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<a href="_loaded_network_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_loaded_network_8hpp.xhtml">LoadedNetwork.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "<a class="code" href="_layer_8hpp.xhtml">Layer.hpp</a>"</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include "<a class="code" href="_graph_8hpp.xhtml">Graph.hpp</a>"</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_processes_8hpp.xhtml">Processes.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include "<a class="code" href="_profiling_8hpp.xhtml">Profiling.hpp</a>"</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include "<a class="code" href="_heap_profiling_8hpp.xhtml">HeapProfiling.hpp</a>"</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include "<a class="code" href="_working_mem_handle_8hpp.xhtml">WorkingMemHandle.hpp</a>"</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="preprocessor">#include <<a class="code" href="_backend_registry_8hpp.xhtml">armnn/BackendRegistry.hpp</a>></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <<a class="code" href="_logging_8hpp.xhtml">armnn/Logging.hpp</a>></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <<a class="code" href="_assert_8hpp.xhtml">armnn/utility/Assert.hpp</a>></span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> </div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <<a class="code" href="_tensor_handle_8hpp.xhtml">backendsCommon/TensorHandle.hpp</a>></span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include <<a class="code" href="include_2armnn_2backends_2_i_memory_manager_8hpp.xhtml">armnn/backends/IMemoryManager.hpp</a>></span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include <<a class="code" href="_mem_copy_workload_8hpp.xhtml">backendsCommon/MemCopyWorkload.hpp</a>></span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include <<a class="code" href="_mem_sync_workload_8hpp.xhtml">backendsCommon/MemSyncWorkload.hpp</a>></span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="preprocessor">#include <fmt/format.h></span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> {</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> </div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="keyword">using namespace </span><a class="code" href="namespacestd.xhtml">std</a>;</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn_1_1profiling.xhtml">armnn::profiling</a>;</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> <span class="keyword">namespace</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> {</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ExceptionType></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> std::string ToErrorMessage(<span class="keyword">const</span> <span class="keywordtype">char</span> * prefix, <span class="keyword">const</span> ExceptionType & <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>)</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> {</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  std::stringstream ss;</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  ss << prefix << <span class="stringliteral">" "</span> << error.what();</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="keywordflow">return</span> ss.str();</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> }</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> </div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="keywordtype">void</span> AddLayerStructure(std::unique_ptr<TimelineUtilityMethods>& timelineUtils,</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn_serializer.xhtml#a9a8118be7780e95363d631cbca7e7800">Layer</a>& layer,</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  ProfilingGuid networkGuid)</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> {</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="comment">// Add layer to the post-optimisation network structure</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  std::string layerName = layer.GetNameStr().empty() ? <span class="stringliteral">"<Unnamed>"</span> : layer.GetNameStr();</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  timelineUtils->CreateNamedTypedChildEntity(layer.GetGuid(),</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  networkGuid,</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  layerName,</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  LabelsAndEventClasses::LAYER_GUID);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& input : layer.GetInputSlots())</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  {</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">const</span> IOutputSlot* source = input.GetConnectedOutputSlot();</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(source != NULL);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  timelineUtils->CreateConnectionRelationship(ProfilingRelationshipType::RetentionLink,</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  source->GetOwningLayerGuid(),</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  layer.GetGuid());</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  }</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> }</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> <span class="keywordtype">void</span> AddWorkloadStructure(std::unique_ptr<TimelineUtilityMethods>& timelineUtils,</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  std::unique_ptr<IWorkload>& workload,</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn_serializer.xhtml#a9a8118be7780e95363d631cbca7e7800">Layer</a>& layer)</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> {</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="comment">// Add workload to the post-optimisation network structure</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  timelineUtils->CreateTypedEntity(workload->GetGuid(), LabelsAndEventClasses::WORKLOAD_GUID);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  timelineUtils->MarkEntityWithLabel(workload->GetGuid(),</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  layer.GetBackendId().Get(),</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  LabelsAndEventClasses::BACKENDID_GUID);</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>  <span class="comment">// Link the workload to the layer</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  timelineUtils->CreateRelationship(ProfilingRelationshipType::RetentionLink,</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  layer.GetGuid(),</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  workload->GetGuid(),</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  LabelsAndEventClasses::CHILD_GUID);</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="comment">// anonymous</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00081"></a><span class="lineno"><a class="line" href="classarmnn_1_1_loaded_network.xhtml#a8e94a5375ad367ddee9c69e04e110a54"> 81</a></span> std::unique_ptr<LoadedNetwork> <a class="code" href="classarmnn_1_1_loaded_network.xhtml#a8e94a5375ad367ddee9c69e04e110a54">LoadedNetwork::MakeLoadedNetwork</a>(std::unique_ptr<IOptimizedNetwork> net,</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  std::string& errorMessage,</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a>& networkProperties,</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <a class="code" href="classarmnn_1_1profiling_1_1_profiling_service.xhtml">profiling::ProfilingService</a>& profilingService)</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>  std::unique_ptr<LoadedNetwork> loadedNetwork;</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>  <span class="keyword">auto</span> Fail = [&](<span class="keyword">const</span> std::exception& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>) -> std::unique_ptr<LoadedNetwork></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>  errorMessage = ToErrorMessage(<span class="stringliteral">"An error occurred when preparing the network workloads: "</span>, error);</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(error) << errorMessage;</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> </div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordflow">return</span> std::unique_ptr<LoadedNetwork>();</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  };</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> </div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  {</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  loadedNetwork.reset(<span class="keyword">new</span> <a class="code" href="classarmnn_1_1_loaded_network.xhtml">LoadedNetwork</a>(std::move(net), networkProperties, profilingService));</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  }</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_runtime_exception.xhtml">armnn::RuntimeException</a>& error)</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="keywordflow">return</span> Fail(error);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  }</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>& error)</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  {</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keywordflow">return</span> Fail(error);</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>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> std::runtime_error& error)</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  {</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keywordflow">return</span> Fail(error);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  }</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> </div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keywordflow">return</span> loadedNetwork;</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> }</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> LoadedNetwork::LoadedNetwork(std::unique_ptr<IOptimizedNetwork> net,</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a>& networkProperties,</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="classarmnn_1_1profiling_1_1_profiling_service.xhtml">profiling::ProfilingService</a>& profilingService) :</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  m_OptimizedNetwork(std::move(net)),</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  m_NetworkProperties(networkProperties),</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  m_TensorHandleFactoryRegistry(),</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  m_ProfilingService(profilingService)</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> {</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="comment">// Create a profiler and register it for the current thread.</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  m_Profiler = std::make_shared<IProfiler>();</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">ProfilerManager::GetInstance</a>().<a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a7b1e3e5bf386004541be2b5b22443208">RegisterProfiler</a>(m_Profiler.get());</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  m_Profiler->EnableProfiling(networkProperties.<a class="code" href="structarmnn_1_1_i_network_properties.xhtml#a7e26a8e7f1878d82bef452ef3531eaeb">m_ProfilingEnabled</a>);</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> </div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordflow">if</span> (networkProperties.<a class="code" href="structarmnn_1_1_i_network_properties.xhtml#a243bb6beeac3bee21a740198495f5aa1">m_OutputNetworkDetails</a>) m_Profiler->EnableNetworkDetailsToStdOut();</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>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& order = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().<a class="code" href="classarmnn_1_1_graph.xhtml#a9a7209345edfdb2b066b0ceb66414d7c">TopologicalSort</a>();</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="comment">//First create tensor handlers, backends and workload factories.</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="comment">//Handlers are created before workloads are.</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="comment">//Because workload creation can modify some of the handlers,</span></div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="comment">//(for example the splitter and concat layers).</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& layer : order)</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  {</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keyword">auto</span> <span class="keyword">const</span>& backendId = layer->GetBackendId();</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keywordflow">if</span> (m_Backends.count(backendId) == 0)</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>  <span class="keyword">auto</span> createBackend = <a class="code" href="namespacearmnn.xhtml#ac2807505b850738bc8a1991ce669dd47">BackendRegistryInstance</a>().<a class="code" href="classarmnn_1_1_backend_registry.xhtml#afc0c63ca8db8957b58826f6d7bd231b2">GetFactory</a>(backendId);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keyword">auto</span> it = m_Backends.emplace(std::make_pair(backendId, createBackend()));</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml">IBackendInternal</a>* backend = it.first->second.get();</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> </div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordflow">if</span> (backend-><a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#af8f716b0eab6b9d63196d5a53d5fac81">SupportsTensorAllocatorAPI</a>())</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  {</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="keyword">auto</span> workloadFactory = backend-><a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#afd5a5e90515b31c0174f76ec8897e9b1">CreateWorkloadFactory</a>(</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  m_TensorHandleFactoryRegistry, m_OptimizedNetwork->pOptimizedNetworkImpl->GetModelOptions(),</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.xhtml#a5b05f3b7208ec7cea3338e30057c0bac">MemorySourceFlags</a><span class="keyword">></span>(m_NetworkProperties.m_InputSource),</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  static_cast<MemorySourceFlags>(m_NetworkProperties.m_OutputSource));</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  m_WorkloadFactories.emplace(</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  std::make_pair(backendId, std::make_pair(std::move(workloadFactory), <span class="keyword">nullptr</span>)));</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  }</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keywordflow">else</span></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>  <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> memoryManager = backend-><a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a0e8cb8533d1d0b2cb93f926dac11dd16">CreateMemoryManager</a>();</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keyword">auto</span> workloadFactory = backend-><a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#afd5a5e90515b31c0174f76ec8897e9b1">CreateWorkloadFactory</a>(</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  memoryManager, m_OptimizedNetwork->pOptimizedNetworkImpl->GetModelOptions());</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>  m_WorkloadFactories.emplace(</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  std::make_pair(backendId, std::make_pair(std::move(workloadFactory), memoryManager)));</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  }</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  }</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  }</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> </div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keywordflow">if</span> (!networkProperties.<a class="code" href="structarmnn_1_1_i_network_properties.xhtml#ad3ab02a7f6310b35c59ca78b509905ca">m_AsyncEnabled</a>)</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  {</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& layer : order)</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  {</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keyword">auto</span>& workloadFactory = GetWorkloadFactory(*layer);</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>  <span class="keywordflow">switch</span> (layer->GetType())</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  {</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">LayerType::Input</a>:</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a115bfc5d633eae55c67f9588acdd2bf9">LayerType::MemImport</a>:</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  {</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="comment">// If IsImportEnabled is true then we need to set IsMemoryManaged</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="comment">// to false when creating TensorHandles</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  layer->CreateTensorHandles(m_TensorHandleFactoryRegistry,</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  workloadFactory,</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  !m_NetworkProperties.m_ImportEnabled);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  }</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keywordflow">default</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="comment">// Look for a layer with 1 OutputSlot which has 1 connection and that connection is an Output Layer</span></div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="comment">// If Export is enabled disable memory management so we can export, otherwise we do a copy</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keywordflow">if</span> ((layer->GetNumOutputSlots() == 1) &&</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  (layer->GetOutputSlots()[0].GetNumConnections() == 1) &&</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  (layer->GetOutputSlots()[0].GetConnection(0)->GetOwningLayer().GetType() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">LayerType::Output</a>))</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  {</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  layer->CreateTensorHandles(m_TensorHandleFactoryRegistry,</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  workloadFactory,</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  !m_NetworkProperties.m_ExportEnabled);</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  }</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  {</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  layer->CreateTensorHandles(m_TensorHandleFactoryRegistry, workloadFactory);</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>  }</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>  }</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> </div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  ProfilingGuid networkGuid = m_OptimizedNetwork->GetGuid();</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  std::unique_ptr<TimelineUtilityMethods> timelineUtils =</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <a class="code" href="classarmnn_1_1profiling_1_1_timeline_utility_methods.xhtml#a0e5c863245b8d7dc5e874c0c66eebae1">TimelineUtilityMethods::GetTimelineUtils</a>(m_ProfilingService);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keywordflow">if</span> (timelineUtils)</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  {</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  timelineUtils->CreateTypedEntity(networkGuid, LabelsAndEventClasses::NETWORK_GUID);</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="comment">// Mark the network with a start of life event</span></div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  timelineUtils->RecordEvent(networkGuid, LabelsAndEventClasses::ARMNN_PROFILING_SOL_EVENT_CLASS);</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="comment">// and with the process ID</span></div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keywordtype">int</span> processID = <a class="code" href="namespacearmnn_utils_1_1_processes.xhtml#a1d95dea376acbd82dde773e05db454be">armnnUtils::Processes::GetCurrentId</a>();</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  std::stringstream ss;</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  ss << processID;</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  timelineUtils->MarkEntityWithLabel(networkGuid, ss.str(), LabelsAndEventClasses::PROCESS_ID_GUID);</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  }</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> </div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="comment">//Then create workloads.</span></div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& layer : order)</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  {</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keywordflow">if</span> (timelineUtils)</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="comment">// Add layer to the post-optimisation network structure</span></div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  AddLayerStructure(timelineUtils, *layer, networkGuid);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  }</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> </div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory = GetWorkloadFactory(*layer);</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span> </div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="keywordflow">switch</span> (layer->GetType())</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  {</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">LayerType::Input</a>:</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">LayerType::Output</a>:</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  {</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="comment">// Inputs and outputs are treated in a special way - see EnqueueInput() and EnqueueOutput().</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  }</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  {</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="keyword">auto</span> workload = layer->CreateWorkload(workloadFactory);</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>  <span class="keywordflow">if</span> (!workload)</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>  <span class="keyword">const</span> <span class="keywordtype">char</span>* <span class="keyword">const</span> layerName =</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  layer->GetNameStr().length() != 0 ? layer->GetName() : <span class="stringliteral">"<Unnamed>"</span>;</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  fmt::format(<span class="stringliteral">"No workload created for layer (name: '{0}' type: '{1}') (compute '{2}')"</span>,</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  layerName, static_cast<int>(layer->GetType()), layer->GetBackendId().Get()</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  ));</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  }</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>  <span class="keywordflow">if</span> (timelineUtils)</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  {</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="comment">// Add workload to the post-optimisation network structure</span></div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  AddWorkloadStructure(timelineUtils, workload, *layer);</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  }</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>  <span class="comment">// For async networks ConstantWorkloads are managed exclusively by LoadedNetwork</span></div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="comment">// and are separated out from the other workloads</span></div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="keywordflow">if</span> (networkProperties.<a class="code" href="structarmnn_1_1_i_network_properties.xhtml#ad3ab02a7f6310b35c59ca78b509905ca">m_AsyncEnabled</a> && layer->GetType() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255">LayerType::Constant</a>)</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  {</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  m_ConstantWorkloads[layer->GetGuid()] = std::move(workload);</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  }</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keywordflow">else</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>  m_WorkloadQueue.push_back(move(workload));</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>  <span class="comment">// release the constant data in the layer..</span></div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  layer->ReleaseConstantData();</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  }</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  }</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  }</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> </div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& workloadFactory : m_WorkloadFactories)</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>  workloadFactory.second.first->AfterWorkloadsCreated();</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="keywordflow">if</span> (timelineUtils)</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  {</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="comment">// Commit to send the post-optimisation network structure</span></div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  timelineUtils->Commit();</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  }</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> </div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keywordflow">if</span> (!networkProperties.<a class="code" href="structarmnn_1_1_i_network_properties.xhtml#ad3ab02a7f6310b35c59ca78b509905ca">m_AsyncEnabled</a>)</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="comment">// Set up memory.</span></div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().AllocateDynamicBuffers();</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span> </div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="comment">// Now that the intermediate tensor memory has been set-up,</span></div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <span class="comment">// do any post allocation configuration for each workload.</span></div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> &workload : m_WorkloadQueue)</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  {</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  workload->PostAllocationConfigure();</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</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>  <span class="keywordflow">else</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>  AllocateAndExecuteConstantWorkloads();</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  }</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span> }</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span> </div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> <span class="keywordtype">void</span> LoadedNetwork::AllocateAndExecuteConstantWorkloads()</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>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& order = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph();</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& layer : order)</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  {</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keywordflow">if</span> (layer->GetType() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255">LayerType::Constant</a>)</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>  <span class="keyword">const</span> <span class="keyword">auto</span>& outSlot = layer->GetOutputSlots()[0];</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <span class="keyword">const</span> <span class="keyword">auto</span> factoryId = outSlot.GetTensorHandleFactoryId();</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(factoryId != <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a9c95f90eb40e31f629e0e2947b8bc6f9">ITensorHandleFactory::LegacyFactoryId</a>);</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <span class="keyword">auto</span>& workloadFactory = GetWorkloadFactory(*layer);</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span> </div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  layer->CreateTensorHandles(m_TensorHandleFactoryRegistry, workloadFactory);</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml">ITensorHandle</a>* tensorHandle = outSlot.GetOutputHandler().GetData();</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>  m_ConstantTensorHandles[layer->GetGuid()] = tensorHandle;</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  tensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a5cceed8b707a09bf27eb61f17acf8a88">Allocate</a>();</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> </div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml">WorkingMemDescriptor</a> memDesc;</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  memDesc.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.push_back(tensorHandle);</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  m_ConstantWorkloads[layer->GetGuid()]->ExecuteAsync(memDesc);</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  }</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  }</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span> }</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span> </div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span> </div><div class="line"><a name="l00334"></a><span class="lineno"><a class="line" href="classarmnn_1_1_loaded_network.xhtml#a23e33c3caadba06bcd5b50dc2c23c19e"> 334</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_loaded_network.xhtml#a23e33c3caadba06bcd5b50dc2c23c19e">LoadedNetwork::SendNetworkStructure</a>()</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span> {</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& order = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().<a class="code" href="classarmnn_1_1_graph.xhtml#a9a7209345edfdb2b066b0ceb66414d7c">TopologicalSort</a>();</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  ProfilingGuid networkGuid = m_OptimizedNetwork->GetGuid();</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> </div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  std::unique_ptr<TimelineUtilityMethods> timelineUtils =</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <a class="code" href="classarmnn_1_1profiling_1_1_timeline_utility_methods.xhtml#a0e5c863245b8d7dc5e874c0c66eebae1">TimelineUtilityMethods::GetTimelineUtils</a>(m_ProfilingService);</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span> </div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  timelineUtils->CreateTypedEntity(networkGuid, LabelsAndEventClasses::NETWORK_GUID);</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>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& layer : order)</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  {</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="comment">// Add layer to the post-optimisation network structure</span></div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  AddLayerStructure(timelineUtils, *layer, networkGuid);</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="keywordflow">switch</span> (layer->GetType())</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  {</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">LayerType::Input</a>:</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">LayerType::Output</a>:</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>  <span class="comment">// Inputs and outputs are treated in a special way - see EnqueueInput() and EnqueueOutput().</span></div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  }</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  {</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>& workload : m_WorkloadQueue)</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>  <span class="comment">// Add workload to the post-optimisation network structure</span></div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  AddWorkloadStructure(timelineUtils, workload, *layer);</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  }</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keywordflow">break</span>;</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>  }</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  }</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <span class="comment">// Commit to send the post-optimisation network structure</span></div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  timelineUtils->Commit();</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> </div><div class="line"><a name="l00371"></a><span class="lineno"><a class="line" href="classarmnn_1_1_loaded_network.xhtml#ac8be6c967db9e797ade32fa3db497422"> 371</a></span> profiling::ProfilingGuid <a class="code" href="classarmnn_1_1_loaded_network.xhtml#ac8be6c967db9e797ade32fa3db497422">LoadedNetwork::GetNetworkGuid</a>()</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span> {</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="keywordflow">return</span> m_OptimizedNetwork->GetGuid();</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span> }</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span> </div><div class="line"><a name="l00376"></a><span class="lineno"><a class="line" href="classarmnn_1_1_loaded_network.xhtml#af616683424cb40d83b5a923db7f06f11"> 376</a></span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="classarmnn_1_1_loaded_network.xhtml#af616683424cb40d83b5a923db7f06f11">LoadedNetwork::GetInputTensorInfo</a>(<a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> layerId)<span class="keyword"> const</span></div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span> <span class="keyword"></span>{</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& inputLayer : m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().GetInputLayers())</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>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(inputLayer->GetNumOutputSlots() == 1, <span class="stringliteral">"Input layer should have exactly 1 output slot"</span>);</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="keywordflow">if</span> (inputLayer->GetBindingId() == layerId)</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  {</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="keywordflow">return</span> inputLayer->GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  }</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  }</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span> </div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">"No input layer is associated with id {}"</span>, layerId));</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> }</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> </div><div class="line"><a name="l00390"></a><span class="lineno"><a class="line" href="classarmnn_1_1_loaded_network.xhtml#a2b6b57945bc68f659e08d28c8a015e91"> 390</a></span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="classarmnn_1_1_loaded_network.xhtml#a2b6b57945bc68f659e08d28c8a015e91">LoadedNetwork::GetOutputTensorInfo</a>(<a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> layerId)<span class="keyword"> const</span></div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span> <span class="keyword"></span>{</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& outputLayer : m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().GetOutputLayers())</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  {</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(outputLayer->GetNumInputSlots() == 1, <span class="stringliteral">"Output layer should have exactly 1 input slot"</span>);</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(outputLayer->GetInputSlot(0).GetConnection(), <span class="stringliteral">"Input slot on Output layer must be connected"</span>);</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <span class="keywordflow">if</span> (outputLayer->GetBindingId() == layerId)</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  {</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="keywordflow">return</span> outputLayer->GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  }</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  }</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span> </div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">"No output layer is associated with id {}"</span>, layerId));</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</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> <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& LoadedNetwork::GetWorkloadFactory(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>& layer)<span class="keyword"> const</span></div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span> <span class="keyword"></span>{</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>* workloadFactory = <span class="keyword">nullptr</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="keyword">auto</span> it = m_WorkloadFactories.find(layer.<a class="code" href="classarmnn_1_1_layer.xhtml#afdb1d37740e7a083b625d669588b6a0e">GetBackendId</a>());</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="keywordflow">if</span> (it == m_WorkloadFactories.end())</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  {</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_runtime_exception.xhtml">RuntimeException</a>(fmt::format(<span class="stringliteral">"No workload factory for {0} to be used for layer: {1}"</span>,</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  layer.<a class="code" href="classarmnn_1_1_layer.xhtml#afdb1d37740e7a083b625d669588b6a0e">GetBackendId</a>().<a class="code" href="classarmnn_1_1_backend_id.xhtml#af7445617163d3f07c47b92ae56c6cf8b">Get</a>(),</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  layer.<a class="code" href="classarmnn_1_1_layer.xhtml#a9a97cb6d32661a57fc33bd29b8e41ff4">GetNameStr</a>()),</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>());</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>  workloadFactory = it->second.first.get();</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="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(workloadFactory, <span class="stringliteral">"No workload factory"</span>);</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span> </div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  std::string reasonIfUnsupported;</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a74dc9ec1a223eab8b072368b2dacee87">IWorkloadFactory::IsLayerSupported</a>(layer,</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  {},</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  reasonIfUnsupported,</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  m_OptimizedNetwork->pOptimizedNetworkImpl->GetModelOptions()),</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <span class="stringliteral">"Factory does not support layer"</span>);</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(reasonIfUnsupported);</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="keywordflow">return</span> *workloadFactory;</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span> }</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> <span class="keyword">namespace </span>{</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span> </div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span> <span class="comment">// Non-copyable class owning accelerator-specific tensor data.</span></div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span> <span class="keyword">class </span>TensorPin</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="keyword">public</span>:</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  TensorPin(std::unique_ptr<ITensorHandle> handle, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> <span class="keywordtype">id</span>)</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  : m_TensorHandle(std::move(handle))</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  , m_TensorInfo(info)</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  , m_Id(<span class="keywordtype">id</span>)</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  {</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  }</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span> </div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml">ITensorHandle</a>* GetTensorHandle()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_TensorHandle.get(); }</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <a class="code" href="namespacearmnn.xhtml#af7ec4c0fa4375a45a70e4e31f3d8af47">GetTensorInfo</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_TensorInfo; }</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> GetBindingId()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_Id; }</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span> </div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  std::unique_ptr<ITensorHandle> m_TensorHandle;</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> m_TensorInfo;</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> m_Id;</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> </div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span> <span class="keyword">static</span> <span class="keyword">const</span> TensorPin& GetTensorPin(<a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> <span class="keywordtype">id</span>,</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="keyword">const</span> std::vector<TensorPin>& pins,</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <span class="keywordtype">char</span> <span class="keyword">const</span>* bindingPointDesc)</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>  <span class="keyword">auto</span> it = std::find_if(pins.begin(), pins.end(),</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  [id](<span class="keyword">const</span> TensorPin& pin)</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  {</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keywordflow">return</span> pin.GetBindingId() == id;</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  });</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span> </div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <span class="keywordflow">if</span> (it != pins.end())</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  {</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <span class="keywordflow">return</span> *it;</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  }</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  {</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">"No tensor supplied for {0} {1}"</span>, bindingPointDesc, <span class="keywordtype">id</span>));</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  }</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span> }</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span> </div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span> <span class="comment">// Stores data that needs to be kept accessible for the entire execution of a workload.</span></div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span> <span class="keyword">class </span>WorkloadData</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="keyword">public</span>:</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  WorkloadData(<span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a>& inputTensors, <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a>& outputTensors)</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  {</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  m_InputTensorPins.reserve(inputTensors.size());</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  m_OutputTensorPins.reserve(outputTensors.size());</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">for</span> (<span class="keyword">auto</span> inputTensorPair : inputTensors)</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  {</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <span class="keyword">auto</span> inputTensor = inputTensorPair.second;</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span> </div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  std::unique_ptr<ITensorHandle> tensorHandle =</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  std::make_unique<ConstPassthroughTensorHandle>(inputTensor.GetInfo(),inputTensor.GetMemoryArea());</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> layerId = inputTensorPair.first;</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span> </div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  m_InputTensorPins.emplace_back(std::move(tensorHandle), inputTensor.GetInfo(), layerId);</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  }</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span> </div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> outputTensorPair : outputTensors)</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  {</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <span class="keyword">auto</span> outputTensor = outputTensorPair.second;</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span> </div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  std::unique_ptr<ITensorHandle> tensorHandle =</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  std::make_unique<PassthroughTensorHandle>(outputTensor.GetInfo(), outputTensor.GetMemoryArea());</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> layerId = outputTensorPair.first;</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span> </div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  m_OutputTensorPins.emplace_back(std::move(tensorHandle), outputTensor.GetInfo(), layerId);</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  }</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  }</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span> </div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <span class="keyword">const</span> TensorPin& GetInputTensorPin(<a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> <span class="keywordtype">id</span>)<span class="keyword"> const</span></div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <span class="keywordflow">return</span> GetTensorPin(<span class="keywordtype">id</span>, m_InputTensorPins, <span class="stringliteral">"input"</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> </div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  <span class="keyword">const</span> TensorPin& GetOutputTensorPin(<a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> <span class="keywordtype">id</span>)<span class="keyword"> const</span></div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <span class="keywordflow">return</span> GetTensorPin(<span class="keywordtype">id</span>, m_OutputTensorPins, <span class="stringliteral">"output"</span>);</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> <span class="keyword">private</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>  std::vector<TensorPin> m_InputTensorPins;</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  std::vector<TensorPin> m_OutputTensorPins;</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> </div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span> }</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span> </div><div class="line"><a name="l00525"></a><span class="lineno"><a class="line" href="classarmnn_1_1_loaded_network.xhtml#a878c3febb600fd2ccf3b5cb1f9a61e27"> 525</a></span> <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">Status</a> <a class="code" href="classarmnn_1_1_loaded_network.xhtml#a878c3febb600fd2ccf3b5cb1f9a61e27">LoadedNetwork::EnqueueWorkload</a>(<span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a>& inputTensors,</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a>& outputTensors)</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph();</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span> </div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <span class="comment">// Walk graph to determine the order of execution.</span></div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="keywordflow">if</span> (graph.<a class="code" href="classarmnn_1_1_graph.xhtml#afdf8eb85585a798ad0e936bde884d87b">GetNumLayers</a>() < 2)</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="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407a7b83d3f08fa392b79e3f553b585971cd">warning</a>) << <span class="stringliteral">"IRuntime::EnqueueWorkload()::Less than two nodes in graph"</span>;</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70bae139a585510a502bbf1841cf589f5086">Status::Failure</a>;</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> </div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="comment">// Data that must be kept alive for the entire execution of the workload.</span></div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  WorkloadData workloadData(inputTensors, outputTensors);</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span> </div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  <span class="keywordflow">if</span> (graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a8d8179a4a0703602a5d7dbb6e92eaf69">GetNumInputs</a>() != inputTensors.size())</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  {</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"Number of inputs provided does not match network."</span>);</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="comment">// For each input to the network, call EnqueueInput with the data passed by the user.</span></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>  <a class="code" href="_profiling_8hpp.xhtml#a5ccc65e2c464ac05ce311fdae7ede03a">ARMNN_SCOPED_PROFILING_EVENT</a>(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">Compute::Undefined</a>, <span class="stringliteral">"PrepareInputs"</span>);</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  m_InputQueue.clear();</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  m_InputQueue.reserve(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a8d8179a4a0703602a5d7dbb6e92eaf69">GetNumInputs</a>());</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_bindable_layer.xhtml">BindableLayer</a>* inputLayer : graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a919fb58873ef3a6549e4490e226f2eae">GetInputLayers</a>())</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  {</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <span class="keyword">const</span> TensorPin& pin = workloadData.GetInputTensorPin(inputLayer->GetBindingId());</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  EnqueueInput(*inputLayer, pin.GetTensorHandle(), pin.GetTensorInfo());</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  }</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  }</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span> </div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <span class="comment">// For each output to the network, call EnqueueOutput with the data passed by the user.</span></div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  {</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <a class="code" href="_profiling_8hpp.xhtml#a5ccc65e2c464ac05ce311fdae7ede03a">ARMNN_SCOPED_PROFILING_EVENT</a>(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">Compute::Undefined</a>, <span class="stringliteral">"PrepareOutputs"</span>);</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  m_OutputQueue.clear();</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  m_OutputQueue.reserve(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a604654b453ec291a503d62a0beb849d3">GetNumOutputs</a>());</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_bindable_layer.xhtml">BindableLayer</a>* outputLayer : graph.<a class="code" href="classarmnn_1_1_graph.xhtml#aa311c7fe7e05406c9ff4e4ed3ba09825">GetOutputLayers</a>())</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="keyword">const</span> TensorPin& pin = workloadData.GetOutputTensorPin(outputLayer->GetBindingId());</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  EnqueueOutput(*outputLayer, pin.GetTensorHandle(), pin.GetTensorInfo());</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  }</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  }</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span> </div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  std::unique_ptr<TimelineUtilityMethods> timelineUtils =</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  <a class="code" href="classarmnn_1_1profiling_1_1_timeline_utility_methods.xhtml#a0e5c863245b8d7dc5e874c0c66eebae1">TimelineUtilityMethods::GetTimelineUtils</a>(m_ProfilingService);</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  ProfilingGuid inferenceGuid = m_ProfilingService.GetNextGuid();</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="keywordflow">if</span> (timelineUtils)</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="comment">// Add inference timeline trace if profiling is enabled.</span></div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  ProfilingGuid networkGuid = m_OptimizedNetwork->GetGuid();</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  timelineUtils->CreateTypedEntity(inferenceGuid, LabelsAndEventClasses::INFERENCE_GUID);</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  timelineUtils->CreateRelationship(ProfilingRelationshipType::RetentionLink,</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  networkGuid,</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  inferenceGuid,</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  LabelsAndEventClasses::EXECUTION_OF_GUID);</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  timelineUtils->RecordEvent(inferenceGuid, LabelsAndEventClasses::ARMNN_PROFILING_SOL_EVENT_CLASS);</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">bool</span> executionSucceeded = <span class="keyword">true</span>;</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span> </div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  {</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <span class="keywordflow">if</span> (m_ProfilingService.IsProfilingEnabled())</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>  m_ProfilingService.IncrementCounterValue(armnn::profiling::INFERENCES_RUN);</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>  <a class="code" href="_profiling_8hpp.xhtml#a5ccc65e2c464ac05ce311fdae7ede03a">ARMNN_SCOPED_PROFILING_EVENT</a>(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">Compute::Undefined</a>, <span class="stringliteral">"Execute"</span>);</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  <a class="code" href="_heap_profiling_8hpp.xhtml#aeeb927880fc4ffc2eea754a67d884a53">ARMNN_SCOPED_HEAP_PROFILING</a>(<span class="stringliteral">"Executing"</span>);</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  executionSucceeded = Execute(timelineUtils, inferenceGuid);</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">if</span> (timelineUtils)</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>  <span class="comment">// Add end of life of the inference timeline if profiling is enabled.</span></div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  timelineUtils->RecordEvent(inferenceGuid, LabelsAndEventClasses::ARMNN_PROFILING_EOL_EVENT_CLASS);</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  timelineUtils->Commit();</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  }</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <span class="keywordflow">return</span> executionSucceeded ? <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a> : <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70bae139a585510a502bbf1841cf589f5086">Status::Failure</a>;</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span> }</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span> </div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span> <span class="keywordtype">void</span> LoadedNetwork::EnqueueInput(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_bindable_layer.xhtml">BindableLayer</a>& layer, <a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml">ITensorHandle</a>* tensorHandle, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo)</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span> {</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <span class="keywordflow">if</span> (layer.<a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() != <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">LayerType::Input</a>)</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  {</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"EnqueueInput: given layer not an InputLayer"</span>);</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  }</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span> </div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  <span class="keywordflow">if</span> (tensorHandle == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  {</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"EnqueueInput: tensorHandle must not be NULL"</span>);</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  }</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span> </div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <a class="code" href="structarmnn_1_1_mem_copy_queue_descriptor.xhtml">InputQueueDescriptor</a> inputQueueDescriptor;</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span> </div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  inputQueueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.push_back(tensorHandle);</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  info.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.push_back(tensorInfo);</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="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(layer.<a class="code" href="classarmnn_1_1_layer.xhtml#a1594bddc87d6477df300317658f566bb">GetNumOutputSlots</a>() == 1, <span class="stringliteral">"Can only handle Input Layer with one output"</span>);</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_output_handler.xhtml">OutputHandler</a>& handler = layer.<a class="code" href="classarmnn_1_1_layer.xhtml#af2c0edc7ea62a8baaec4d3d9b2b09256">GetOutputHandler</a>();</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = handler.GetTensorInfo();</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml">ITensorHandle</a>* outputTensorHandle = handler.GetData();</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(outputTensorHandle != <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  <span class="stringliteral">"Data should have been allocated."</span>);</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  inputQueueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.push_back(outputTensorHandle);</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  info.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.push_back(outputTensorInfo);</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span> </div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  <a class="code" href="namespacearmnn.xhtml#a5b05f3b7208ec7cea3338e30057c0bac">MemorySourceFlags</a> importFlags = outputTensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a55cddc2dbb32d680cd85b635ba370e48">GetImportFlags</a>();</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  <span class="keywordtype">bool</span> needMemCopy = <span class="keyword">true</span>;</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  <span class="keywordflow">if</span> (m_NetworkProperties.m_ImportEnabled) <span class="comment">// Try import the input tensor</span></div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  {</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearmnn.xhtml#a84f86b4de5adf0b164e811c87051a0ee">CheckFlag</a>(importFlags, m_NetworkProperties.m_InputSource))</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  {</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  needMemCopy = <span class="keyword">false</span>;</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <span class="comment">// This assumes a CPU Tensor handle</span></div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <span class="keywordtype">void</span>* mem = tensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a9afbc055a017adf1bc38ee137bca6e90">Map</a>(<span class="keyword">false</span>);</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  <span class="keywordflow">if</span> (outputTensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a4f81a9eff30c9b9fe76f5b83af470ba7">Import</a>(mem, m_NetworkProperties.m_InputSource))</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>  tensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a563609828050f1b3a7868c23f3365923">Unmap</a>();</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  <span class="keywordflow">return</span>; <span class="comment">// No need for a workload since the import has been done.</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>  tensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a563609828050f1b3a7868c23f3365923">Unmap</a>();</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_memory_import_exception.xhtml">MemoryImportException</a>(<span class="stringliteral">"EnqueueInput: Memory Import failed"</span>);</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>  }</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  <span class="keywordflow">if</span> (needMemCopy)</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="comment">// Create a mem copy workload for input since we did not import</span></div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  std::unique_ptr<IWorkload> inputWorkload = std::make_unique<CopyMemGenericWorkload>(inputQueueDescriptor, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span> </div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(inputWorkload, <span class="stringliteral">"No input workload created"</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>  std::unique_ptr<TimelineUtilityMethods> timelineUtils =</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <a class="code" href="classarmnn_1_1profiling_1_1_timeline_utility_methods.xhtml#a0e5c863245b8d7dc5e874c0c66eebae1">TimelineUtilityMethods::GetTimelineUtils</a>(m_ProfilingService);</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <span class="keywordflow">if</span> (timelineUtils)</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  {</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <span class="comment">// Add Input Workload to the post-optimisation network structure</span></div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  AddWorkloadStructure(timelineUtils, inputWorkload, layer);</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  timelineUtils->Commit();</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</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>  m_InputQueue.push_back(move(inputWorkload));</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</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> <span class="keywordtype">void</span> LoadedNetwork::EnqueueOutput(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_bindable_layer.xhtml">BindableLayer</a>& layer, <a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml">ITensorHandle</a>* tensorHandle, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo)</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span> {</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <span class="keywordflow">if</span> (layer.<a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() != <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">LayerType::Output</a>)</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  {</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"EnqueueOutput: given layer not an OutputLayer"</span>);</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  }</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">if</span> (tensorHandle == <span class="keyword">nullptr</span>)</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="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"EnqueueOutput: tensorHandle must not be NULL"</span>);</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  }</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span> </div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  <a class="code" href="structarmnn_1_1_mem_copy_queue_descriptor.xhtml">OutputQueueDescriptor</a> outputQueueDescriptor;</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span> </div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  outputQueueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.push_back(tensorHandle);</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  info.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.push_back(tensorInfo);</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span> </div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(layer.<a class="code" href="classarmnn_1_1_layer.xhtml#abc0660dc440c8a285b456c9ef6383c26">GetNumInputSlots</a>() == 1, <span class="stringliteral">"Output Layer should have exactly one input."</span>);</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span> </div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  <span class="comment">// Gets the output handler from the previous node.</span></div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_output_handler.xhtml">OutputHandler</a>& outputHandler = layer.<a class="code" href="classarmnn_1_1_layer.xhtml#af5f530544d09a44d726f24702b67b35b">GetInputSlots</a>()[0].GetConnectedOutputSlot()->GetOutputHandler();</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span> </div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = outputHandler.GetTensorInfo();</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml">ITensorHandle</a>* inputTensorHandle = outputHandler.GetData();</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(inputTensorHandle != <span class="keyword">nullptr</span>, <span class="stringliteral">"Data should have been allocated."</span>);</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>  <span class="comment">// Try import the output tensor.</span></div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  <span class="comment">// Note: We can only import the output pointer if all of the following hold true:</span></div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  <span class="comment">// a) The imported pointer is aligned sufficiently</span></div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  <span class="comment">// b) The tensor has zero padding</span></div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  <span class="comment">// c) There is only one connection to the OutputSlot and it is to an OutputLayer.</span></div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  <span class="comment">// d) The output pointer is allocated via malloc. (Other types will be supported in a later release)</span></div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  <span class="comment">// e) m_IsExportEnabled must be set to true</span></div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <span class="keywordtype">bool</span> needMemCopy = <span class="keyword">true</span>;</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  <span class="keywordflow">if</span> (m_NetworkProperties.m_ExportEnabled &&</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  (layer.<a class="code" href="classarmnn_1_1_layer.xhtml#af5f530544d09a44d726f24702b67b35b">GetInputSlots</a>()[0].GetConnectedOutputSlot()->GetNumConnections() == 1))</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  {</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  <span class="keywordflow">if</span>(layer.<a class="code" href="classarmnn_1_1_layer.xhtml#af5f530544d09a44d726f24702b67b35b">GetInputSlots</a>()[0].GetConnectedOutputSlot()->GetOwningLayer().GetType() != <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">LayerType::Input</a>)</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>  <a class="code" href="namespacearmnn.xhtml#a5b05f3b7208ec7cea3338e30057c0bac">MemorySourceFlags</a> importFlags = inputTensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a55cddc2dbb32d680cd85b635ba370e48">GetImportFlags</a>();</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  <span class="keywordflow">if</span> (<a class="code" href="namespacearmnn.xhtml#a84f86b4de5adf0b164e811c87051a0ee">CheckFlag</a>(importFlags, m_NetworkProperties.m_OutputSource))</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>  needMemCopy = <span class="keyword">false</span>;</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  <span class="keywordtype">void</span> *mem = tensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a9afbc055a017adf1bc38ee137bca6e90">Map</a>(<span class="keyword">false</span>);</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  <span class="keywordtype">bool</span> importOk = inputTensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a4f81a9eff30c9b9fe76f5b83af470ba7">Import</a>(mem, m_NetworkProperties.m_OutputSource);</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  tensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a563609828050f1b3a7868c23f3365923">Unmap</a>();</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span> </div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  <span class="keywordflow">if</span> (importOk)</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  {</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  <span class="comment">// Insert synchronization workload</span></div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  <a class="code" href="structarmnn_1_1_mem_sync_queue_descriptor.xhtml">MemSyncQueueDescriptor</a> syncDesc;</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  syncDesc.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.push_back(inputTensorHandle);</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  info.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.push_back(inputTensorInfo);</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  <span class="keyword">auto</span> syncWorkload = std::make_unique<SyncMemGenericWorkload>(syncDesc, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(syncWorkload, <span class="stringliteral">"No sync workload created"</span>);</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  m_OutputQueue.push_back(move(syncWorkload));</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  }</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  {</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_memory_export_exception.xhtml">MemoryExportException</a>(<span class="stringliteral">"EnqueueOutput: Memory Export failed"</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>  }</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  }</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  }</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  <span class="keywordflow">if</span> (needMemCopy)</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  {</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  <span class="comment">// If we got here then we didn't export the memory, so add an output workload which performs a memcopy.</span></div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  outputQueueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.push_back(inputTensorHandle);</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  info.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.push_back(inputTensorInfo);</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span> </div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  std::unique_ptr<IWorkload> outputWorkload =</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  std::make_unique<CopyMemGenericWorkload>(outputQueueDescriptor, <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(outputWorkload, <span class="stringliteral">"No output workload created"</span>);</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span> </div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  std::unique_ptr<TimelineUtilityMethods> timelineUtils =</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  <a class="code" href="classarmnn_1_1profiling_1_1_timeline_utility_methods.xhtml#a0e5c863245b8d7dc5e874c0c66eebae1">TimelineUtilityMethods::GetTimelineUtils</a>(m_ProfilingService);</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  <span class="keywordflow">if</span> (timelineUtils)</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>  <span class="comment">// Add Output Workload to the post-optimisation network structure</span></div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  AddWorkloadStructure(timelineUtils, outputWorkload, layer);</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  timelineUtils->Commit();</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  }</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span> </div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  m_OutputQueue.push_back(move(outputWorkload));</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> }</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span> </div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span> <span class="keywordtype">void</span> LoadedNetwork::AllocateWorkingMemory(std::lock_guard<std::mutex>& lock)</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span> {</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  <a class="code" href="_profiling_8hpp.xhtml#a5ccc65e2c464ac05ce311fdae7ede03a">ARMNN_SCOPED_PROFILING_EVENT</a>(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">Compute::Undefined</a>, <span class="stringliteral">"Working Memory Allocation"</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>  <span class="comment">// this unused parameter makes sure we can only call this function with a valid lock</span></div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(lock);</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">if</span> (m_IsWorkingMemAllocated)</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="keywordflow">return</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>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& workloadFactory : m_WorkloadFactories)</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  {</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> memoryManager = workloadFactory.second.second;</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  <span class="keywordflow">if</span> (memoryManager)</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  {</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  memoryManager->Acquire();</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  }</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  }</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  m_TensorHandleFactoryRegistry.AquireMemory();</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  m_IsWorkingMemAllocated = <span class="keyword">true</span>;</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> </div><div class="line"><a name="l00781"></a><span class="lineno"><a class="line" href="classarmnn_1_1_loaded_network.xhtml#aaf8558a23ae9be6e7ea165989f1fa808"> 781</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_loaded_network.xhtml#aaf8558a23ae9be6e7ea165989f1fa808">LoadedNetwork::FreeWorkingMemory</a>()</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span> {</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  std::lock_guard<std::mutex> lockGuard(m_WorkingMemMutex);</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  <span class="keywordflow">if</span> (!m_IsWorkingMemAllocated)</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  {</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  <span class="keywordflow">return</span>;</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>  <span class="comment">// Informs the memory managers to release memory in it's respective memory group</span></div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& workloadFactory : m_WorkloadFactories)</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>  <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a> memoryManager = workloadFactory.second.second;</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  <span class="keywordflow">if</span> (memoryManager)</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>  memoryManager->Release();</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  }</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  }</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  m_TensorHandleFactoryRegistry.ReleaseMemory();</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  m_IsWorkingMemAllocated = <span class="keyword">false</span>;</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span> }</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span> </div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span> <span class="keywordtype">bool</span> <a class="code" href="classarmnn_1_1_loaded_network.xhtml#aaedc133d26fe9a55f6565c8c47d5a8f9">LoadedNetwork::Execute</a>(std::unique_ptr<TimelineUtilityMethods>& timelineUtils,</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  profiling::ProfilingGuid inferenceGuid)</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>  <span class="keywordtype">bool</span> success = <span class="keyword">true</span>;</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span> </div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  <span class="keyword">auto</span> Fail = [&](<span class="keyword">const</span> std::exception& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>)</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  {</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(error) << <span class="stringliteral">"An error occurred attempting to execute a workload: "</span> << error.what();</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  success = <span class="keyword">false</span>;</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  };</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">try</span></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>  std::lock_guard<std::mutex> lockGuard(m_WorkingMemMutex);</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  AllocateWorkingMemory(lockGuard);</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span> </div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  ProfilingDynamicGuid workloadInferenceID(0);</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  <span class="keyword">auto</span> ExecuteQueue = [&timelineUtils, &workloadInferenceID, &inferenceGuid](<a class="code" href="classarmnn_1_1_loaded_network.xhtml#a48fe2df41d914c38c913160956acc706">WorkloadQueue</a>& queue)</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  {</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>& workload : queue)</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  {</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  <span class="keywordflow">if</span>(timelineUtils)</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  {</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  workloadInferenceID = timelineUtils->RecordWorkloadInferenceAndStartOfLifeEvent(workload->GetGuid(),</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  inferenceGuid);</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  }</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  workload->Execute();</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  <span class="keywordflow">if</span>(timelineUtils)</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  {</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  timelineUtils->RecordEndOfLifeEvent(workloadInferenceID);</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  }</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  }</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  };</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span> </div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  ExecuteQueue(m_InputQueue);</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  ExecuteQueue(m_WorkloadQueue);</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  ExecuteQueue(m_OutputQueue);</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  }</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_runtime_exception.xhtml">RuntimeException</a>& error)</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  {</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  Fail(error);</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  }</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> std::runtime_error& error)</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  {</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  Fail(error);</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  }</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span> </div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>  <span class="keywordflow">return</span> success;</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span> }</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span> </div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span> <span class="keywordtype">void</span> LoadedNetwork::EnqueueInput(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_bindable_layer.xhtml">BindableLayer</a>& layer,</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>& inputTensor,</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  <a class="code" href="classarmnn_1_1experimental_1_1_working_mem_handle.xhtml">WorkingMemHandle</a>& context)</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span> {</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  <span class="keywordflow">if</span> (layer.<a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() != <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">LayerType::Input</a>)</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  {</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"EnqueueInput: given layer not an InputLayer"</span>);</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  }</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  <a class="code" href="namespacearmnn.xhtml#afad4088a9a058114ee5f87246f87bf49">LayerGuid</a> <span class="keywordtype">id</span> = layer.<a class="code" href="classarmnn_1_1_layer.xhtml#a8dc12f0ee5b232d397bd18ced1a72a64">GetGuid</a>();</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  <a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml">WorkingMemDescriptor</a> descriptor = context.<a class="code" href="classarmnn_1_1experimental_1_1_working_mem_handle.xhtml#a70e0e7889988786ac840bf2785f3db95">GetWorkingMemDescriptor</a>(<span class="keywordtype">id</span>);</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span> </div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  <a class="code" href="namespacearmnn.xhtml#a5b05f3b7208ec7cea3338e30057c0bac">MemorySourceFlags</a> importFlags = descriptor.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0]->GetImportFlags();</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  <span class="keywordflow">if</span> (m_NetworkProperties.m_ImportEnabled) <span class="comment">// Try import the input tensor</span></div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  {</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  <span class="keywordflow">if</span> (<a class="code" href="namespacearmnn.xhtml#a84f86b4de5adf0b164e811c87051a0ee">CheckFlag</a>(importFlags, m_NetworkProperties.m_InputSource) )</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  {</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  <span class="comment">// This assumes a CPU Tensor handle</span></div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  std::unique_ptr<ITensorHandle> tensorHandle =</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  std::make_unique<ConstPassthroughTensorHandle>(inputTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>(),</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  inputTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>());</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span> </div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  <span class="keywordtype">void</span>* mem = tensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a9afbc055a017adf1bc38ee137bca6e90">Map</a>(<span class="keyword">false</span>);</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  <span class="keywordflow">if</span> (descriptor.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0]->Import(mem, m_NetworkProperties.m_InputSource))</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  {</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  tensorHandle->Unmap();</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  }</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  tensorHandle->Unmap();</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_memory_import_exception.xhtml">MemoryImportException</a>(<span class="stringliteral">"EnqueueInput: Memory Import failed"</span>);</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  }</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  {</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_memory_import_exception.xhtml">MemoryImportException</a>(<span class="stringliteral">"EnqueueInput: Memory Import failed, backend does not support Import"</span>);</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  }</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  }</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  {</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  std::unique_ptr<ITensorHandle> tensorHandle =</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  std::make_unique<ConstPassthroughTensorHandle>(inputTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>(), inputTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>());</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span> </div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  <span class="keyword">auto</span> copyFunc = [](<span class="keywordtype">void</span>* dst, <span class="keyword">const</span> <span class="keywordtype">void</span>* src, <span class="keywordtype">size_t</span> size)</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  {</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  memcpy(dst, src, size);</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  };</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span> </div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& input : descriptor.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>)</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  {</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  <a class="code" href="namespacearmnn.xhtml#a92c91193007aa49f4732d6dba5397f8d">CopyTensorContentsGeneric</a>(tensorHandle.get(), input, copyFunc);</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  }</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  }</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span> }</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span> </div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span> <span class="keywordtype">void</span> LoadedNetwork::EnqueueOutput(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_bindable_layer.xhtml">BindableLayer</a>& layer, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>& outputTensor, <a class="code" href="classarmnn_1_1experimental_1_1_working_mem_handle.xhtml">WorkingMemHandle</a>& handle)</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span> {</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  <span class="keywordflow">if</span> (layer.<a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() != <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">LayerType::Output</a>)</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  {</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"EnqueueOutput: given layer not an OutputLayer"</span>);</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  }</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(layer.<a class="code" href="classarmnn_1_1_layer.xhtml#abc0660dc440c8a285b456c9ef6383c26">GetNumInputSlots</a>() == 1, <span class="stringliteral">"Output Layer should have exactly one input."</span>);</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span> </div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  <a class="code" href="namespacearmnn.xhtml#afad4088a9a058114ee5f87246f87bf49">LayerGuid</a> <span class="keywordtype">id</span> = layer.<a class="code" href="classarmnn_1_1_layer.xhtml#a8dc12f0ee5b232d397bd18ced1a72a64">GetGuid</a>();</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  <a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml">WorkingMemDescriptor</a> descriptor = handle.<a class="code" href="classarmnn_1_1experimental_1_1_working_mem_handle.xhtml#a70e0e7889988786ac840bf2785f3db95">GetWorkingMemDescriptor</a>(<span class="keywordtype">id</span>);</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span> </div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  <a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml">ITensorHandle</a>* inputTensorHandle = descriptor.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0];</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(inputTensorHandle != <span class="keyword">nullptr</span>, <span class="stringliteral">"Data should have been allocated."</span>);</div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span> </div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  <span class="comment">// Try import the output tensor.</span></div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  <span class="comment">// Note: We can only import the output pointer if all of the following hold true:</span></div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  <span class="comment">// a) The imported pointer is aligned sufficiently</span></div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  <span class="comment">// b) The tensor has zero padding</span></div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  <span class="comment">// c) There is only one connection to the OutputSlot and it is to an OutputLayer.</span></div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  <span class="comment">// d) The output pointer is allocated via malloc. (Other types will be supported in a later release)</span></div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  <span class="comment">// e) m_IsExportEnabled must be set to true</span></div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  <span class="keywordflow">if</span> (m_NetworkProperties.m_ExportEnabled &&</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  (layer.<a class="code" href="classarmnn_1_1_layer.xhtml#af5f530544d09a44d726f24702b67b35b">GetInputSlots</a>()[0].GetConnectedOutputSlot()->GetNumConnections() == 1))</div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  {</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  <span class="keywordflow">if</span> (layer.<a class="code" href="classarmnn_1_1_layer.xhtml#af5f530544d09a44d726f24702b67b35b">GetInputSlots</a>()[0].GetConnectedOutputSlot()->GetOwningLayer().GetType() != <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">LayerType::Input</a>)</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  {</div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  <a class="code" href="namespacearmnn.xhtml#a5b05f3b7208ec7cea3338e30057c0bac">MemorySourceFlags</a> importFlags = inputTensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a55cddc2dbb32d680cd85b635ba370e48">GetImportFlags</a>();</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  <span class="keywordflow">if</span> (<a class="code" href="namespacearmnn.xhtml#a84f86b4de5adf0b164e811c87051a0ee">CheckFlag</a>(importFlags, m_NetworkProperties.m_OutputSource))</div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  {</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  std::unique_ptr<ITensorHandle> tensorHandle =</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  std::make_unique<PassthroughTensorHandle>(outputTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>(),</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  outputTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>());</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span> </div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  <span class="keywordtype">void</span>* mem = tensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a9afbc055a017adf1bc38ee137bca6e90">Map</a>(<span class="keyword">false</span>);</div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  <span class="keywordtype">bool</span> importOk = inputTensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a4f81a9eff30c9b9fe76f5b83af470ba7">Import</a>(mem, m_NetworkProperties.m_OutputSource);</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  tensorHandle->Unmap();</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span> </div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  <span class="keywordflow">if</span> (importOk)</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  {</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  <a class="code" href="_profiling_8hpp.xhtml#a5ccc65e2c464ac05ce311fdae7ede03a">ARMNN_SCOPED_PROFILING_EVENT</a>(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">Compute::Undefined</a>, <span class="stringliteral">"SyncMemGeneric_Execute"</span>);</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  inputTensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a9afbc055a017adf1bc38ee137bca6e90">Map</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  inputTensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a563609828050f1b3a7868c23f3365923">Unmap</a>();</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  }</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  {</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_memory_export_exception.xhtml">MemoryExportException</a>(<span class="stringliteral">"EnqueueOutput: Memory Export failed"</span>);</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  }</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  }</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  {</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_memory_export_exception.xhtml">MemoryExportException</a>(<span class="stringliteral">"EnqueueOutput: Memory Export failed, backend does not support Export"</span>);</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  }</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  }</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  {</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_memory_export_exception.xhtml">MemoryExportException</a>(<span class="stringliteral">"EnqueueOutput: Memory Export failed, attempting to export Input Layer"</span>);</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  }</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  }</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  {</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  <span class="keyword">auto</span> copyFunc = [](<span class="keywordtype">void</span>* dst, <span class="keyword">const</span> <span class="keywordtype">void</span>* src, <span class="keywordtype">size_t</span> size)</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  {</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  memcpy(dst, src, size);</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  };</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span> </div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  std::unique_ptr<ITensorHandle> tensorHandle =</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>  std::make_unique<PassthroughTensorHandle>(outputTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>(),</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  outputTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>());</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span> </div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  <a class="code" href="namespacearmnn.xhtml#a92c91193007aa49f4732d6dba5397f8d">CopyTensorContentsGeneric</a>(inputTensorHandle, tensorHandle.get(), copyFunc);</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  }</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span> }</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span> </div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span> </div><div class="line"><a name="l00977"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a9ef4b4b6c421b5fd4b62274e63d08f11"> 977</a></span> <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> <a class="code" href="namespacearmnn.xhtml#a9ef4b4b6c421b5fd4b62274e63d08f11">GetInputTensor</a>(<span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> layerId, <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a>& inputTensors)</div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span> {</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> inputTensorPair : inputTensors)</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  {</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> <span class="keywordtype">id</span> = inputTensorPair.first;</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  <span class="keywordflow">if</span> (<span class="keywordtype">id</span> == layerId)</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  {</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  <span class="keywordflow">return</span> inputTensorPair.second;</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  }</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  }</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"Input does not exist."</span>);</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span> }</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span> </div><div class="line"><a name="l00990"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#ac624e40d8096e61c73b246934f18afd0"> 990</a></span> <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a> <a class="code" href="namespacearmnn.xhtml#ac624e40d8096e61c73b246934f18afd0">GetOutputTensor</a>(<span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> layerId, <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a>& outputTensors)</div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span> {</div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> outputTensorPair : outputTensors)</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  {</div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span>  <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> <span class="keywordtype">id</span> = outputTensorPair.first;</div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span>  <span class="keywordflow">if</span> (<span class="keywordtype">id</span> == layerId)</div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span>  {</div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  <span class="keywordflow">return</span> outputTensorPair.second;</div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  }</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  }</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"Output does not exist."</span>);</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span> }</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span> </div><div class="line"><a name="l01003"></a><span class="lineno"><a class="line" href="classarmnn_1_1_loaded_network.xhtml#aaedc133d26fe9a55f6565c8c47d5a8f9"> 1003</a></span> <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70b">Status</a> <a class="code" href="classarmnn_1_1_loaded_network.xhtml#aaedc133d26fe9a55f6565c8c47d5a8f9">LoadedNetwork::Execute</a>(<span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a>& inputTensors,</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a>& outputTensors,</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  <a class="code" href="classarmnn_1_1experimental_1_1_i_working_mem_handle.xhtml">IWorkingMemHandle</a>& iWorkingMemHandle)</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span> {</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph();</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span> </div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  <span class="comment">// Walk graph to determine the order of execution.</span></div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  <span class="keywordflow">if</span> (graph.<a class="code" href="classarmnn_1_1_graph.xhtml#afdf8eb85585a798ad0e936bde884d87b">GetNumLayers</a>() < 2)</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  {</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407a7b83d3f08fa392b79e3f553b585971cd">warning</a>) << <span class="stringliteral">"IRuntime::EnqueueWorkload()::Less than two nodes in graph"</span>;</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70bae139a585510a502bbf1841cf589f5086">Status::Failure</a>;</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  }</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span> </div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  <span class="keywordflow">if</span> (graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a8d8179a4a0703602a5d7dbb6e92eaf69">GetNumInputs</a>() != inputTensors.size())</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  {</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"Number of inputs provided does not match network."</span>);</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>  }</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span> </div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>  std::unique_ptr<profiling::TimelineUtilityMethods> timelineUtils =</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  <a class="code" href="classarmnn_1_1profiling_1_1_timeline_utility_methods.xhtml#a0e5c863245b8d7dc5e874c0c66eebae1">profiling::TimelineUtilityMethods::GetTimelineUtils</a>(m_ProfilingService);</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  profiling::ProfilingGuid inferenceGuid = m_ProfilingService.GetNextGuid();</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  <span class="keywordflow">if</span> (timelineUtils)</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>  {</div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  <span class="comment">// Add inference timeline trace if profiling is enabled.</span></div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  profiling::ProfilingGuid networkGuid = m_OptimizedNetwork->GetGuid();</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  timelineUtils->CreateTypedEntity(inferenceGuid, profiling::LabelsAndEventClasses::INFERENCE_GUID);</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  timelineUtils->CreateRelationship(<a class="code" href="namespacearmnn_1_1profiling.xhtml#ab805f5aa9f101e7f6d971daba044c3c2afc04d023850b425f3b9c62b3a55dc110">profiling::ProfilingRelationshipType::RetentionLink</a>,</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  networkGuid,</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  inferenceGuid,</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>  profiling::LabelsAndEventClasses::EXECUTION_OF_GUID);</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>  timelineUtils->RecordEvent(inferenceGuid, profiling::LabelsAndEventClasses::ARMNN_PROFILING_SOL_EVENT_CLASS);</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>  }</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span> </div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  <span class="keywordtype">bool</span> executionSucceeded = <span class="keyword">true</span>;</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span> </div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>  <span class="keywordflow">if</span> (timelineUtils)</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>  {</div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>  <span class="comment">// Add end of life of the inference timeline if profiling is enabled.</span></div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>  timelineUtils->RecordEvent(inferenceGuid, profiling::LabelsAndEventClasses::ARMNN_PROFILING_EOL_EVENT_CLASS);</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  timelineUtils->Commit();</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  }</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  <a class="code" href="classarmnn_1_1experimental_1_1_working_mem_handle.xhtml">WorkingMemHandle</a>& workingMemHandle = <span class="keyword">dynamic_cast<</span><a class="code" href="classarmnn_1_1experimental_1_1_working_mem_handle.xhtml">WorkingMemHandle</a>&<span class="keyword">></span>(iWorkingMemHandle);</div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>  std::lock_guard<std::mutex> lockGuard(workingMemHandle.<a class="code" href="classarmnn_1_1experimental_1_1_working_mem_handle.xhtml#aa1985d354ac4640b403702a4e882f852">GetMutex</a>());</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span> </div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  <span class="keywordflow">if</span> (!workingMemHandle.<a class="code" href="classarmnn_1_1experimental_1_1_working_mem_handle.xhtml#a1a573373f4505385578f830caebf6adb">IsAllocated</a>())</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>  {</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>  workingMemHandle.<a class="code" href="classarmnn_1_1experimental_1_1_working_mem_handle.xhtml#a8518772c5d692e334a76617582b10b92">Allocate</a>();</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>  }</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span> </div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>  {</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>  <a class="code" href="_profiling_8hpp.xhtml#a5ccc65e2c464ac05ce311fdae7ede03a">ARMNN_SCOPED_PROFILING_EVENT</a>(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">Compute::Undefined</a>, <span class="stringliteral">"PrepareInputs"</span>);</div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_bindable_layer.xhtml">BindableLayer</a>* inputLayer : graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a919fb58873ef3a6549e4490e226f2eae">GetInputLayers</a>())</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  {</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>  EnqueueInput(*inputLayer, <a class="code" href="namespacearmnn.xhtml#a9ef4b4b6c421b5fd4b62274e63d08f11">GetInputTensor</a>(inputLayer->GetBindingId(), inputTensors), workingMemHandle);</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  }</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  }</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span> </div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  <span class="keyword">auto</span> Fail = [&](<span class="keyword">const</span> std::exception& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>)</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>  {</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(error) << <span class="stringliteral">"An error occurred attempting to execute a workload: "</span> << error.what();</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>  executionSucceeded = <span class="keyword">false</span>;</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  };</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  profiling::ProfilingDynamicGuid workloadInferenceID(0);</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span> </div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>  {</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < m_WorkloadQueue.size(); ++i)</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>  {</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  <span class="keyword">auto</span>& workload = m_WorkloadQueue[i];</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  <span class="keywordflow">if</span> (timelineUtils)</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>  {</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  workloadInferenceID = timelineUtils->RecordWorkloadInferenceAndStartOfLifeEvent(workload->GetGuid(),</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  inferenceGuid);</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  }</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>  workload->ExecuteAsync(workingMemHandle.<a class="code" href="classarmnn_1_1experimental_1_1_working_mem_handle.xhtml#a1915a1eb2ba2554103a09de391a9f6bd">GetWorkingMemDescriptorAt</a>(i));</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span> </div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>  <span class="keywordflow">if</span> (timelineUtils)</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>  {</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>  timelineUtils->RecordEndOfLifeEvent(workloadInferenceID);</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>  }</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  }</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>  }</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_runtime_exception.xhtml">RuntimeException</a>& error)</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  {</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  Fail(error);</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  }</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> std::runtime_error& error)</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>  {</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  Fail(error);</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  }</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>  <span class="comment">// For each output to the network, call EnqueueOutput with the data passed by the user.</span></div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  {</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  <a class="code" href="_profiling_8hpp.xhtml#a5ccc65e2c464ac05ce311fdae7ede03a">ARMNN_SCOPED_PROFILING_EVENT</a>(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">Compute::Undefined</a>, <span class="stringliteral">"PrepareOutputs"</span>);</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_bindable_layer.xhtml">BindableLayer</a> *outputLayer : graph.<a class="code" href="classarmnn_1_1_graph.xhtml#aa311c7fe7e05406c9ff4e4ed3ba09825">GetOutputLayers</a>())</div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  {</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>  EnqueueOutput(*outputLayer, <a class="code" href="namespacearmnn.xhtml#ac624e40d8096e61c73b246934f18afd0">GetOutputTensor</a>(outputLayer->GetBindingId(), outputTensors), workingMemHandle);</div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  }</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>  }</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span> </div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>  <span class="keywordflow">return</span> executionSucceeded ? <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a> : <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70bae139a585510a502bbf1841cf589f5086">Status::Failure</a>;</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span> }</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span> <span class="comment"></span></div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span> <span class="comment">/// Create a new unique WorkingMemHandle object. Create multiple handles if you wish to have</span></div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span> <span class="comment">/// overlapped Execution by calling this function from different threads.</span></div><div class="line"><a name="l01107"></a><span class="lineno"><a class="line" href="classarmnn_1_1_loaded_network.xhtml#a16e72675c37a8f251cf02951e222d4ab"> 1107</a></span> <span class="comment"></span>std::unique_ptr<IWorkingMemHandle> <a class="code" href="classarmnn_1_1_loaded_network.xhtml#a16e72675c37a8f251cf02951e222d4ab">LoadedNetwork::CreateWorkingMemHandle</a>(<a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a> networkId)</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span> {</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& order = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph();</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  std::unordered_map<LayerGuid, std::vector<std::unique_ptr<ITensorHandle> > > tensorHandleMap;</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>  std::vector<WorkingMemDescriptor> workingMemDescriptors;</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  std::unordered_map<LayerGuid, WorkingMemDescriptor> workingMemDescriptorMap;</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>  <a class="code" href="classarmnn_1_1_tensor_handle_factory_registry.xhtml">TensorHandleFactoryRegistry</a> tensorHandleFactoryRegistry;</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>  WorkloadFactoryMap workloadFactoryMap;</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span> </div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  std::vector<std::shared_ptr<IMemoryManager>> memoryManagers;</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span> </div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span>& backend : m_Backends)</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>  {</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>  <span class="keywordflow">if</span> (backend.second->SupportsTensorAllocatorAPI())</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  {</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>  backend.second->RegisterTensorHandleFactories(</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>  tensorHandleFactoryRegistry,</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  static_cast<MemorySourceFlags>(m_NetworkProperties.m_InputSource),</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>  static_cast<MemorySourceFlags>(m_NetworkProperties.m_OutputSource));</div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>  memoryManagers.emplace_back(tensorHandleFactoryRegistry.<a class="code" href="classarmnn_1_1_tensor_handle_factory_registry.xhtml#a7d5faf6ceef6e7246b3b4976869b7a18">GetMemoryManagers</a>().back());</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  }</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>  {</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>  std::shared_ptr<IMemoryManager> memoryManager = backend.second->CreateMemoryManager();</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  <span class="keyword">auto</span> workloadFactory = backend.second->CreateWorkloadFactory(</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  memoryManager, m_OptimizedNetwork->pOptimizedNetworkImpl->GetModelOptions());</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span> </div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>  workloadFactoryMap.emplace(</div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  std::make_pair(backend.first, std::make_pair(std::move(workloadFactory), memoryManager)));</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  memoryManagers.emplace_back(memoryManager);</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  }</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>  }</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span> </div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  <span class="keyword">auto</span> GetTensorHandle = [&](<a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* layer, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_output_slot.xhtml">OutputSlot</a>& outputSlot, <span class="keywordtype">bool</span> isMemoryManaged)</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  {</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>  <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a32f4aa6a7089d877af08928139c2c277">ITensorHandleFactory::FactoryId</a> factoryId = outputSlot.GetTensorHandleFactoryId();</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo = outputSlot.GetTensorInfo();</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span> </div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  <span class="keywordflow">if</span> (factoryId == <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a9c95f90eb40e31f629e0e2947b8bc6f9">ITensorHandleFactory::LegacyFactoryId</a>)</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  {</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  <a class="code" href="classarmnn_1_1_backend_id.xhtml">BackendId</a> <span class="keywordtype">id</span> = layer-><a class="code" href="classarmnn_1_1_layer.xhtml#afdb1d37740e7a083b625d669588b6a0e">GetBackendId</a>();</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  <a class="code" href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  <span class="keywordflow">return</span> workloadFactoryMap.at(<span class="keywordtype">id</span>).first->CreateTensorHandle(tensorInfo, isMemoryManaged);</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>  <a class="code" href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>  }</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  {</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">ITensorHandleFactory</a>* handleFactory = tensorHandleFactoryRegistry.<a class="code" href="classarmnn_1_1_tensor_handle_factory_registry.xhtml#ae5ecc42140a12c855554a4a621fc8456">GetFactory</a>(factoryId);</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(handleFactory);</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  <span class="keywordflow">return</span> handleFactory-><a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(tensorInfo, isMemoryManaged);</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  }</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  };</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span> </div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>  std::unordered_map<const ITensorHandle*, unsigned int> handleReferenceCounts;</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& layer : order)</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>  {</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>  <a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml">WorkingMemDescriptor</a> workingMemDescriptor;</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span> </div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>  <span class="comment">// Constant layers execution and management is handled during loaded network construction</span></div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  <span class="keywordflow">if</span> (layer-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255">LayerType::Constant</a>)</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  {</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  <span class="keywordflow">continue</span>;</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  }</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  <span class="keywordtype">bool</span> isMemoryManaged = <span class="keyword">true</span>;</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  <span class="keywordtype">bool</span> isInputLayer = <span class="keyword">true</span>;</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  <span class="comment">// Look for the layer with 1 OutputSlot which has 1 connection and that connection is an Output Layer</span></div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  <span class="comment">// If Export is enabled disable memory management so we can export, otherwise we do a copy</span></div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  <span class="keywordflow">if</span> ((layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a1594bddc87d6477df300317658f566bb">GetNumOutputSlots</a>() == 1) &&</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  (layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a98cdff4e0b45f4c80bfcedaf926e16e0">GetOutputSlots</a>()[0].GetNumConnections() == 1) &&</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  (layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a98cdff4e0b45f4c80bfcedaf926e16e0">GetOutputSlots</a>()[0].GetConnection(0)->GetOwningLayer().GetType() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">LayerType::Output</a>))</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  {</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  isMemoryManaged = !m_NetworkProperties.m_ExportEnabled;</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  }</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (layer-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">LayerType::Input</a> || layer-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a115bfc5d633eae55c67f9588acdd2bf9">LayerType::MemImport</a>)</div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  {</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  <span class="comment">// Input layers/workloads will not be executed so the descriptor is not added to workingMemDescriptors</span></div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  <span class="comment">// However we will still need to manage the tensorHandle</span></div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  isInputLayer = <span class="keyword">false</span>;</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  isMemoryManaged = !m_NetworkProperties.m_ExportEnabled;</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>  }</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span> </div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  <span class="comment">// Create a tensor handle for each output slot of a layer</span></div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>  <span class="comment">// Once we create it, we start managing its lifetime</span></div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>& slot : layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a98cdff4e0b45f4c80bfcedaf926e16e0">GetOutputSlots</a>())</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  {</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  tensorHandleMap[layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a8dc12f0ee5b232d397bd18ced1a72a64">GetGuid</a>()].emplace_back(GetTensorHandle(layer, slot, isMemoryManaged));</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  <a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml">ITensorHandle</a>* tensorHandle = tensorHandleMap[layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a8dc12f0ee5b232d397bd18ced1a72a64">GetGuid</a>()].back().get();</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span> </div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  workingMemDescriptor.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.push_back(tensorHandle);</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>  tensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a04149d0f9b9c54c05e95693111490daf">Manage</a>();</div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numConnections = slot.GetNumConnections();</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(numConnections != 0);</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span> </div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>  handleReferenceCounts[tensorHandle] = numConnections;</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  }</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  <span class="comment">// Loop through the input slots in the same layer and decrement the reference counter associated</span></div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  <span class="comment">// to each tensor handle we encounter.</span></div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  <span class="comment">// Once it reaches zero, the lifetime of the tensor handle has ended, and we mark it's memory as available</span></div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  <span class="comment">// so that the next tensor handle with a non overlapping lifetime can share it's memory.</span></div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>& slot : layer-><a class="code" href="classarmnn_1_1_layer.xhtml#af5f530544d09a44d726f24702b67b35b">GetInputSlots</a>())</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>  {</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(slot.GetConnection());</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>  <span class="keyword">auto</span> outputSlot = slot.GetConnectedOutputSlot();</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>  <span class="keyword">auto</span> key = outputSlot->GetOwningLayer().GetGuid();</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span> </div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  <span class="comment">// Constant layers execution and management is handled during loaded network construction</span></div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>  <span class="keyword">auto</span> found = m_ConstantTensorHandles.find(key);</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>  <span class="keywordflow">if</span> (found != m_ConstantTensorHandles.end())</div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>  {</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  workingMemDescriptor.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.push_back(found->second);</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>  <span class="keywordflow">continue</span>;</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>  }</div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span> </div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>  <span class="keyword">auto</span> search = tensorHandleMap.find(key);</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> index = outputSlot->CalculateIndexOnOwner();</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>  <a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml">ITensorHandle</a>* inputTensorHandle = search->second[index].get();</div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  workingMemDescriptor.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.push_back(inputTensorHandle);</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  --handleReferenceCounts.at(inputTensorHandle);</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  <span class="keywordflow">if</span> (handleReferenceCounts.at(inputTensorHandle) == 0u)</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  {</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>  <span class="comment">// Stop managing lifetime of tensor handle</span></div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>  inputTensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a5cceed8b707a09bf27eb61f17acf8a88">Allocate</a>();</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>  handleReferenceCounts.erase(inputTensorHandle);</div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>  }</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>  }</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>  workingMemDescriptorMap.insert({layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a8dc12f0ee5b232d397bd18ced1a72a64">GetGuid</a>(), workingMemDescriptor});</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span> </div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  <span class="comment">// Input layers/workloads will not be executed, so the descriptor is not added to workingMemDescriptors</span></div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  <span class="comment">// However we will still need to manage the tensorHandle</span></div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>  <span class="keywordflow">if</span> (isInputLayer)</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>  {</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>  workingMemDescriptors.push_back(workingMemDescriptor);</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>  }</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  }</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span> </div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  <span class="keywordflow">return</span> std::make_unique<WorkingMemHandle>(networkId,</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  workingMemDescriptors,</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  workingMemDescriptorMap,</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>  memoryManagers,</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>  std::move(tensorHandleMap));</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span> }</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span> </div><div class="line"><a name="l01249"></a><span class="lineno"><a class="line" href="classarmnn_1_1_loaded_network.xhtml#a091ea8d2d804c8902f3120fdf2a36512"> 1249</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_loaded_network.xhtml#a091ea8d2d804c8902f3120fdf2a36512">LoadedNetwork::RegisterDebugCallback</a>(<span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a15f3ad9b5e4e3d46b0a6dda246a7bc28">DebugCallbackFunction</a>& func)</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span> {</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& workloadPtr: m_WorkloadQueue)</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>  {</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>  workloadPtr.get()->RegisterDebugCallback(func);</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  }</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span> }</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span> </div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span> }</div><div class="ttc" id="_mem_copy_workload_8hpp_xhtml"><div class="ttname"><a href="_mem_copy_workload_8hpp.xhtml">MemCopyWorkload.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_loaded_network_xhtml_a16e72675c37a8f251cf02951e222d4ab"><div class="ttname"><a href="classarmnn_1_1_loaded_network.xhtml#a16e72675c37a8f251cf02951e222d4ab">armnn::LoadedNetwork::CreateWorkingMemHandle</a></div><div class="ttdeci">std::unique_ptr< IWorkingMemHandle > CreateWorkingMemHandle(NetworkId networkId)</div><div class="ttdoc">Create a new unique WorkingMemHandle object. </div><div class="ttdef"><b>Definition:</b> <a href="_loaded_network_8cpp_source.xhtml#l01107">LoadedNetwork.cpp:1107</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_xhtml_a4f81a9eff30c9b9fe76f5b83af470ba7"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle.xhtml#a4f81a9eff30c9b9fe76f5b83af470ba7">armnn::ITensorHandle::Import</a></div><div class="ttdeci">virtual bool Import(void *memory, MemorySource source)</div><div class="ttdoc">Import externally allocated memory. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_tensor_handle_8hpp_source.xhtml#l00075">ITensorHandle.hpp:75</a></div></div>
<div class="ttc" id="classarmnn_1_1_backend_registry_xhtml_afc0c63ca8db8957b58826f6d7bd231b2"><div class="ttname"><a href="classarmnn_1_1_backend_registry.xhtml#afc0c63ca8db8957b58826f6d7bd231b2">armnn::BackendRegistry::GetFactory</a></div><div class="ttdeci">FactoryFunction GetFactory(const BackendId &id) const</div><div class="ttdef"><b>Definition:</b> <a href="_backend_registry_8cpp_source.xhtml#l00055">BackendRegistry.cpp:55</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_abc0660dc440c8a285b456c9ef6383c26"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#abc0660dc440c8a285b456c9ef6383c26">armnn::Layer::GetNumInputSlots</a></div><div class="ttdeci">unsigned int GetNumInputSlots() const override</div><div class="ttdoc">Returns the number of connectable input slots. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00313">Layer.hpp:313</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_backend_internal_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml">armnn::IBackendInternal</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.xhtml#l00075">IBackendInternal.hpp:75</a></div></div>
<div class="ttc" id="classarmnn_1_1profiling_1_1_timeline_utility_methods_xhtml_a0e5c863245b8d7dc5e874c0c66eebae1"><div class="ttname"><a href="classarmnn_1_1profiling_1_1_timeline_utility_methods.xhtml#a0e5c863245b8d7dc5e874c0c66eebae1">armnn::profiling::TimelineUtilityMethods::GetTimelineUtils</a></div><div class="ttdeci">static std::unique_ptr< TimelineUtilityMethods > GetTimelineUtils(ProfilingService &profilingService)</div><div class="ttdef"><b>Definition:</b> <a href="_timeline_utility_methods_8cpp_source.xhtml#l00018">TimelineUtilityMethods.cpp:18</a></div></div>
<div class="ttc" id="classarmnn_1_1_profiler_manager_xhtml_a93857080c2523bf3395e7aa7e6024d5c"><div class="ttname"><a href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a></div><div class="ttdeci">static ProfilerManager & GetInstance()</div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8cpp_source.xhtml#l00526">Profiling.cpp:526</a></div></div>
<div class="ttc" id="_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="_tensor_handle_8hpp.xhtml">TensorHandle.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_utils_1_1_processes_xhtml_a1d95dea376acbd82dde773e05db454be"><div class="ttname"><a href="namespacearmnn_utils_1_1_processes.xhtml#a1d95dea376acbd82dde773e05db454be">armnnUtils::Processes::GetCurrentId</a></div><div class="ttdeci">int GetCurrentId()</div><div class="ttdef"><b>Definition:</b> <a href="_processes_8cpp_source.xhtml#l00019">Processes.cpp:19</a></div></div>
<div class="ttc" id="_deprecated_8hpp_xhtml_ab66a241a0ed3ee89c866e777b035d0ed"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_BEGIN</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00033">Deprecated.hpp:33</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="classarmnn_1_1_i_workload_factory_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8hpp_source.xhtml#l00022">WorkloadFactory.hpp:22</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_xhtml_a55cddc2dbb32d680cd85b635ba370e48"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle.xhtml#a55cddc2dbb32d680cd85b635ba370e48">armnn::ITensorHandle::GetImportFlags</a></div><div class="ttdeci">virtual unsigned int GetImportFlags() const</div><div class="ttdoc">Get flags describing supported import sources. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_tensor_handle_8hpp_source.xhtml#l00069">ITensorHandle.hpp:69</a></div></div>
<div class="ttc" id="structarmnn_1_1_i_network_properties_xhtml_ad3ab02a7f6310b35c59ca78b509905ca"><div class="ttname"><a href="structarmnn_1_1_i_network_properties.xhtml#ad3ab02a7f6310b35c59ca78b509905ca">armnn::INetworkProperties::m_AsyncEnabled</a></div><div class="ttdeci">const bool m_AsyncEnabled</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00084">IRuntime.hpp:84</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ac624e40d8096e61c73b246934f18afd0"><div class="ttname"><a href="namespacearmnn.xhtml#ac624e40d8096e61c73b246934f18afd0">armnn::GetOutputTensor</a></div><div class="ttdeci">const armnn::Tensor GetOutputTensor(const LayerBindingId layerId, const OutputTensors &outputTensors)</div><div class="ttdef"><b>Definition:</b> <a href="_loaded_network_8cpp_source.xhtml#l00990">LoadedNetwork.cpp:990</a></div></div>
<div class="ttc" id="namespacearmnn_serializer_xhtml_a9a8118be7780e95363d631cbca7e7800"><div class="ttname"><a href="namespacearmnn_serializer.xhtml#a9a8118be7780e95363d631cbca7e7800">armnnSerializer::Layer</a></div><div class="ttdeci">Layer</div><div class="ttdef"><b>Definition:</b> <a href="_armnn_schema__generated_8h_source.xhtml#l01174">ArmnnSchema_generated.h:1174</a></div></div>
<div class="ttc" id="classarmnn_1_1_bindable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_bindable_layer.xhtml">armnn::BindableLayer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00431">Layer.hpp:431</a></div></div>
<div class="ttc" id="classarmnn_1_1experimental_1_1_working_mem_handle_xhtml_a1915a1eb2ba2554103a09de391a9f6bd"><div class="ttname"><a href="classarmnn_1_1experimental_1_1_working_mem_handle.xhtml#a1915a1eb2ba2554103a09de391a9f6bd">armnn::experimental::WorkingMemHandle::GetWorkingMemDescriptorAt</a></div><div class="ttdeci">WorkingMemDescriptor & GetWorkingMemDescriptorAt(unsigned int id) override</div><div class="ttdoc">Get the WorkingMemDescriptor at an index. </div><div class="ttdef"><b>Definition:</b> <a href="_working_mem_handle_8hpp_source.xhtml#l00073">WorkingMemHandle.hpp:73</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_i_tensor_handle_xhtml_a5cceed8b707a09bf27eb61f17acf8a88"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle.xhtml#a5cceed8b707a09bf27eb61f17acf8a88">armnn::ITensorHandle::Allocate</a></div><div class="ttdeci">virtual void Allocate()=0</div><div class="ttdoc">Indicate to the memory manager that this resource is no longer active. </div></div>
<div class="ttc" id="classarmnn_1_1_loaded_network_xhtml_af616683424cb40d83b5a923db7f06f11"><div class="ttname"><a href="classarmnn_1_1_loaded_network.xhtml#af616683424cb40d83b5a923db7f06f11">armnn::LoadedNetwork::GetInputTensorInfo</a></div><div class="ttdeci">TensorInfo GetInputTensorInfo(LayerBindingId layerId) const</div><div class="ttdef"><b>Definition:</b> <a href="_loaded_network_8cpp_source.xhtml#l00376">LoadedNetwork.cpp:376</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#l00202">Logging.hpp:202</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_xhtml_a04149d0f9b9c54c05e95693111490daf"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle.xhtml#a04149d0f9b9c54c05e95693111490daf">armnn::ITensorHandle::Manage</a></div><div class="ttdeci">virtual void Manage()=0</div><div class="ttdoc">Indicate to the memory manager that this resource is active. </div></div>
<div class="ttc" id="namespacearmnn_xhtml_ac2807505b850738bc8a1991ce669dd47"><div class="ttname"><a href="namespacearmnn.xhtml#ac2807505b850738bc8a1991ce669dd47">armnn::BackendRegistryInstance</a></div><div class="ttdeci">BackendRegistry & BackendRegistryInstance()</div><div class="ttdef"><b>Definition:</b> <a href="_backend_registry_8cpp_source.xhtml#l00013">BackendRegistry.cpp:13</a></div></div>
<div class="ttc" id="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#l00360">Tensor.hpp:360</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a5b05f3b7208ec7cea3338e30057c0bac"><div class="ttname"><a href="namespacearmnn.xhtml#a5b05f3b7208ec7cea3338e30057c0bac">armnn::MemorySourceFlags</a></div><div class="ttdeci">unsigned int MemorySourceFlags</div><div class="ttdef"><b>Definition:</b> <a href="_memory_sources_8hpp_source.xhtml#l00015">MemorySources.hpp:15</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_aa81f67ac64f0c249e26499600c45d996"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">armnn::BaseTensor::GetMemoryArea</a></div><div class="ttdeci">MemoryType GetMemoryArea() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00305">Tensor.hpp:305</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_a604654b453ec291a503d62a0beb849d3"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a604654b453ec291a503d62a0beb849d3">armnn::Graph::GetNumOutputs</a></div><div class="ttdeci">size_t GetNumOutputs() const</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00181">Graph.hpp:181</a></div></div>
<div class="ttc" id="classarmnn_1_1_loaded_network_xhtml_a2b6b57945bc68f659e08d28c8a015e91"><div class="ttname"><a href="classarmnn_1_1_loaded_network.xhtml#a2b6b57945bc68f659e08d28c8a015e91">armnn::LoadedNetwork::GetOutputTensorInfo</a></div><div class="ttdeci">TensorInfo GetOutputTensorInfo(LayerBindingId layerId) const</div><div class="ttdef"><b>Definition:</b> <a href="_loaded_network_8cpp_source.xhtml#l00390">LoadedNetwork.cpp:390</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__software__tools_8dox_source.xhtml#l00006">01_00_software_tools.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="classarmnn_1_1_layer_xhtml_af5f530544d09a44d726f24702b67b35b"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#af5f530544d09a44d726f24702b67b35b">armnn::Layer::GetInputSlots</a></div><div class="ttdeci">const std::vector< InputSlot > & GetInputSlots() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00237">Layer.hpp:237</a></div></div>
<div class="ttc" id="_loaded_network_8hpp_xhtml"><div class="ttname"><a href="_loaded_network_8hpp.xhtml">LoadedNetwork.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a15f3ad9b5e4e3d46b0a6dda246a7bc28"><div class="ttname"><a href="namespacearmnn.xhtml#a15f3ad9b5e4e3d46b0a6dda246a7bc28">armnn::DebugCallbackFunction</a></div><div class="ttdeci">std::function< void(LayerGuid guid, unsigned int slotIndex, ITensorHandle *tensorHandle)> DebugCallbackFunction</div><div class="ttdoc">Define the type of callback for the Debug layer to call. </div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00321">Types.hpp:321</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_a1594bddc87d6477df300317658f566bb"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a1594bddc87d6477df300317658f566bb">armnn::Layer::GetNumOutputSlots</a></div><div class="ttdeci">unsigned int GetNumOutputSlots() const override</div><div class="ttdoc">Returns the number of connectable output slots. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00314">Layer.hpp:314</a></div></div>
<div class="ttc" id="_processes_8hpp_xhtml"><div class="ttname"><a href="_processes_8hpp.xhtml">Processes.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">armnn::BoostLogSeverityMapping::error</a></div></div>
<div class="ttc" id="_backend_registry_8hpp_xhtml"><div class="ttname"><a href="_backend_registry_8hpp.xhtml">BackendRegistry.hpp</a></div></div>
<div class="ttc" id="_profiling_8hpp_xhtml_a5ccc65e2c464ac05ce311fdae7ede03a"><div class="ttname"><a href="_profiling_8hpp.xhtml#a5ccc65e2c464ac05ce311fdae7ede03a">ARMNN_SCOPED_PROFILING_EVENT</a></div><div class="ttdeci">#define ARMNN_SCOPED_PROFILING_EVENT(backendId, name)</div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8hpp_source.xhtml#l00219">Profiling.hpp:219</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#l00244">Types.hpp:244</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#l00032">IRuntime.hpp:32</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_1experimental_1_1_working_mem_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml">armnn::experimental::WorkingMemDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_working_mem_descriptor_8hpp_source.xhtml#l00018">WorkingMemDescriptor.hpp:18</a></div></div>
<div class="ttc" id="classarmnn_1_1_loaded_network_xhtml_aaedc133d26fe9a55f6565c8c47d5a8f9"><div class="ttname"><a href="classarmnn_1_1_loaded_network.xhtml#aaedc133d26fe9a55f6565c8c47d5a8f9">armnn::LoadedNetwork::Execute</a></div><div class="ttdeci">Status Execute(const InputTensors &inputTensors, const OutputTensors &outputTensors, IWorkingMemHandle &workingMemHandle)</div><div class="ttdoc">Thread safe execution of the loaded network. </div><div class="ttdef"><b>Definition:</b> <a href="_loaded_network_8cpp_source.xhtml#l01003">LoadedNetwork.cpp:1003</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_backend_internal_xhtml_afd5a5e90515b31c0174f76ec8897e9b1"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml#afd5a5e90515b31c0174f76ec8897e9b1">armnn::IBackendInternal::CreateWorkloadFactory</a></div><div class="ttdeci">virtual IWorkloadFactoryPtr CreateWorkloadFactory(const IMemoryManagerSharedPtr &memoryManager=nullptr) const =0</div></div>
<div class="ttc" id="_working_mem_handle_8hpp_xhtml"><div class="ttname"><a href="_working_mem_handle_8hpp.xhtml">WorkingMemHandle.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1experimental_1_1_working_mem_descriptor_xhtml_a4b50e46a6810018f3edecfb68b2a76b3"><div class="ttname"><a href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">armnn::experimental::WorkingMemDescriptor::m_Inputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Inputs</div><div class="ttdef"><b>Definition:</b> <a href="_working_mem_descriptor_8hpp_source.xhtml#l00020">WorkingMemDescriptor.hpp:20</a></div></div>
<div class="ttc" id="namespacestd_xhtml"><div class="ttname"><a href="namespacestd.xhtml">std</a></div><div class="ttdef"><b>Definition:</b> <a href="_backend_id_8hpp_source.xhtml#l00147">BackendId.hpp:147</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml_ac97905bfa0daab357b91df1347600309"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">armnn::WorkloadInfo::m_InputTensorInfos</a></div><div class="ttdeci">std::vector< TensorInfo > m_InputTensorInfos</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00018">WorkloadInfo.hpp:18</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38"><div class="ttname"><a href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">armnn::Status::Success</a></div></div>
<div class="ttc" id="classarmnn_1_1experimental_1_1_working_mem_handle_xhtml_a70e0e7889988786ac840bf2785f3db95"><div class="ttname"><a href="classarmnn_1_1experimental_1_1_working_mem_handle.xhtml#a70e0e7889988786ac840bf2785f3db95">armnn::experimental::WorkingMemHandle::GetWorkingMemDescriptor</a></div><div class="ttdeci">WorkingMemDescriptor & GetWorkingMemDescriptor(LayerGuid id) override</div><div class="ttdoc">Get the WorkingMemDescriptor for a Layer. The mutex must be locked. </div><div class="ttdef"><b>Definition:</b> <a href="_working_mem_handle_8hpp_source.xhtml#l00064">WorkingMemHandle.hpp:64</a></div></div>
<div class="ttc" id="_deprecated_8hpp_xhtml_ad762b11b48e5c1d1c1743f529485728a"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_END</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00034">Deprecated.hpp:34</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml_a91c4dfde57907d7698c7531785690a7f"><div class="ttname"><a href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a></div><div class="ttdeci">#define ARMNN_ASSERT_MSG(COND, MSG)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00015">Assert.hpp:15</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_backend_internal_xhtml_af8f716b0eab6b9d63196d5a53d5fac81"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml#af8f716b0eab6b9d63196d5a53d5fac81">armnn::IBackendInternal::SupportsTensorAllocatorAPI</a></div><div class="ttdeci">bool SupportsTensorAllocatorAPI() const</div><div class="ttdef"><b>Definition:</b> <a href="_i_backend_internal_8cpp_source.xhtml#l00156">IBackendInternal.cpp:156</a></div></div>
<div class="ttc" id="_graph_8hpp_xhtml"><div class="ttname"><a href="_graph_8hpp.xhtml">Graph.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_backend_internal_xhtml_a693b40e6b94e958836aeb0410ca186bd"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a></div><div class="ttdeci">std::shared_ptr< IMemoryManager > IMemoryManagerSharedPtr</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.xhtml#l00099">IBackendInternal.hpp:99</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle.xhtml">armnn::ITensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_tensor_handle_8hpp_source.xhtml#l00015">ITensorHandle.hpp:15</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="_heap_profiling_8hpp_xhtml_aeeb927880fc4ffc2eea754a67d884a53"><div class="ttname"><a href="_heap_profiling_8hpp.xhtml#aeeb927880fc4ffc2eea754a67d884a53">ARMNN_SCOPED_HEAP_PROFILING</a></div><div class="ttdeci">#define ARMNN_SCOPED_HEAP_PROFILING(TAG)</div><div class="ttdef"><b>Definition:</b> <a href="_heap_profiling_8hpp_source.xhtml#l00045">HeapProfiling.hpp:45</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeaec0fc0100c4fc1ce4eea230c3dc10360">armnn::Compute::Undefined</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a0d8160388a127c1a23b37bc88dc6e2ec"><div class="ttname"><a href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">armnn::NetworkId</a></div><div class="ttdeci">int NetworkId</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00024">IRuntime.hpp:24</a></div></div>
<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00327">Tensor.hpp:327</a></div></div>
<div class="ttc" id="namespacearmnn_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#l00361">Tensor.hpp:361</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_a9a97cb6d32661a57fc33bd29b8e41ff4"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a9a97cb6d32661a57fc33bd29b8e41ff4">armnn::Layer::GetNameStr</a></div><div class="ttdeci">const std::string & GetNameStr() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00220">Layer.hpp:220</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_ad8e15c530c929ab823d89ae9fd2d3f11"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">armnn::Layer::GetType</a></div><div class="ttdeci">LayerType GetType() const override</div><div class="ttdoc">Returns the armnn::LayerType of this layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00265">Layer.hpp:265</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#l00029">Types.hpp:29</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml">armnn::OutputSlot</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00083">Layer.hpp:83</a></div></div>
<div class="ttc" id="classarmnn_1_1experimental_1_1_i_working_mem_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1experimental_1_1_i_working_mem_handle.xhtml">armnn::experimental::IWorkingMemHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_working_mem_handle_8hpp_source.xhtml#l00020">IWorkingMemHandle.hpp:20</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml_a5698be69cbd5dfe6c28fcd9867e8cbed"><div class="ttname"><a href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a></div><div class="ttdeci">#define ARMNN_ASSERT(COND)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00014">Assert.hpp:14</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_handler_xhtml"><div class="ttname"><a href="classarmnn_1_1_output_handler.xhtml">armnn::OutputHandler</a></div><div class="ttdef"><b>Definition:</b> <a href="_output_handler_8hpp_source.xhtml#l00028">OutputHandler.hpp:28</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml_a67b178f8a836bc1e52b8de109760adfd"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">armnn::WorkloadInfo::m_OutputTensorInfos</a></div><div class="ttdeci">std::vector< TensorInfo > m_OutputTensorInfos</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00019">WorkloadInfo.hpp:19</a></div></div>
<div class="ttc" id="namespacearmnn_1_1profiling_xhtml"><div class="ttname"><a href="namespacearmnn_1_1profiling.xhtml">armnn::profiling</a></div><div class="ttdef"><b>Definition:</b> <a href="_backend_id_8hpp_source.xhtml#l00168">BackendId.hpp:168</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a74dc9ec1a223eab8b072368b2dacee87"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a74dc9ec1a223eab8b072368b2dacee87">armnn::IWorkloadFactory::IsLayerSupported</a></div><div class="ttdeci">static bool IsLayerSupported(const BackendId &backendId, const IConnectableLayer &layer, Optional< DataType > dataType, std::string &outReasonIfUnsupported)</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01418">WorkloadFactory.cpp:1418</a></div></div>
<div class="ttc" id="classarmnn_1_1_runtime_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_runtime_exception.xhtml">armnn::RuntimeException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00120">Exceptions.hpp:120</a></div></div>
<div class="ttc" id="classarmnn_1_1_loaded_network_xhtml_a48fe2df41d914c38c913160956acc706"><div class="ttname"><a href="classarmnn_1_1_loaded_network.xhtml#a48fe2df41d914c38c913160956acc706">armnn::LoadedNetwork::WorkloadQueue</a></div><div class="ttdeci">std::vector< std::unique_ptr< IWorkload > > WorkloadQueue</div><div class="ttdef"><b>Definition:</b> <a href="_loaded_network_8hpp_source.xhtml#l00038">LoadedNetwork.hpp:38</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_a8aeddebdcf02e1832b22203c08a6b678"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">armnn::BaseTensor::GetInfo</a></div><div class="ttdeci">const TensorInfo & GetInfo() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00295">Tensor.hpp:295</a></div></div>
<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
<div class="ttc" id="_exceptions_8hpp_xhtml_aa3be76aec4ce713822a5ea1ecbb7bc61"><div class="ttname"><a href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a></div><div class="ttdeci">#define CHECK_LOCATION()</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00197">Exceptions.hpp:197</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_afdb1d37740e7a083b625d669588b6a0e"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#afdb1d37740e7a083b625d669588b6a0e">armnn::Layer::GetBackendId</a></div><div class="ttdeci">const BackendId & GetBackendId() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00269">Layer.hpp:269</a></div></div>
<div class="ttc" id="namespacearmnn_1_1profiling_xhtml_ab805f5aa9f101e7f6d971daba044c3c2afc04d023850b425f3b9c62b3a55dc110"><div class="ttname"><a href="namespacearmnn_1_1profiling.xhtml#ab805f5aa9f101e7f6d971daba044c3c2afc04d023850b425f3b9c62b3a55dc110">armnn::profiling::ProfilingRelationshipType::RetentionLink</a></div></div>
<div class="ttc" id="classarmnn_1_1experimental_1_1_working_mem_handle_xhtml_a8518772c5d692e334a76617582b10b92"><div class="ttname"><a href="classarmnn_1_1experimental_1_1_working_mem_handle.xhtml#a8518772c5d692e334a76617582b10b92">armnn::experimental::WorkingMemHandle::Allocate</a></div><div class="ttdeci">void Allocate() override</div><div class="ttdoc">Allocate the backing memory required for execution. </div><div class="ttdef"><b>Definition:</b> <a href="_working_mem_handle_8cpp_source.xhtml#l00033">WorkingMemHandle.cpp:33</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml">armnn::Graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00029">Graph.hpp:29</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_a98cdff4e0b45f4c80bfcedaf926e16e0"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a98cdff4e0b45f4c80bfcedaf926e16e0">armnn::Layer::GetOutputSlots</a></div><div class="ttdeci">const std::vector< OutputSlot > & GetOutputSlots() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00238">Layer.hpp:238</a></div></div>
<div class="ttc" id="structarmnn_1_1_i_network_properties_xhtml_a243bb6beeac3bee21a740198495f5aa1"><div class="ttname"><a href="structarmnn_1_1_i_network_properties.xhtml#a243bb6beeac3bee21a740198495f5aa1">armnn::INetworkProperties::m_OutputNetworkDetails</a></div><div class="ttdeci">const bool m_OutputNetworkDetails</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00088">IRuntime.hpp:88</a></div></div>
<div class="ttc" id="_logging_8hpp_xhtml"><div class="ttname"><a href="_logging_8hpp.xhtml">Logging.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_handle_factory_registry_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_handle_factory_registry.xhtml">armnn::TensorHandleFactoryRegistry</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_handle_factory_registry_8hpp_source.xhtml#l00020">TensorHandleFactoryRegistry.hpp:20</a></div></div>
<div class="ttc" id="classarmnn_1_1experimental_1_1_working_mem_handle_xhtml_aa1985d354ac4640b403702a4e882f852"><div class="ttname"><a href="classarmnn_1_1experimental_1_1_working_mem_handle.xhtml#aa1985d354ac4640b403702a4e882f852">armnn::experimental::WorkingMemHandle::GetMutex</a></div><div class="ttdeci">std::mutex & GetMutex() override</div><div class="ttdoc">Get a mutex which can be used for synchronizing access to the WorkingMemHandle object. </div><div class="ttdef"><b>Definition:</b> <a href="_working_mem_handle_8hpp_source.xhtml#l00058">WorkingMemHandle.hpp:58</a></div></div>
<div class="ttc" id="classarmnn_1_1_loaded_network_xhtml_aaf8558a23ae9be6e7ea165989f1fa808"><div class="ttname"><a href="classarmnn_1_1_loaded_network.xhtml#aaf8558a23ae9be6e7ea165989f1fa808">armnn::LoadedNetwork::FreeWorkingMemory</a></div><div class="ttdeci">void FreeWorkingMemory()</div><div class="ttdef"><b>Definition:</b> <a href="_loaded_network_8cpp_source.xhtml#l00781">LoadedNetwork.cpp:781</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_aa311c7fe7e05406c9ff4e4ed3ba09825"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#aa311c7fe7e05406c9ff4e4ed3ba09825">armnn::Graph::GetOutputLayers</a></div><div class="ttdeci">OutputLayersAccessor GetOutputLayers() const</div><div class="ttdoc">Returns a wrapper object with begin(), end() methods to iterate over the output layers in a range-bas...</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00189">Graph.hpp:189</a></div></div>
<div class="ttc" id="structarmnn_1_1_mem_copy_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_mem_copy_queue_descriptor.xhtml">armnn::MemCopyQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00076">WorkloadData.hpp:76</a></div></div>
<div class="ttc" id="classarmnn_1_1_loaded_network_xhtml_a878c3febb600fd2ccf3b5cb1f9a61e27"><div class="ttname"><a href="classarmnn_1_1_loaded_network.xhtml#a878c3febb600fd2ccf3b5cb1f9a61e27">armnn::LoadedNetwork::EnqueueWorkload</a></div><div class="ttdeci">Status EnqueueWorkload(const InputTensors &inputTensors, const OutputTensors &outputTensors)</div><div class="ttdoc">Single thread execution of the loaded network. </div><div class="ttdef"><b>Definition:</b> <a href="_loaded_network_8cpp_source.xhtml#l00525">LoadedNetwork.cpp:525</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255">armnn::LayerType::Constant</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml"><div class="ttname"><a href="_assert_8hpp.xhtml">Assert.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1profiling_1_1_profiling_service_xhtml"><div class="ttname"><a href="classarmnn_1_1profiling_1_1_profiling_service.xhtml">armnn::profiling::ProfilingService</a></div><div class="ttdef"><b>Definition:</b> <a href="_profiling_service_8hpp_source.xhtml#l00050">ProfilingService.hpp:50</a></div></div>
<div class="ttc" id="_layer_8hpp_xhtml"><div class="ttname"><a href="_layer_8hpp.xhtml">Layer.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a115bfc5d633eae55c67f9588acdd2bf9"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a115bfc5d633eae55c67f9588acdd2bf9">armnn::LayerType::MemImport</a></div></div>
<div class="ttc" id="classarmnn_1_1_profiler_manager_xhtml_a7b1e3e5bf386004541be2b5b22443208"><div class="ttname"><a href="classarmnn_1_1_profiler_manager.xhtml#a7b1e3e5bf386004541be2b5b22443208">armnn::ProfilerManager::RegisterProfiler</a></div><div class="ttdeci">void RegisterProfiler(IProfiler *profiler)</div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8cpp_source.xhtml#l00533">Profiling.cpp:533</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_xhtml_a9afbc055a017adf1bc38ee137bca6e90"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle.xhtml#a9afbc055a017adf1bc38ee137bca6e90">armnn::ITensorHandle::Map</a></div><div class="ttdeci">virtual const void * Map(bool blocking=true) const =0</div><div class="ttdoc">Map the tensor data for access. </div></div>
<div class="ttc" id="classarmnn_1_1_loaded_network_xhtml"><div class="ttname"><a href="classarmnn_1_1_loaded_network.xhtml">armnn::LoadedNetwork</a></div><div class="ttdef"><b>Definition:</b> <a href="_loaded_network_8hpp_source.xhtml#l00035">LoadedNetwork.hpp:35</a></div></div>
<div class="ttc" id="classarmnn_1_1_loaded_network_xhtml_ac8be6c967db9e797ade32fa3db497422"><div class="ttname"><a href="classarmnn_1_1_loaded_network.xhtml#ac8be6c967db9e797ade32fa3db497422">armnn::LoadedNetwork::GetNetworkGuid</a></div><div class="ttdeci">profiling::ProfilingGuid GetNetworkGuid()</div><div class="ttdef"><b>Definition:</b> <a href="_loaded_network_8cpp_source.xhtml#l00371">LoadedNetwork.cpp:371</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_xhtml_a563609828050f1b3a7868c23f3365923"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle.xhtml#a563609828050f1b3a7868c23f3365923">armnn::ITensorHandle::Unmap</a></div><div class="ttdeci">virtual void Unmap() const =0</div><div class="ttdoc">Unmap the tensor data. </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="classarmnn_1_1experimental_1_1_working_mem_handle_xhtml_a1a573373f4505385578f830caebf6adb"><div class="ttname"><a href="classarmnn_1_1experimental_1_1_working_mem_handle.xhtml#a1a573373f4505385578f830caebf6adb">armnn::experimental::WorkingMemHandle::IsAllocated</a></div><div class="ttdeci">bool IsAllocated() override</div><div class="ttdoc">IsAllocated returns true if the backing memory is currently allocated. The mutex must be locked...</div><div class="ttdef"><b>Definition:</b> <a href="_working_mem_handle_8hpp_source.xhtml#l00052">WorkingMemHandle.hpp:52</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a6abd491bb99ffe88bd472c1ae5a1ed1a"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">armnn::QueueDescriptor::m_Outputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Outputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00031">WorkloadData.hpp:31</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="classarmnn_1_1_layer_xhtml_af2c0edc7ea62a8baaec4d3d9b2b09256"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#af2c0edc7ea62a8baaec4d3d9b2b09256">armnn::Layer::GetOutputHandler</a></div><div class="ttdeci">const OutputHandler & GetOutputHandler(unsigned int i=0) const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00225">Layer.hpp:225</a></div></div>
<div class="ttc" id="classarmnn_1_1_backend_id_xhtml_af7445617163d3f07c47b92ae56c6cf8b"><div class="ttname"><a href="classarmnn_1_1_backend_id.xhtml#af7445617163d3f07c47b92ae56c6cf8b">armnn::BackendId::Get</a></div><div class="ttdeci">const std::string & Get() const</div><div class="ttdef"><b>Definition:</b> <a href="_backend_id_8hpp_source.xhtml#l00136">BackendId.hpp:136</a></div></div>
<div class="ttc" id="classarmnn_1_1_memory_import_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_memory_import_exception.xhtml">armnn::MemoryImportException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00125">Exceptions.hpp:125</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_factory_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_tensor_handle_factory_8hpp_source.xhtml#l00042">ITensorHandleFactory.hpp:42</a></div></div>
<div class="ttc" id="classarmnn_1_1_loaded_network_xhtml_a091ea8d2d804c8902f3120fdf2a36512"><div class="ttname"><a href="classarmnn_1_1_loaded_network.xhtml#a091ea8d2d804c8902f3120fdf2a36512">armnn::LoadedNetwork::RegisterDebugCallback</a></div><div class="ttdeci">void RegisterDebugCallback(const DebugCallbackFunction &func)</div><div class="ttdef"><b>Definition:</b> <a href="_loaded_network_8cpp_source.xhtml#l01249">LoadedNetwork.cpp:1249</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_afad4088a9a058114ee5f87246f87bf49"><div class="ttname"><a href="namespacearmnn.xhtml#afad4088a9a058114ee5f87246f87bf49">armnn::LayerGuid</a></div><div class="ttdeci">profiling::ProfilingGuid LayerGuid</div><div class="ttdoc">Define LayerGuid type. </div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00313">Types.hpp:313</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_handle_factory_registry_xhtml_ae5ecc42140a12c855554a4a621fc8456"><div class="ttname"><a href="classarmnn_1_1_tensor_handle_factory_registry.xhtml#ae5ecc42140a12c855554a4a621fc8456">armnn::TensorHandleFactoryRegistry::GetFactory</a></div><div class="ttdeci">ITensorHandleFactory * GetFactory(ITensorHandleFactory::FactoryId id) const</div><div class="ttdoc">Find a TensorHandleFactory by Id Returns nullptr if not found. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_handle_factory_registry_8cpp_source.xhtml#l00039">TensorHandleFactoryRegistry.cpp:39</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="structarmnn_1_1experimental_1_1_working_mem_descriptor_xhtml_a6abd491bb99ffe88bd472c1ae5a1ed1a"><div class="ttname"><a href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">armnn::experimental::WorkingMemDescriptor::m_Outputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Outputs</div><div class="ttdef"><b>Definition:</b> <a href="_working_mem_descriptor_8hpp_source.xhtml#l00021">WorkingMemDescriptor.hpp:21</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_factory_xhtml_a32f4aa6a7089d877af08928139c2c277"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a32f4aa6a7089d877af08928139c2c277">armnn::ITensorHandleFactory::FactoryId</a></div><div class="ttdeci">std::string FactoryId</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_tensor_handle_factory_8hpp_source.xhtml#l00045">ITensorHandleFactory.hpp:45</a></div></div>
<div class="ttc" id="include_2armnn_2backends_2_i_memory_manager_8hpp_xhtml"><div class="ttname"><a href="include_2armnn_2backends_2_i_memory_manager_8hpp.xhtml">IMemoryManager.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_memory_export_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_memory_export_exception.xhtml">armnn::MemoryExportException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00130">Exceptions.hpp:130</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about TensorInfos of a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_handle_factory_registry_xhtml_a7d5faf6ceef6e7246b3b4976869b7a18"><div class="ttname"><a href="classarmnn_1_1_tensor_handle_factory_registry.xhtml#a7d5faf6ceef6e7246b3b4976869b7a18">armnn::TensorHandleFactoryRegistry::GetMemoryManagers</a></div><div class="ttdeci">std::vector< std::shared_ptr< IMemoryManager > > & GetMemoryManagers()</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_handle_factory_registry_8hpp_source.xhtml#l00048">TensorHandleFactoryRegistry.hpp:48</a></div></div>
<div class="ttc" id="_mem_sync_workload_8hpp_xhtml"><div class="ttname"><a href="_mem_sync_workload_8hpp.xhtml">MemSyncWorkload.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a84f86b4de5adf0b164e811c87051a0ee"><div class="ttname"><a href="namespacearmnn.xhtml#a84f86b4de5adf0b164e811c87051a0ee">armnn::CheckFlag</a></div><div class="ttdeci">bool CheckFlag(MemorySourceFlags flags, MemorySource source)</div><div class="ttdef"><b>Definition:</b> <a href="_memory_sources_8hpp_source.xhtml#l00041">MemorySources.hpp:41</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a92c91193007aa49f4732d6dba5397f8d"><div class="ttname"><a href="namespacearmnn.xhtml#a92c91193007aa49f4732d6dba5397f8d">armnn::CopyTensorContentsGeneric</a></div><div class="ttdeci">void CopyTensorContentsGeneric(const ITensorHandle *srcTensor, ITensorHandle *dstTensor, CopyFunc copy)</div><div class="ttdef"><b>Definition:</b> <a href="_workload_utils_8hpp_source.xhtml#l00047">WorkloadUtils.hpp:47</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_a9a7209345edfdb2b066b0ceb66414d7c"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a9a7209345edfdb2b066b0ceb66414d7c">armnn::Graph::TopologicalSort</a></div><div class="ttdeci">Graph & TopologicalSort()</div><div class="ttdoc">Sorts layers in topological order and return this. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00177">Graph.hpp:177</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_a919fb58873ef3a6549e4490e226f2eae"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a919fb58873ef3a6549e4490e226f2eae">armnn::Graph::GetInputLayers</a></div><div class="ttdeci">InputLayersAccessor GetInputLayers() const</div><div class="ttdoc">Returns a wrapper object with begin(), end() methods to iterate over the input layers in a range-base...</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00185">Graph.hpp:185</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a4b50e46a6810018f3edecfb68b2a76b3"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">armnn::QueueDescriptor::m_Inputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Inputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00030">WorkloadData.hpp:30</a></div></div>
<div class="ttc" id="_heap_profiling_8hpp_xhtml"><div class="ttname"><a href="_heap_profiling_8hpp.xhtml">HeapProfiling.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_afdf8eb85585a798ad0e936bde884d87b"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#afdf8eb85585a798ad0e936bde884d87b">armnn::Graph::GetNumLayers</a></div><div class="ttdeci">size_t GetNumLayers() const</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00191">Graph.hpp:191</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_backend_internal_xhtml_a0e8cb8533d1d0b2cb93f926dac11dd16"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml#a0e8cb8533d1d0b2cb93f926dac11dd16">armnn::IBackendInternal::CreateMemoryManager</a></div><div class="ttdeci">virtual ARMNN_NO_DEPRECATE_WARN_END IMemoryManagerUniquePtr CreateMemoryManager() const</div><div class="ttdef"><b>Definition:</b> <a href="_i_backend_internal_8cpp_source.xhtml#l00032">IBackendInternal.cpp:32</a></div></div>
<div class="ttc" id="structarmnn_1_1_mem_sync_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_mem_sync_queue_descriptor.xhtml">armnn::MemSyncQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00089">WorkloadData.hpp:89</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a9ef4b4b6c421b5fd4b62274e63d08f11"><div class="ttname"><a href="namespacearmnn.xhtml#a9ef4b4b6c421b5fd4b62274e63d08f11">armnn::GetInputTensor</a></div><div class="ttdeci">const armnn::ConstTensor GetInputTensor(const LayerBindingId layerId, const InputTensors &inputTensors)</div><div class="ttdef"><b>Definition:</b> <a href="_loaded_network_8cpp_source.xhtml#l00977">LoadedNetwork.cpp:977</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_af7ec4c0fa4375a45a70e4e31f3d8af47"><div class="ttname"><a href="namespacearmnn.xhtml#af7ec4c0fa4375a45a70e4e31f3d8af47">armnn::GetTensorInfo</a></div><div class="ttdeci">const TensorInfo & GetTensorInfo(const ITensorHandle *tensorHandle)</div><div class="ttdoc">float32 helpers </div><div class="ttdef"><b>Definition:</b> <a href="_ref_workload_utils_8hpp_source.xhtml#l00026">RefWorkloadUtils.hpp:26</a></div></div>
<div class="ttc" id="_profiling_8hpp_xhtml"><div class="ttname"><a href="_profiling_8hpp.xhtml">Profiling.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_loaded_network_xhtml_a8e94a5375ad367ddee9c69e04e110a54"><div class="ttname"><a href="classarmnn_1_1_loaded_network.xhtml#a8e94a5375ad367ddee9c69e04e110a54">armnn::LoadedNetwork::MakeLoadedNetwork</a></div><div class="ttdeci">static std::unique_ptr< LoadedNetwork > MakeLoadedNetwork(std::unique_ptr< IOptimizedNetwork > net, std::string &errorMessage, const INetworkProperties &networkProperties, profiling::ProfilingService &profilingService)</div><div class="ttdef"><b>Definition:</b> <a href="_loaded_network_8cpp_source.xhtml#l00081">LoadedNetwork.cpp:81</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_a8d8179a4a0703602a5d7dbb6e92eaf69"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a8d8179a4a0703602a5d7dbb6e92eaf69">armnn::Graph::GetNumInputs</a></div><div class="ttdeci">size_t GetNumInputs() const</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00180">Graph.hpp:180</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_factory_xhtml_a375f11dd42ff042435e8771cf287b20c"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">armnn::ITensorHandleFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo) const =0</div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_factory_xhtml_a9c95f90eb40e31f629e0e2947b8bc6f9"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a9c95f90eb40e31f629e0e2947b8bc6f9">armnn::ITensorHandleFactory::LegacyFactoryId</a></div><div class="ttdeci">static const FactoryId LegacyFactoryId</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_tensor_handle_factory_8hpp_source.xhtml#l00046">ITensorHandleFactory.hpp:46</a></div></div>
<div class="ttc" id="classarmnn_1_1experimental_1_1_working_mem_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1experimental_1_1_working_mem_handle.xhtml">armnn::experimental::WorkingMemHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_working_mem_handle_8hpp_source.xhtml#l00024">WorkingMemHandle.hpp:24</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml">armnn::Layer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00210">Layer.hpp:210</a></div></div>
<div class="ttc" id="structarmnn_1_1_i_network_properties_xhtml_a7e26a8e7f1878d82bef452ef3531eaeb"><div class="ttname"><a href="structarmnn_1_1_i_network_properties.xhtml#a7e26a8e7f1878d82bef452ef3531eaeb">armnn::INetworkProperties::m_ProfilingEnabled</a></div><div class="ttdeci">const bool m_ProfilingEnabled</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00086">IRuntime.hpp:86</a></div></div>
<div class="ttc" id="classarmnn_1_1_backend_id_xhtml"><div class="ttname"><a href="classarmnn_1_1_backend_id.xhtml">armnn::BackendId</a></div><div class="ttdef"><b>Definition:</b> <a href="_backend_id_8hpp_source.xhtml#l00075">BackendId.hpp:75</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_a8dc12f0ee5b232d397bd18ced1a72a64"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a8dc12f0ee5b232d397bd18ced1a72a64">armnn::Layer::GetGuid</a></div><div class="ttdeci">LayerGuid GetGuid() const final</div><div class="ttdoc">Returns the unique id of the layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00322">Layer.hpp:322</a></div></div>
<div class="ttc" id="classarmnn_1_1_loaded_network_xhtml_a23e33c3caadba06bcd5b50dc2c23c19e"><div class="ttname"><a href="classarmnn_1_1_loaded_network.xhtml#a23e33c3caadba06bcd5b50dc2c23c19e">armnn::LoadedNetwork::SendNetworkStructure</a></div><div class="ttdeci">void SendNetworkStructure()</div><div class="ttdef"><b>Definition:</b> <a href="_loaded_network_8cpp_source.xhtml#l00334">LoadedNetwork.cpp:334</a></div></div>
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