn_1_1_optimizer_options.xhtml#a71194277c60153a5f86539f5d39f01db">m_ModelOptions</a>.push_back(cpuAcc);</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>  <span class="keyword">const</span> <span class="keyword">auto</span> optimization_start_time = <a class="code" href="namespacearmnn.xhtml#ac895f6f6897ce335b7b433201bae0b48">armnn::GetTimeNow</a>();</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a>(*network, m_Params.m_ComputeDevices, m_Runtime->GetDeviceSpec(), options);</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span> </div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>) << <span class="stringliteral">"Optimization time: "</span> << std::setprecision(2)</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  << std::fixed << <a class="code" href="namespacearmnn.xhtml#a441621f00fd5665898c81a5ae3473c6b">armnn::GetTimeDuration</a>(optimization_start_time).count() << <span class="stringliteral">" ms\n"</span>;</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span> </div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="keywordflow">if</span> (!optNet)</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>  <a class="code" href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a>(<span class="stringliteral">"Optimize returned nullptr"</span>);</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">// If v,visualize-optimized-model is enabled then construct a file name for the dot file.</span></div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="keywordflow">if</span> (m_Params.m_EnableLayerDetails)</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  {</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  fs::path filename = m_Params.m_ModelPath;</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  filename.replace_extension(<span class="stringliteral">"dot"</span>);</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  std::fstream file(filename.c_str(), std::ios_base::out);</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  optNet-><a class="code" href="classarmnn_1_1_i_optimized_network.xhtml#a26794f014974a6f963a8925de07bffeb">SerializeToDot</a>(file);</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  }</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span> </div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <span class="keywordflow">return</span> optNet;</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span> }</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span> </div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span> std::unique_ptr<ArmNNExecutor::IParser> ArmNNExecutor::CreateParser()</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span> {</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <span class="comment">// If no model format is given check the file name</span></div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  <span class="keyword">const</span> std::string& modelFormat = m_Params.m_ModelPath;</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span> </div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  m_Params.m_IsModelBinary = modelFormat.find(<span class="stringliteral">"json"</span>) == std::string::npos ? true : <span class="keyword">false</span>;</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  std::unique_ptr<IParser> parser = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  <span class="comment">// Forward to implementation based on the parser type</span></div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <span class="keywordflow">if</span> (modelFormat.find(<span class="stringliteral">"armnn"</span>) != std::string::npos)</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  {</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span> <span class="preprocessor">#if defined(ARMNN_SERIALIZER)</span></div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  parser = std::make_unique<ArmNNDeserializer>();</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span> <span class="preprocessor">#else</span></div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a>(<span class="stringliteral">"Not built with serialization support."</span>);</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  }</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(modelFormat.find(<span class="stringliteral">"tflite"</span>) != std::string::npos)</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  {</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span> <span class="preprocessor">#if defined(ARMNN_TF_LITE_PARSER)</span></div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  parser = std::make_unique<TfliteParser>(m_Params);</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span> <span class="preprocessor">#else</span></div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a>(<span class="stringliteral">"Not built with Tensorflow-Lite parser support."</span>);</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  }</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (modelFormat.find(<span class="stringliteral">"onnx"</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> <span class="preprocessor">#if defined(ARMNN_ONNX_PARSER)</span></div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  parser = std::make_unique<OnnxParser>();</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span> <span class="preprocessor">#else</span></div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a>(<span class="stringliteral">"Not built with Onnx parser support."</span>);</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span> <span class="preprocessor">#endif</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> </div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  <span class="keywordflow">return</span> parser;</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span> }</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span> </div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span> <span class="keywordtype">void</span> ArmNNExecutor::PrintOutputTensors(<span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">armnn::OutputTensors</a>* outputTensors,</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> iteration)</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span> {</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <span class="keyword">auto</span> findOutputName = [&](<span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> id)</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  {</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> it = m_IOInfo.m_OutputInfoMap.begin(); it != m_IOInfo.m_OutputInfoMap.end(); ++it)</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>  <span class="keywordflow">if</span> (<span class="keywordtype">id</span> == it->second.first)</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  {</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <span class="keywordflow">return</span> it->first;</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>  }</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  <span class="keywordflow">return</span> std::string{};</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputIndex = 0;</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numOutputs = outputTensors->size();</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& output: *outputTensors)</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  {</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  <span class="keyword">const</span> <span class="keyword">auto</span> bindingName = findOutputName(output.first);</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  <span class="comment">// We've made sure before that the number of output files either equals numOutputs, in which</span></div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <span class="comment">// case we override those files when processing the results of each iteration (only the result</span></div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <span class="comment">// of the last iteration will be stored), or there are enough</span></div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <span class="comment">// output files for each output of each iteration.</span></div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  <span class="keywordtype">size_t</span> outputFileIndex = iteration * numOutputs + outputIndex;</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  <span class="keywordflow">if</span> (!m_Params.m_OutputTensorFiles.empty())</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>  outputFileIndex = outputFileIndex % m_Params.m_OutputTensorFiles.size();</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>) << <span class="stringliteral">"Writing output: "</span> << bindingName << <span class="stringliteral">" bindingId: '"</span></div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  << output.first</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  << <span class="stringliteral">"' of iteration: "</span> << iteration + 1 << <span class="stringliteral">" to file: '"</span></div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  << m_Params.m_OutputTensorFiles[outputFileIndex] << <span class="stringliteral">"'"</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> </div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<std::string></a> outputTensorFile = m_Params.m_OutputTensorFiles.empty() ?</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a>() :</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  armnn::MakeOptional<std::string>(</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  m_Params.m_OutputTensorFiles[outputFileIndex]);</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span> </div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  <a class="code" href="struct_output_write_info.xhtml">OutputWriteInfo</a> outputWriteInfo</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  {</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  outputTensorFile,</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  bindingName,</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  output.second,</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  !m_Params.m_DontPrintOutputs</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  };</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>  std::cout << bindingName << <span class="stringliteral">": "</span>;</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  std::vector<float> values;</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  <span class="keywordflow">switch</span> (output.second.GetDataType())</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">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</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>  PrintTensor<float>(outputWriteInfo, <span class="stringliteral">"%f "</span>);</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <span class="keywordflow">break</span>;</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> </div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>:</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  {</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  PrintTensor<int>(outputWriteInfo, <span class="stringliteral">"%d "</span>);</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  }</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>:</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>:</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  {</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  PrintTensor<int8_t>(outputWriteInfo, <span class="stringliteral">"%d "</span>);</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  <span class="keywordflow">break</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  {</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  PrintTensor<uint8_t>(outputWriteInfo, <span class="stringliteral">"%d "</span>);</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  }</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a>:</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>:</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>:</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>:</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::DataType::Signed64</a>:</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  {</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a>(<span class="stringliteral">"Unexpected DataType"</span>);</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  }</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  }</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  std::cout << <span class="stringliteral">"\n"</span>;</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  }</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span> }</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span> </div><div class="line"><a name="l00671"></a><span class="lineno"><a class="line" href="class_arm_n_n_executor.xhtml#ac65a3d900d923c4582e059c2281e70e3"> 671</a></span> <span class="keywordtype">void</span> <a class="code" href="class_arm_n_n_executor.xhtml#ac65a3d900d923c4582e059c2281e70e3">ArmNNExecutor::CompareAndPrintResult</a>(std::vector<const void*> otherOutput)</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span> {</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> index = 0;</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span> </div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& outputTensors: m_OutputTensorsVec)</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  {</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& outputTensor: outputTensors)</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  {</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  <span class="keywordtype">float</span> result = 0;</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  <span class="keywordtype">size_t</span> size = outputTensor.second.GetNumBytes();</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span> </div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  <span class="keywordflow">switch</span> (outputTensor.second.GetDataType())</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  {</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</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>  result = ComputeRMSE<float>(outputTensor.second.GetMemoryArea(), otherOutput[index++], size);</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  <span class="keywordflow">break</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="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>:</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  {</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  result = ComputeRMSE<int16_t>(outputTensor.second.GetMemoryArea(), otherOutput[index++], size);</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  }</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>:</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>:</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  {</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  result = ComputeRMSE<int8_t>(outputTensor.second.GetMemoryArea(), otherOutput[index++], size);</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  }</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  {</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  result = ComputeRMSE<uint8_t>(outputTensor.second.GetMemoryArea(), otherOutput[index++], size);</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  }</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  {</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  <a class="code" href="_network_execution_utils_8cpp.xhtml#a0d853d3a7b138f39cc775c26e2c0821a">LogAndThrow</a>(<span class="stringliteral">"Unexpected DataType"</span>);</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  }</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  }</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  std::cout << <span class="stringliteral">"RMSE: of "</span> << result << <span class="stringliteral">"\n"</span>;</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  }</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  }</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span> }</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span> <span class="preprocessor">#if defined(ARMNN_SERIALIZER)</span></div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span> ArmNNExecutor::ArmNNDeserializer::ArmNNDeserializer() : m_Parser(<a class="code" href="classarmnn_deserializer_1_1_i_deserializer.xhtml#af116abd698a7feb92876ae48917005a4">armnnDeserializer::IDeserializer::Create</a>()){}</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> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> ArmNNExecutor::ArmNNDeserializer::CreateNetwork(<span class="keyword">const</span> <a class="code" href="struct_execute_network_params.xhtml">ExecuteNetworkParams</a>& params)</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="keyword">const</span> std::string& modelPath = params.<a class="code" href="struct_execute_network_params.xhtml#a4fa312cf0d60fbd3988a7c76ab8e2980">m_ModelPath</a>;</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span> </div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  std::ifstream file(modelPath, std::ios::binary);</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  <span class="keywordflow">return</span> m_Parser->CreateNetworkFromBinary(file);</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span> }</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span> </div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span> <a class="code" href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">armnn::BindingPointInfo</a></div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span> ArmNNExecutor::ArmNNDeserializer::GetInputBindingPointInfo(<span class="keywordtype">size_t</span>, <span class="keyword">const</span> std::string& inputName)</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>  <a class="code" href="structarmnn_deserializer_1_1_binding_point_info.xhtml">armnnDeserializer::BindingPointInfo</a> DeserializerBPI = m_Parser->GetNetworkInputBindingInfo(0, inputName);</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  <span class="keywordflow">return</span> {DeserializerBPI.<a class="code" href="structarmnn_deserializer_1_1_binding_point_info.xhtml#a663b3104ec65e4e08b5e37fb42942087">m_BindingId</a>, DeserializerBPI.<a class="code" href="structarmnn_deserializer_1_1_binding_point_info.xhtml#aa308d10e76e29f09e44a933a2d091a79">m_TensorInfo</a>};</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> </div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span> <a class="code" href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">armnn::BindingPointInfo</a></div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span> ArmNNExecutor::ArmNNDeserializer::GetOutputBindingPointInfo(<span class="keywordtype">size_t</span>, <span class="keyword">const</span> std::string& outputName)</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span> {</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  <a class="code" href="structarmnn_deserializer_1_1_binding_point_info.xhtml">armnnDeserializer::BindingPointInfo</a> DeserializerBPI = m_Parser->GetNetworkOutputBindingInfo(0, outputName);</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  <span class="keywordflow">return</span> {DeserializerBPI.<a class="code" href="structarmnn_deserializer_1_1_binding_point_info.xhtml#a663b3104ec65e4e08b5e37fb42942087">m_BindingId</a>, DeserializerBPI.<a class="code" href="structarmnn_deserializer_1_1_binding_point_info.xhtml#aa308d10e76e29f09e44a933a2d091a79">m_TensorInfo</a>};</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span> }</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span> </div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span> <span class="preprocessor">#if defined(ARMNN_TF_LITE_PARSER)</span></div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span> ArmNNExecutor::TfliteParser::TfliteParser(<span class="keyword">const</span> <a class="code" href="struct_execute_network_params.xhtml">ExecuteNetworkParams</a>& params)</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span> {</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  <a class="code" href="structarmnn_tf_lite_parser_1_1_i_tf_lite_parser_1_1_tf_lite_parser_options.xhtml">armnnTfLiteParser::ITfLiteParser::TfLiteParserOptions</a> options;</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  options.<a class="code" href="structarmnn_tf_lite_parser_1_1_i_tf_lite_parser_1_1_tf_lite_parser_options.xhtml#a8675330934407606641327cec4bb29f4">m_StandInLayerForUnsupported</a> = params.<a class="code" href="struct_execute_network_params.xhtml#a5c7f0c083da98e7b6e9ba79d2fcd985d">m_ParseUnsupported</a>;</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  options.<a class="code" href="structarmnn_tf_lite_parser_1_1_i_tf_lite_parser_1_1_tf_lite_parser_options.xhtml#ad9e70e3709afbaa45bf9c3cfa0148b2b">m_InferAndValidate</a> = params.<a class="code" href="struct_execute_network_params.xhtml#a2a245a63e87f363df491ad8c35be54c5">m_InferOutputShape</a>;</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span> </div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  m_Parser = <a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a9932449a89a62cfcfd72a4fedbee1ab7">armnnTfLiteParser::ITfLiteParser::Create</a>(options);</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"> 750</span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> ArmNNExecutor::TfliteParser::CreateNetwork(<span class="keyword">const</span> <a class="code" href="struct_execute_network_params.xhtml">ExecuteNetworkParams</a>& params)</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span> {</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  <span class="keyword">const</span> std::string& modelPath = params.<a class="code" href="struct_execute_network_params.xhtml#a4fa312cf0d60fbd3988a7c76ab8e2980">m_ModelPath</a>;</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <span class="keywordflow">return</span> m_Parser->CreateNetworkFromBinaryFile(modelPath.c_str());</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span> }</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span> </div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span> <a class="code" href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">armnn::BindingPointInfo</a> ArmNNExecutor::TfliteParser::GetInputBindingPointInfo(<span class="keywordtype">size_t</span> subgraphId,</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  <span class="keyword">const</span> std::string& inputName)</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span> {</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  <span class="keywordflow">return</span> m_Parser->GetNetworkInputBindingInfo(subgraphId, inputName);</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span> }</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span> </div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span> <a class="code" href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">armnn::BindingPointInfo</a> ArmNNExecutor::TfliteParser::GetOutputBindingPointInfo(<span class="keywordtype">size_t</span> subgraphId,</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  <span class="keyword">const</span> std::string& outputName)</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span> {</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  <span class="keywordflow">return</span> m_Parser->GetNetworkOutputBindingInfo(subgraphId, outputName);</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span> }</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span> </div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span> </div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span> <span class="preprocessor">#if defined(ARMNN_ONNX_PARSER)</span></div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span> ArmNNExecutor::OnnxParser::OnnxParser() : m_Parser(<a class="code" href="classarmnn_onnx_parser_1_1_i_onnx_parser.xhtml#af9b9254fb8a084f0db4f7deff0498b20">armnnOnnxParser::IOnnxParser::Create</a>()){}</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span> </div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> ArmNNExecutor::OnnxParser::CreateNetwork(<span class="keyword">const</span> <a class="code" href="struct_execute_network_params.xhtml">ExecuteNetworkParams</a>& params)</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>  <span class="keyword">const</span> std::string& modelPath = params.<a class="code" href="struct_execute_network_params.xhtml#a4fa312cf0d60fbd3988a7c76ab8e2980">m_ModelPath</a>;</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  m_Parser = <a class="code" href="classarmnn_onnx_parser_1_1_i_onnx_parser.xhtml#af9b9254fb8a084f0db4f7deff0498b20">armnnOnnxParser::IOnnxParser::Create</a>();</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  std::map<std::string, armnn::TensorShape> inputShapes;</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  <span class="keywordflow">if</span>(!params.<a class="code" href="struct_execute_network_params.xhtml#aefe5049ae533ea46d0ddaee6be93f646">m_InputTensorShapes</a>.empty())</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  {</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> numInputShapes = params.<a class="code" href="struct_execute_network_params.xhtml#aefe5049ae533ea46d0ddaee6be93f646">m_InputTensorShapes</a>.size();</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> numInputBindings = params.<a class="code" href="struct_execute_network_params.xhtml#aaf3c7f286030842a31025309ab6a8329">m_InputNames</a>.size();</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  <span class="keywordflow">if</span>(numInputShapes < numInputBindings)</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  {</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  fmt::format(<span class="stringliteral">"Not every input has its tensor shape specified: expected={0}, got={1}"</span>,</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  numInputBindings, numInputShapes));</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  }</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span> </div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < numInputShapes; i++)</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  {</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  inputShapes[params.<a class="code" href="struct_execute_network_params.xhtml#aaf3c7f286030842a31025309ab6a8329">m_InputNames</a>[i]] = params.<a class="code" href="struct_execute_network_params.xhtml#aefe5049ae533ea46d0ddaee6be93f646">m_InputTensorShapes</a>[i];</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  }</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span> </div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  <span class="keywordflow">return</span> params.<a class="code" href="struct_execute_network_params.xhtml#a6bf2f586c403977d31c7d32d371918cf">m_IsModelBinary</a> ?</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  m_Parser->CreateNetworkFromBinaryFile(modelPath.c_str(), inputShapes) :</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  m_Parser->CreateNetworkFromTextFile(modelPath.c_str(), inputShapes);</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  }</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span> </div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  <span class="comment">// Handle text and binary input differently by calling the corresponding parser function</span></div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  <span class="keywordflow">return</span> params.<a class="code" href="struct_execute_network_params.xhtml#a6bf2f586c403977d31c7d32d371918cf">m_IsModelBinary</a> ?</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  m_Parser->CreateNetworkFromBinaryFile(params.<a class="code" href="struct_execute_network_params.xhtml#a4fa312cf0d60fbd3988a7c76ab8e2980">m_ModelPath</a>.c_str()) :</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  m_Parser->CreateNetworkFromTextFile(params.<a class="code" href="struct_execute_network_params.xhtml#a4fa312cf0d60fbd3988a7c76ab8e2980">m_ModelPath</a>.c_str());</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span> }</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span> </div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span> <a class="code" href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">armnn::BindingPointInfo</a> ArmNNExecutor::OnnxParser::GetInputBindingPointInfo(<span class="keywordtype">size_t</span>, <span class="keyword">const</span> std::string& inputName)</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span> {</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  <span class="keywordflow">return</span> m_Parser->GetNetworkInputBindingInfo(inputName);</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span> }</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span> </div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span> <a class="code" href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">armnn::BindingPointInfo</a> ArmNNExecutor::OnnxParser::GetOutputBindingPointInfo(<span class="keywordtype">size_t</span>, <span class="keyword">const</span> std::string& outputName)</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span> {</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  <span class="keywordflow">return</span> m_Parser->GetNetworkOutputBindingInfo(outputName);</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span> }</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span> <span class="preprocessor">#endif</span></div><div class="ttc" id="structarmnn_1_1_optimizer_options_xhtml_a71194277c60153a5f86539f5d39f01db"><div class="ttname"><a href="structarmnn_1_1_optimizer_options.xhtml#a71194277c60153a5f86539f5d39f01db">armnn::OptimizerOptions::m_ModelOptions</a></div><div class="ttdeci">ModelOptions m_ModelOptions</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00227">INetwork.hpp:227</a></div></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="classarmnn_1_1_i_runtime_xhtml_ad44ecd3700748dc30dc4bbe34ba5bde7"><div class="ttname"><a href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a></div><div class="ttdeci">static IRuntimePtr Create(const CreationOptions &options)</div><div class="ttdef"><b>Definition:</b> <a href="_runtime_8cpp_source.xhtml#l00049">Runtime.cpp:49</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00068">INetwork.hpp:68</a></div></div>
<div class="ttc" id="_i_async_execution_callback_8hpp_xhtml"><div class="ttname"><a href="_i_async_execution_callback_8hpp.xhtml">IAsyncExecutionCallback.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a></div></div>
<div class="ttc" id="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="structarmnn_1_1_optimizer_options_xhtml_a9416d94a8aad7cdfa47eb35e825cbda5"><div class="ttname"><a href="structarmnn_1_1_optimizer_options.xhtml#a9416d94a8aad7cdfa47eb35e825cbda5">armnn::OptimizerOptions::m_shapeInferenceMethod</a></div><div class="ttdeci">ShapeInferenceMethod m_shapeInferenceMethod</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00221">INetwork.hpp:221</a></div></div>
<div class="ttc" id="classarmnn_1_1experimental_1_1_async_callback_manager_xhtml_a27aff9e4e5d9bce49988a2de6a1ebc59"><div class="ttname"><a href="classarmnn_1_1experimental_1_1_async_callback_manager.xhtml#a27aff9e4e5d9bce49988a2de6a1ebc59">armnn::experimental::AsyncCallbackManager::GetNewCallback</a></div><div class="ttdeci">std::shared_ptr< AsyncExecutionCallback > GetNewCallback()</div><div class="ttdef"><b>Definition:</b> <a href="_async_execution_callback_8cpp_source.xhtml#l00046">AsyncExecutionCallback.cpp:46</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00191">Tensor.hpp:191</a></div></div>
<div class="ttc" id="structarmnn_tf_lite_parser_1_1_i_tf_lite_parser_1_1_tf_lite_parser_options_xhtml"><div class="ttname"><a href="structarmnn_tf_lite_parser_1_1_i_tf_lite_parser_1_1_tf_lite_parser_options.xhtml">armnnTfLiteParser::ITfLiteParser::TfLiteParserOptions</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_tf_lite_parser_8hpp_source.xhtml#l00029">ITfLiteParser.hpp:29</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_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="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_ab17deb382179697b4702cc4f909e71f8"><div class="ttname"><a href="struct_execute_network_params.xhtml#ab17deb382179697b4702cc4f909e71f8">ExecuteNetworkParams::m_OutputDetailsOnlyToStdOut</a></div><div class="ttdeci">bool m_OutputDetailsOnlyToStdOut</div><div class="ttdef"><b>Definition:</b> <a href="_execute_network_params_8hpp_source.xhtml#l00048">ExecuteNetworkParams.hpp:48</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div>
<div class="ttc" id="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="_execute_network_params_8hpp_source.xhtml#l00031">ExecuteNetworkParams.hpp:31</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_abcbdfb544ece4c31d0b37715ad0f3be0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">armnn::TensorInfo::GetNumBytes</a></div><div class="ttdeci">unsigned int GetNumBytes() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00427">Tensor.cpp:427</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae060224135f57f926cbda9d2732a2b1fa566666dfc3a9a82da0d7b0816b19f278"><div class="ttname"><a href="namespacearmnn.xhtml#ae060224135f57f926cbda9d2732a2b1fa566666dfc3a9a82da0d7b0816b19f278">armnn::ProfilingDetailsMethod::DetailsOnly</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_aefe5049ae533ea46d0ddaee6be93f646"><div class="ttname"><a href="struct_execute_network_params.xhtml#aefe5049ae533ea46d0ddaee6be93f646">ExecuteNetworkParams::m_InputTensorShapes</a></div><div class="ttdeci">std::vector< armnn::TensorShape > m_InputTensorShapes</div><div class="ttdef"><b>Definition:</b> <a href="_execute_network_params_8hpp_source.xhtml#l00043">ExecuteNetworkParams.hpp:43</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="classarmnn_1_1_i_network_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml">armnn::INetwork</a></div><div class="ttdoc">Main network class which provides the interface for building up a neural network. ...</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00246">INetwork.hpp:246</a></div></div>
<div class="ttc" id="structarmnn_tf_lite_parser_1_1_i_tf_lite_parser_1_1_tf_lite_parser_options_xhtml_ad9e70e3709afbaa45bf9c3cfa0148b2b"><div class="ttname"><a href="structarmnn_tf_lite_parser_1_1_i_tf_lite_parser_1_1_tf_lite_parser_options.xhtml#ad9e70e3709afbaa45bf9c3cfa0148b2b">armnnTfLiteParser::ITfLiteParser::TfLiteParserOptions::m_InferAndValidate</a></div><div class="ttdeci">bool m_InferAndValidate</div><div class="ttdef"><b>Definition:</b> <a href="_i_tf_lite_parser_8hpp_source.xhtml#l00038">ITfLiteParser.hpp:38</a></div></div>
<div class="ttc" id="classarmnn_deserializer_1_1_i_deserializer_xhtml_af116abd698a7feb92876ae48917005a4"><div class="ttname"><a href="classarmnn_deserializer_1_1_i_deserializer.xhtml#af116abd698a7feb92876ae48917005a4">armnnDeserializer::IDeserializer::Create</a></div><div class="ttdeci">static IDeserializerPtr Create()</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8cpp_source.xhtml#l00047">Deserializer.cpp:47</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a></div></div>
<div class="ttc" id="structarmnn_1_1_optimizer_options_xhtml_a11f463726addcc1d2845266997d79e9c"><div class="ttname"><a href="structarmnn_1_1_optimizer_options.xhtml#a11f463726addcc1d2845266997d79e9c">armnn::OptimizerOptions::m_ReduceFp32ToBf16</a></div><div class="ttdeci">bool m_ReduceFp32ToBf16</div><div class="ttdoc">Reduces all Fp32 operators in the model to Bf16 for faster processing. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00218">INetwork.hpp:218</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_aa01bce88f89975a5a031db4cc8861527"><div class="ttname"><a href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">armnn::InputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class ConstTensor > > InputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00392">Tensor.hpp:392</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::DataType::Signed64</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div>
<div class="ttc" id="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="class_arm_n_n_executor_xhtml_ac65a3d900d923c4582e059c2281e70e3"><div class="ttname"><a href="class_arm_n_n_executor.xhtml#ac65a3d900d923c4582e059c2281e70e3">ArmNNExecutor::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="_arm_n_n_executor_8cpp_source.xhtml#l00671">ArmNNExecutor.cpp:671</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_abf3cb45be3828b72b4ac08f87ac6c779"><div class="ttname"><a href="struct_execute_network_params.xhtml#abf3cb45be3828b72b4ac08f87ac6c779">ExecuteNetworkParams::m_Concurrent</a></div><div class="ttdeci">bool m_Concurrent</div><div class="ttdef"><b>Definition:</b> <a href="_execute_network_params_8hpp_source.xhtml#l00029">ExecuteNetworkParams.hpp:29</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__quick__start_8dox_source.xhtml#l00006">01_00_quick_start.dox:6</a></div></div>
<div class="ttc" id="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="_execute_network_params_8hpp_source.xhtml#l00051">ExecuteNetworkParams.hpp:51</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="struct_execute_network_params_xhtml_a7adc5dcfe3d76ac489f253c4d5f439c8"><div class="ttname"><a href="struct_execute_network_params.xhtml#a7adc5dcfe3d76ac489f253c4d5f439c8">ExecuteNetworkParams::m_ThreadPoolSize</a></div><div class="ttdeci">size_t m_ThreadPoolSize</div><div class="ttdef"><b>Definition:</b> <a href="_execute_network_params_8hpp_source.xhtml#l00062">ExecuteNetworkParams.hpp:62</a></div></div>
<div class="ttc" id="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser_xhtml_a9932449a89a62cfcfd72a4fedbee1ab7"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a9932449a89a62cfcfd72a4fedbee1ab7">armnnTfLiteParser::ITfLiteParser::Create</a></div><div class="ttdeci">static ITfLiteParserPtr Create(const armnn::Optional< TfLiteParserOptions > &options=armnn::EmptyOptional())</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00063">TfLiteParser.cpp:63</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_ae96fc745917a3a0c0de7a818c9a05012"><div class="ttname"><a href="struct_execute_network_params.xhtml#ae96fc745917a3a0c0de7a818c9a05012">ExecuteNetworkParams::m_OutputDetailsToStdOut</a></div><div class="ttdeci">bool m_OutputDetailsToStdOut</div><div class="ttdef"><b>Definition:</b> <a href="_execute_network_params_8hpp_source.xhtml#l00047">ExecuteNetworkParams.hpp:47</a></div></div>
<div class="ttc" id="structarmnn_deserializer_1_1_binding_point_info_xhtml"><div class="ttname"><a href="structarmnn_deserializer_1_1_binding_point_info.xhtml">armnnDeserializer::BindingPointInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_deserializer_8hpp_source.xhtml#l00018">IDeserializer.hpp:18</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ab8cf8f9fb6792e654c2d8d8382f6f01b"><div class="ttname"><a href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a></div><div class="ttdeci">int LayerBindingId</div><div class="ttdoc">Type of identifiers for bindable layers (inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00290">Types.hpp:290</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae060224135f57f926cbda9d2732a2b1f"><div class="ttname"><a href="namespacearmnn.xhtml#ae060224135f57f926cbda9d2732a2b1f">armnn::ProfilingDetailsMethod</a></div><div class="ttdeci">ProfilingDetailsMethod</div><div class="ttdoc">Define the behaviour of the internal profiler when outputting network details. </div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00071">Types.hpp:71</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a81b5ff8545adad19a1c9d4ca076d552c"><div class="ttname"><a href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">armnn::GetDataTypeName</a></div><div class="ttdeci">constexpr const char * GetDataTypeName(DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00202">TypesUtils.hpp:202</a></div></div>
<div class="ttc" id="structarmnn_1_1_i_network_properties_xhtml"><div class="ttname"><a href="structarmnn_1_1_i_network_properties.xhtml">armnn::INetworkProperties</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00035">IRuntime.hpp:35</a></div></div>
<div class="ttc" id="structarmnn_1_1_base_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a></div><div class="ttdoc">Base class for all descriptors. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00022">Descriptors.hpp:22</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and a mutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00319">Tensor.hpp:319</a></div></div>
<div class="ttc" id="structarmnn_1_1_optimizer_options_xhtml_a6e1a42622ca43dafc7ba8e684c016eb4"><div class="ttname"><a href="structarmnn_1_1_optimizer_options.xhtml#a6e1a42622ca43dafc7ba8e684c016eb4">armnn::OptimizerOptions::m_ReduceFp32ToFp16</a></div><div class="ttdeci">bool m_ReduceFp32ToFp16</div><div class="ttdoc">Reduces all Fp32 operators in the model to Fp16 for faster processing. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00208">INetwork.hpp:208</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_strategy_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_strategy.xhtml">armnn::IStrategy</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_strategy_8hpp_source.xhtml#l00016">IStrategy.hpp:16</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="_execute_network_params_8hpp_source.xhtml#l00040">ExecuteNetworkParams.hpp:40</a></div></div>
<div class="ttc" id="structarmnn_1_1_optimizer_options_xhtml_a69eb14082d40fa0a3cff50457344a5e0"><div class="ttname"><a href="structarmnn_1_1_optimizer_options.xhtml#a69eb14082d40fa0a3cff50457344a5e0">armnn::OptimizerOptions::m_Debug</a></div><div class="ttdeci">bool m_Debug</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00211">INetwork.hpp:211</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae060224135f57f926cbda9d2732a2b1fa497ab261a562e316736c2cb59b839d32"><div class="ttname"><a href="namespacearmnn.xhtml#ae060224135f57f926cbda9d2732a2b1fa497ab261a562e316736c2cb59b839d32">armnn::ProfilingDetailsMethod::DetailsWithEvents</a></div></div>
<div class="ttc" id="struct_output_write_info_xhtml"><div class="ttname"><a href="struct_output_write_info.xhtml">OutputWriteInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_network_execution_utils_8hpp_source.xhtml#l00187">NetworkExecutionUtils.hpp:187</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="namespacearmnn_xhtml_a82e98ef05fd67036d1195ba17174d685"><div class="ttname"><a href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a></div><div class="ttdeci">IOptimizedNetworkPtr Optimize(const INetwork &network, const std::vector< BackendId > &backendPreferences, const IDeviceSpec &deviceSpec, const OptimizerOptions &options=OptimizerOptions(), Optional< std::vector< std::string > &> messages=EmptyOptional())</div><div class="ttdoc">Create an optimized version of the network. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l01864">Network.cpp:1864</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">armnn::TensorInfo::GetQuantizationOffset</a></div><div class="ttdeci">int32_t GetQuantizationOffset() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00478">Tensor.cpp:478</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00461">Tensor.cpp:461</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00198">Tensor.hpp:198</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_optimized_network_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_optimized_network.xhtml">armnn::IOptimizedNetwork</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00791">INetwork.hpp:791</a></div></div>
<div class="ttc" id="_arm_n_n_executor_8hpp_xhtml"><div class="ttname"><a href="_arm_n_n_executor_8hpp.xhtml">ArmNNExecutor.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407a7b83d3f08fa392b79e3f553b585971cd"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407a7b83d3f08fa392b79e3f553b585971cd">armnn::BoostLogSeverityMapping::warning</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="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00327">Tensor.hpp:327</a></div></div>
<div class="ttc" id="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="namespacearmnn_xhtml_adf57837d00e8352d9b5cc5ab1fb5fee9af6486a22a9bb11959bfae60a3e5174b1"><div class="ttname"><a href="namespacearmnn.xhtml#adf57837d00e8352d9b5cc5ab1fb5fee9af6486a22a9bb11959bfae60a3e5174b1">armnn::ShapeInferenceMethod::ValidateOnly</a></div><div class="ttdoc">Validate all output shapes. </div></div>
<div class="ttc" id="namespacearmnn_xhtml_a8f091a512915d1cb29a4ebf13dfc53ea"><div class="ttname"><a href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">armnn::OutputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class Tensor > > OutputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00393">Tensor.hpp:393</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a67a0db04d321a74b7e7fcfd3f1a3f70b"><div class="ttname"><a href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">armnn::Status</a></div><div class="ttdeci">Status</div><div class="ttdoc">enumeration </div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00042">Types.hpp:42</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a674efcf6cbdb9e831d653ff0e821fb38"><div class="ttname"><a href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a></div><div class="ttdeci">std::unique_ptr< IOptimizedNetwork, void(*)(IOptimizedNetwork *network)> IOptimizedNetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00239">INetwork.hpp:239</a></div></div>
<div class="ttc" id="structarmnn_1_1_optimizer_options_xhtml_a1b1892da2aaf7eaedaa38671d56b7f19"><div class="ttname"><a href="structarmnn_1_1_optimizer_options.xhtml#a1b1892da2aaf7eaedaa38671d56b7f19">armnn::OptimizerOptions::m_ProfilingEnabled</a></div><div class="ttdeci">bool m_ProfilingEnabled</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00230">INetwork.hpp:230</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a14fcd7f88d11cea0a018269dca5f9277a1131a914388fac73e5f07b0ba0aad523"><div class="ttname"><a href="namespacearmnn.xhtml#a14fcd7f88d11cea0a018269dca5f9277a1131a914388fac73e5f07b0ba0aad523">armnn::MemorySource::Malloc</a></div></div>
<div class="ttc" id="structarmnn_1_1_optimizer_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_optimizer_options.xhtml">armnn::OptimizerOptions</a></div><div class="ttdoc">ArmNN performs an optimization on each model/network before it gets loaded for execution. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00127">INetwork.hpp:127</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a></div></div>
<div class="ttc" id="namespacestd_1_1chrono_xhtml"><div class="ttname"><a href="namespacestd_1_1chrono.xhtml">chrono</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_adceb04ae84c524e4d01881e3754a4d59"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">armnn::IConnectableLayer::GetType</a></div><div class="ttdeci">virtual LayerType GetType() const =0</div><div class="ttdoc">Returns the armnn::LayerType of this layer. </div></div>
<div class="ttc" id="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="class_arm_n_n_executor_xhtml_a21887e76c6b67797ada411a163d81a62"><div class="ttname"><a href="class_arm_n_n_executor.xhtml#a21887e76c6b67797ada411a163d81a62">ArmNNExecutor::PrintNetworkInfo</a></div><div class="ttdeci">void PrintNetworkInfo() override</div><div class="ttdoc">Print available information about the network. </div><div class="ttdef"><b>Definition:</b> <a href="_arm_n_n_executor_8cpp_source.xhtml#l00217">ArmNNExecutor.cpp:217</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_optimized_network_xhtml_a72032c65bf8b8acf09b564b7d80078c5"><div class="ttname"><a href="classarmnn_1_1_i_optimized_network.xhtml#a72032c65bf8b8acf09b564b7d80078c5">armnn::IOptimizedNetwork::ExecuteStrategy</a></div><div class="ttdeci">void ExecuteStrategy(IStrategy &strategy) const</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l02956">Network.cpp:2956</a></div></div>
<div class="ttc" id="structarmnn_1_1_i_runtime_1_1_creation_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00077">IRuntime.hpp:77</a></div></div>
<div class="ttc" id="structarmnn_1_1_backend_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_backend_options.xhtml">armnn::BackendOptions</a></div><div class="ttdoc">Struct for the users to pass backend specific options. </div><div class="ttdef"><b>Definition:</b> <a href="_backend_options_8hpp_source.xhtml#l00022">BackendOptions.hpp:22</a></div></div>
<div class="ttc" id="structarmnn_1_1_i_runtime_1_1_creation_options_xhtml_ae43cf4b5df0068ee6a9151c98947248b"><div class="ttname"><a href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml#ae43cf4b5df0068ee6a9151c98947248b">armnn::IRuntime::CreationOptions::m_DynamicBackendsPath</a></div><div class="ttdeci">std::string m_DynamicBackendsPath</div><div class="ttdoc">Setting this value will override the paths set by the DYNAMIC_BACKEND_PATHS compiler directive Only a...</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00098">IRuntime.hpp:98</a></div></div>
<div class="ttc" id="structarmnn_1_1_i_runtime_1_1_creation_options_xhtml_a2fe8c3eadf4f4f9c0c664a24a2a298f9"><div class="ttname"><a href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml#a2fe8c3eadf4f4f9c0c664a24a2a298f9">armnn::IRuntime::CreationOptions::m_EnableGpuProfiling</a></div><div class="ttdeci">bool m_EnableGpuProfiling</div><div class="ttdoc">Setting this flag will allow the user to obtain GPU profiling information from the runtime...</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00093">IRuntime.hpp:93</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="namespacearmnn_xhtml_a280670a263dc4fd40491f6d0a2737f44"><div class="ttname"><a href="namespacearmnn.xhtml#a280670a263dc4fd40491f6d0a2737f44">armnn::BindingPointInfo</a></div><div class="ttdeci">std::pair< armnn::LayerBindingId, armnn::TensorInfo > BindingPointInfo</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00274">Tensor.hpp:274</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a1697b9751b4ba381f89b8c81cd9dfc3c"><div class="ttname"><a href="struct_execute_network_params.xhtml#a1697b9751b4ba381f89b8c81cd9dfc3c">ExecuteNetworkParams::m_ImportInputsIfAligned</a></div><div class="ttdeci">bool m_ImportInputsIfAligned</div><div class="ttdef"><b>Definition:</b> <a href="_execute_network_params_8hpp_source.xhtml#l00063">ExecuteNetworkParams.hpp:63</a></div></div>
<div class="ttc" id="classarmnn_onnx_parser_1_1_i_onnx_parser_xhtml_af9b9254fb8a084f0db4f7deff0498b20"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_i_onnx_parser.xhtml#af9b9254fb8a084f0db4f7deff0498b20">armnnOnnxParser::IOnnxParser::Create</a></div><div class="ttdeci">static IOnnxParserPtr Create()</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.xhtml#l00038">OnnxParser.cpp:38</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a67a0db04d321a74b7e7fcfd3f1a3f70bae139a585510a502bbf1841cf589f5086"><div class="ttname"><a href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70bae139a585510a502bbf1841cf589f5086">armnn::Status::Failure</a></div></div>
<div class="ttc" id="structarmnn_deserializer_1_1_binding_point_info_xhtml_aa308d10e76e29f09e44a933a2d091a79"><div class="ttname"><a href="structarmnn_deserializer_1_1_binding_point_info.xhtml#aa308d10e76e29f09e44a933a2d091a79">armnnDeserializer::BindingPointInfo::m_TensorInfo</a></div><div class="ttdeci">armnn::TensorInfo m_TensorInfo</div><div class="ttdef"><b>Definition:</b> <a href="_i_deserializer_8hpp_source.xhtml#l00021">IDeserializer.hpp:21</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="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
<div class="ttc" id="_async_execution_callback_8hpp_xhtml"><div class="ttname"><a href="_async_execution_callback_8hpp.xhtml">AsyncExecutionCallback.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot & GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div>
<div class="ttc" id="classarmnn_1_1_i_optimized_network_xhtml_a26794f014974a6f963a8925de07bffeb"><div class="ttname"><a href="classarmnn_1_1_i_optimized_network.xhtml#a26794f014974a6f963a8925de07bffeb">armnn::IOptimizedNetwork::SerializeToDot</a></div><div class="ttdeci">Status SerializeToDot(std::ostream &stream) const</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00509">Network.cpp:509</a></div></div>
<div class="ttc" id="class_arm_n_n_executor_xhtml_a7b274ddaba15738d696359ad327a88ca"><div class="ttname"><a href="class_arm_n_n_executor.xhtml#a7b274ddaba15738d696359ad327a88ca">ArmNNExecutor::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="_arm_n_n_executor_8cpp_source.xhtml#l00198">ArmNNExecutor.cpp:198</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_input_slot_xhtml_a81fbf6103761e55061b62ba989b00f10"><div class="ttname"><a href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">armnn::IInputSlot::GetConnection</a></div><div class="ttdeci">virtual const IOutputSlot * GetConnection() const =0</div></div>
<div class="ttc" id="classarmnn_1_1experimental_1_1_async_callback_manager_xhtml"><div class="ttname"><a href="classarmnn_1_1experimental_1_1_async_callback_manager.xhtml">armnn::experimental::AsyncCallbackManager</a></div><div class="ttdef"><b>Definition:</b> <a href="_async_execution_callback_8hpp_source.xhtml#l00076">AsyncExecutionCallback.hpp:76</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_adf57837d00e8352d9b5cc5ab1fb5fee9a5dd7c525cb1500a2181fd4cc079d7acb"><div class="ttname"><a href="namespacearmnn.xhtml#adf57837d00e8352d9b5cc5ab1fb5fee9a5dd7c525cb1500a2181fd4cc079d7acb">armnn::ShapeInferenceMethod::InferAndValidate</a></div><div class="ttdoc">Infer missing output shapes and validate all output shapes. </div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a9943775a364fc4ab53b85ac88f311886"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">armnn::IOutputSlot::GetTensorInfo</a></div><div class="ttdeci">virtual const TensorInfo & GetTensorInfo() const =0</div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot & GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index. </div></div>
<div class="ttc" id="_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="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</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="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00238">INetwork.hpp:238</a></div></div>
<div class="ttc" id="structarmnn_deserializer_1_1_binding_point_info_xhtml_a663b3104ec65e4e08b5e37fb42942087"><div class="ttname"><a href="structarmnn_deserializer_1_1_binding_point_info.xhtml#a663b3104ec65e4e08b5e37fb42942087">armnnDeserializer::BindingPointInfo::m_BindingId</a></div><div class="ttdeci">armnn::LayerBindingId m_BindingId</div><div class="ttdef"><b>Definition:</b> <a href="_i_deserializer_8hpp_source.xhtml#l00020">IDeserializer.hpp:20</a></div></div>
<div class="ttc" id="_network_execution_utils_8hpp_xhtml"><div class="ttname"><a href="_network_execution_utils_8hpp.xhtml">NetworkExecutionUtils.hpp</a></div></div>
<div class="ttc" id="class_arm_n_n_executor_xhtml_aa9bcc5837a0ef0e503f56d634b1e7184"><div class="ttname"><a href="class_arm_n_n_executor.xhtml#aa9bcc5837a0ef0e503f56d634b1e7184">ArmNNExecutor::ArmNNExecutor</a></div><div class="ttdeci">ArmNNExecutor(const ExecuteNetworkParams &params, armnn::IRuntime::CreationOptions runtimeOptions)</div><div class="ttdef"><b>Definition:</b> <a href="_arm_n_n_executor_8cpp_source.xhtml#l00017">ArmNNExecutor.cpp:17</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a8e72227ebe5ac505cf44790f2e6eb488a87f8a6ab85c9ced3702b4ea641ad4bb5"><div class="ttname"><a href="namespacearmnn.xhtml#a8e72227ebe5ac505cf44790f2e6eb488a87f8a6ab85c9ced3702b4ea641ad4bb5">armnn::QosExecPriority::Medium</a></div></div>
<div class="ttc" id="struct_execute_network_params_xhtml_a2a245a63e87f363df491ad8c35be54c5"><div class="ttname"><a href="struct_execute_network_params.xhtml#a2a245a63e87f363df491ad8c35be54c5">ExecuteNetworkParams::m_InferOutputShape</a></div><div class="ttdeci">bool m_InferOutputShape</div><div class="ttdef"><b>Definition:</b> <a href="_execute_network_params_8hpp_source.xhtml#l00038">ExecuteNetworkParams.hpp:38</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorInfo::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00196">Tensor.hpp:196</a></div></div>
<div class="ttc" id="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="_execute_network_params_8hpp_source.xhtml#l00036">ExecuteNetworkParams.hpp:36</a></div></div>
<div class="ttc" id="classarmnn_1_1experimental_1_1_async_callback_manager_xhtml_a9ee5b1dd7d3a6f619d2ed3d97d75d9b1"><div class="ttname"><a href="classarmnn_1_1experimental_1_1_async_callback_manager.xhtml#a9ee5b1dd7d3a6f619d2ed3d97d75d9b1">armnn::experimental::AsyncCallbackManager::GetNotifiedCallback</a></div><div class="ttdeci">std::shared_ptr< AsyncExecutionCallback > GetNotifiedCallback()</div><div class="ttdef"><b>Definition:</b> <a href="_async_execution_callback_8cpp_source.xhtml#l00060">AsyncExecutionCallback.cpp:60</a></div></div>
<div class="ttc" id="structarmnn_tf_lite_parser_1_1_i_tf_lite_parser_1_1_tf_lite_parser_options_xhtml_a8675330934407606641327cec4bb29f4"><div class="ttname"><a href="structarmnn_tf_lite_parser_1_1_i_tf_lite_parser_1_1_tf_lite_parser_options.xhtml#a8675330934407606641327cec4bb29f4">armnnTfLiteParser::ITfLiteParser::TfLiteParserOptions::m_StandInLayerForUnsupported</a></div><div class="ttdeci">bool m_StandInLayerForUnsupported</div><div class="ttdef"><b>Definition:</b> <a href="_i_tf_lite_parser_8hpp_source.xhtml#l00037">ITfLiteParser.hpp:37</a></div></div>
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