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<div class="title">EndToEndTestImpl.hpp</div> </div>
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<a href="_end_to_end_test_impl_8hpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="preprocessor">#pragma once</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> </div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include <<a class="code" href="_descriptors_8hpp.xhtml">armnn/Descriptors.hpp</a>></span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_i_runtime_8hpp.xhtml">armnn/IRuntime.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_profiling_8hpp.xhtml">Profiling.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a>></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <boost/test/unit_test.hpp></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <vector></span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> {</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> </div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="keyword">template</span><<span class="keyword">typename</span> T></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="keywordtype">bool</span> ConstantUsageTest(<span class="keyword">const</span> std::vector<BackendId>& computeDevice,</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& commonTensorInfo,</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keyword">const</span> std::vector<T>& inputData,</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keyword">const</span> std::vector<T>& constantData,</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keyword">const</span> std::vector<T>& expectedOutputData)</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="comment">// Create runtime in which test will run</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = net->AddInputLayer(0);</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* constant = net->AddConstantLayer(<a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(commonTensorInfo, constantData));</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* add = net->AddAdditionLayer();</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = net->AddOutputLayer(0);</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  constant-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="comment">// Sets the tensors in the network.</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(commonTensorInfo);</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  constant-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(commonTensorInfo);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(commonTensorInfo);</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="comment">// optimize the network</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, computeDevice, runtime->GetDeviceSpec());</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> </div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="comment">// Loads it into the runtime.</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  runtime->LoadNetwork(netId, std::move(optNet));</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  std::vector<T> outputData(inputData.size());</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>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  {</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  {0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(runtime->GetInputTensorInfo(netId, 0), inputData.data())}</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>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  {</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  {0, <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  };</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="comment">// Does the inference.</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  runtime->EnqueueWorkload(netId, inputTensors, outputTensors);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="comment">// Checks the results.</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordflow">return</span> outputData == expectedOutputData;</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> }</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> </div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> <span class="keyword">inline</span> <span class="keywordtype">bool</span> ConstantUsageFloat32Test(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> {</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> commonTensorInfo({ 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> </div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordflow">return</span> ConstantUsageTest(backends,</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  commonTensorInfo,</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  std::vector<float>{ 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }, <span class="comment">// Input.</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  std::vector<float>{ 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }, <span class="comment">// Const input.</span></div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  std::vector<float>{ 7.f, 7.f, 7.f, 7.f, 7.f, 7.f } <span class="comment">// Expected output.</span></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> }</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> <span class="keyword">inline</span> <span class="keywordtype">bool</span> ConstantUsageUint8Test(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> {</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> commonTensorInfo({ 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> scale = 0.023529f;</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keyword">const</span> int8_t offset = -43;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> </div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  commonTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(scale);</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  commonTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(offset);</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">return</span> ConstantUsageTest(backends,</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  commonTensorInfo,</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  armnnUtils::QuantizedVector<uint8_t>({ 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }, scale, offset), <span class="comment">// Input.</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  armnnUtils::QuantizedVector<uint8_t>({ 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }, scale, offset), <span class="comment">// Const input.</span></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  armnnUtils::QuantizedVector<uint8_t>({ 7.f, 7.f, 7.f, 7.f, 7.f, 7.f }, scale, offset) <span class="comment">// Expected output.</span></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  );</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> }</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="comment">// Utility template for comparing tensor elements</span></div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> <span class="keywordtype">bool</span> <a class="code" href="_common_test_utils_8hpp.xhtml#ae828f1d70436f4ad1e74a5c4ecf96929">Compare</a>(T a, T b, <span class="keywordtype">float</span> tolerance = 0.000001f)</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> {</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keywordflow">if</span> (ArmnnType == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a>)</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  {</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="comment">// NOTE: Boolean is represented as uint8_t (with zero equals</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="comment">// false and everything else equals true), therefore values</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="comment">// need to be casted to bool before comparing them</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keywordflow">return</span> <span class="keyword">static_cast<</span><span class="keywordtype">bool</span><span class="keyword">></span>(a) == static_cast<bool>(b);</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  }</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> </div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="comment">// NOTE: All other types can be cast to float and compared with</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="comment">// a certain level of tolerance</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keywordflow">return</span> std::fabs(static_cast<float>(a) - static_cast<float>(b)) <= tolerance;</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> </div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> <span class="comment">// Utility function to find the number of instances of a substring within a string.</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> <span class="keywordtype">int</span> SubStringCounter(std::string& <span class="keywordtype">string</span>, std::string&& substring)</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>  std::size_t found = 0;</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="keywordtype">int</span> count = 0;</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="comment">// Look for the substring starting from where we last found the substring</span></div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordflow">while</span>((found = <span class="keywordtype">string</span>.find(substring, found)) != std::string::npos)</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  {</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  count++;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="comment">// Offset by substring length to avoid finding the same substring twice</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  found += substring.length();</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  }</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keywordflow">return</span> count;</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> }</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> </div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> <span class="keyword">template</span><<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> ArmnnIType, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> ArmnnOType,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keyword">typename</span> TInput = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType<ArmnnIType></a>, <span class="keyword">typename</span> TOutput = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType<ArmnnOType></a>></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> <span class="keywordtype">void</span> EndToEndLayerTestImpl(<a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network,</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keyword">const</span> std::map<<span class="keywordtype">int</span>, std::vector<TInput>>& inputTensorData,</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keyword">const</span> std::map<<span class="keywordtype">int</span>, std::vector<TOutput>>& expectedOutputData,</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  std::vector<BackendId> backends,</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordtype">float</span> tolerance = 0.000001f)</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> {</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="comment">// Create runtime in which test will run</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> </div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="comment">// optimize the network</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="comment">// Loads it into the runtime.</span></div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  runtime->LoadNetwork(netId, std::move(optNet));</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> </div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  inputTensors.reserve(inputTensorData.size());</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& it : inputTensorData)</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  {</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  inputTensors.push_back({it.first,</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(runtime->GetInputTensorInfo(netId, it.first), it.second.data())});</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>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors;</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  outputTensors.reserve(expectedOutputData.size());</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  std::map<int, std::vector<TOutput>> outputStorage;</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& it : expectedOutputData)</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  {</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  std::vector<TOutput> out(it.second.size());</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  outputStorage.emplace(it.first, out);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  outputTensors.push_back({it.first,</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(runtime->GetOutputTensorInfo(netId, it.first),</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  outputStorage.at(it.first).data())});</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  }</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> </div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="comment">// Does the inference.</span></div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  runtime->EnqueueWorkload(netId, inputTensors, outputTensors);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> </div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="comment">// Checks the results.</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& it : expectedOutputData)</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  {</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  std::vector<TOutput> out = outputStorage.at(it.first);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < out.size(); ++i)</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  {</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  BOOST_CHECK_MESSAGE(Compare<ArmnnOType>(it.second[i], out[i], tolerance) == <span class="keyword">true</span>,</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="stringliteral">"Actual output: "</span> << out[i] << <span class="stringliteral">". Expected output:"</span> << it.second[i]);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> </div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  }</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  }</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> }</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> ImportNonAlignedInputPointerTest(std::vector<BackendId> backends)</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> {</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</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="comment">// Create runtime in which test will run</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options));</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>  <span class="comment">// build up the structure of the network</span></div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = net->AddInputLayer(0);</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>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> descriptor;</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaceb46ca115d05c51aa5a16a8867c3304">ActivationFunction::Square</a>;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* pooling = net->AddActivationLayer(descriptor);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> </div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = net->AddOutputLayer(0);</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> </div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> </div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 1, 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 1, 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="comment">// Optimize the network</span></div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  BOOST_CHECK(optNet);</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span> </div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="comment">// Loads it into the runtime.</span></div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  std::string ignoredErrorMessage;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="comment">// Enable Importing</span></div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">true</span>, <span class="keyword">false</span>);</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> </div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="comment">// Creates structures for input & output</span></div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  std::vector<float> inputData</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>  1.0f, 2.0f, 3.0f, 4.0f</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> </div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="comment">// Misaligned input</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="keywordtype">float</span>* misalignedInputData = <span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span>*<span class="keyword">></span>(<span class="keyword">reinterpret_cast<</span><span class="keywordtype">char</span>*<span class="keyword">></span>(inputData.data()) + 1);</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span> </div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  std::vector<float> outputData(4);</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="comment">// Aligned output</span></div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keywordtype">float</span>* alignedOutputData = outputData.data();</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> </div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  {</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  {0,<a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 0), misalignedInputData)},</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  };</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  {</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  {0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), alignedOutputData)}</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  };</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> </div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  runtime->GetProfiler(netId)->EnableProfiling(<span class="keyword">true</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="comment">// Do the inference and expect it to fail with a ImportMemoryException</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  BOOST_CHECK_THROW(runtime->EnqueueWorkload(netId, inputTensors, outputTensors), <a class="code" href="classarmnn_1_1_memory_import_exception.xhtml">MemoryImportException</a>);</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> }</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> </div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> ExportNonAlignedOutputPointerTest(std::vector<BackendId> backends)</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="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> </div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="comment">// Create runtime in which test will run</span></div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options));</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="comment">// build up the structure of the network</span></div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> </div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = net->AddInputLayer(0);</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>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> descriptor;</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaceb46ca115d05c51aa5a16a8867c3304">ActivationFunction::Square</a>;</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* pooling = net->AddActivationLayer(descriptor);</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = net->AddOutputLayer(0);</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>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</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>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 1, 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 1, 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</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="comment">// Optimize the network</span></div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  BOOST_CHECK(optNet);</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> </div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="comment">// Loads it into the runtime.</span></div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  std::string ignoredErrorMessage;</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="comment">// Enable Importing and Exporting</span></div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">true</span>, <span class="keyword">true</span>);</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);</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">// Creates structures for input & output</span></div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  std::vector<float> inputData</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  {</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  1.0f, 2.0f, 3.0f, 4.0f, 5.0f</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  };</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span> </div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="comment">// Aligned input</span></div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="keywordtype">float</span>* alignedInputData = inputData.data();</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>  std::vector<float> outputData(5);</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>  <span class="comment">// Misaligned output</span></div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="keywordtype">float</span>* misalignedOutputData = <span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span>*<span class="keyword">></span>(<span class="keyword">reinterpret_cast<</span><span class="keywordtype">char</span>*<span class="keyword">></span>(outputData.data()) + 1);</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> </div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  {</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  {0,<a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 0), alignedInputData)},</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>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</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>  {0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), misalignedOutputData)}</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  };</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span> </div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <span class="comment">// Do the inference and expect it to fail with a ExportMemoryException</span></div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="keywordflow">if</span> (backends[0] == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a>)</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  {</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="comment">// For CpuAcc the NeonTensorHandle will throw its own exception on misaligned memory</span></div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  BOOST_CHECK_THROW(runtime->EnqueueWorkload(netId, inputTensors, outputTensors), <a class="code" href="classarmnn_1_1_memory_import_exception.xhtml">MemoryImportException</a>);</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  }</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keywordflow">else</span></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>  BOOST_CHECK_THROW(runtime->EnqueueWorkload(netId, inputTensors, outputTensors), <a class="code" href="classarmnn_1_1_memory_export_exception.xhtml">MemoryExportException</a>);</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  }</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span> }</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span> </div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> ImportAlignedPointerTest(std::vector<BackendId> backends)</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>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span> </div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="comment">// Create runtime in which test will run</span></div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options));</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> </div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="comment">// build up the structure of the network</span></div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span> </div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = net->AddInputLayer(0);</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span> </div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> descriptor;</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaceb46ca115d05c51aa5a16a8867c3304">ActivationFunction::Square</a>;</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* pooling = net->AddActivationLayer(descriptor);</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span> </div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = net->AddOutputLayer(0);</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span> </div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span> </div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 1, 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 1, 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> </div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="comment">// Optimize the network</span></div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  BOOST_CHECK(optNet);</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span> </div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="comment">// Loads it into the runtime.</span></div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  std::string ignoredErrorMessage;</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="comment">// Enable Importing</span></div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">true</span>, <span class="keyword">true</span>);</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);</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>  <span class="comment">// Creates structures for input & output</span></div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  std::vector<float> inputData</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  {</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  1.0f, 2.0f, 3.0f, 4.0f</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  };</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span> </div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  std::vector<float> outputData(4);</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span> </div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  std::vector<float> expectedOutput</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  {</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  1.0f, 4.0f, 9.0f, 16.0f</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  };</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span> </div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  {</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  {0,<a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 0), inputData.data())},</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>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</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>  {0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}</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> </div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  runtime->GetProfiler(netId)->EnableProfiling(<span class="keyword">true</span>);</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> </div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="comment">// Do the inference</span></div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  runtime->EnqueueWorkload(netId, inputTensors, outputTensors);</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span> </div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="comment">// Retrieve the Profiler.Print() output to get the workload execution</span></div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <a class="code" href="classarmnn_1_1_profiler_manager.xhtml">ProfilerManager</a>& profilerManager = <a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a>();</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  std::stringstream ss;</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  profilerManager.<a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">GetProfiler</a>()-><a class="code" href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">Print</a>(ss);;</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  std::string dump = ss.str();</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span> </div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="comment">// Contains ActivationWorkload</span></div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  std::size_t found = dump.find(<span class="stringliteral">"ActivationWorkload"</span>);</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span> </div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="comment">// Contains SyncMemGeneric</span></div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  found = dump.find(<span class="stringliteral">"SyncMemGeneric"</span>);</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span> </div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="comment">// Does not contain CopyMemGeneric</span></div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  found = dump.find(<span class="stringliteral">"CopyMemGeneric"</span>);</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  BOOST_TEST(found == std::string::npos);</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span> </div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  <span class="comment">// Check output is as expected</span></div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  BOOST_TEST(outputData == expectedOutput);</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span> }</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span> </div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> ImportOnlyWorkload(std::vector<BackendId> backends)</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>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> </div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</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>  <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = net->AddInputLayer(0);</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span> </div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> descriptor;</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaceb46ca115d05c51aa5a16a8867c3304">ActivationFunction::Square</a>;</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* pooling = net->AddActivationLayer(descriptor);</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = net->AddOutputLayer(0);</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span> </div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span> </div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 1, 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 1, 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span> </div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <span class="comment">// optimize the network</span></div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span> </div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Load Network"</span>);</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="comment">// Load it into the runtime. It should pass.</span></div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  std::string ignoredErrorMessage;</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">true</span>, <span class="keyword">false</span>);</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  BOOST_TEST(runtime->LoadNetwork(netId, std::move(optNet),ignoredErrorMessage, networkProperties)</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  == <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a>);</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span> </div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Generate Data"</span>);</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="comment">// Creates structures for input & output</span></div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  std::vector<float> inputData</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>  1.0f, 2.0f, 3.0f, 4.0f</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  };</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span> </div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  std::vector<float> outputData(4);</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>  std::vector<float> expectedOutput</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  {</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  1.0f, 4.0f, 9.0f, 16.0f</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  };</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span> </div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Create Network"</span>);</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</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>  {0,<a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 0), inputData.data())},</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>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</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>  {0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}</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>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Get Profiler"</span>);</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span> </div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  runtime->GetProfiler(netId)->EnableProfiling(<span class="keyword">true</span>);</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span> </div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Run Inference"</span>);</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="comment">// Do the inference</span></div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  runtime->EnqueueWorkload(netId, inputTensors, outputTensors);</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span> </div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Print Profiler"</span>);</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <span class="comment">// Retrieve the Profiler.Print() output to get the workload execution</span></div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <a class="code" href="classarmnn_1_1_profiler_manager.xhtml">ProfilerManager</a>& profilerManager = <a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a>();</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  std::stringstream ss;</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  profilerManager.<a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">GetProfiler</a>()-><a class="code" href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">Print</a>(ss);;</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  std::string dump = ss.str();</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span> </div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="comment">// Check there are no SyncMemGeneric workloads as we didn't export</span></div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Find SyncMemGeneric"</span>);</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <span class="keywordtype">int</span> count = SubStringCounter(dump, <span class="stringliteral">"SyncMemGeneric"</span>);</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  BOOST_TEST(count == 0);</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span> </div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <span class="comment">// Should only be 1 CopyMemGeneric for the output as we imported</span></div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Find CopyMemGeneric"</span>);</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  count = SubStringCounter(dump, <span class="stringliteral">"CopyMemGeneric"</span>);</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  BOOST_TEST(count == 1);</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>  <span class="comment">// Check the output is correct</span></div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  BOOST_CHECK_EQUAL_COLLECTIONS(outputData.begin(), outputData.end(), expectedOutput.begin(), expectedOutput.end());</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span> }</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> <span class="keyword">inline</span> <span class="keywordtype">void</span> ExportOnlyWorkload(std::vector<BackendId> backends)</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>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</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>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span> </div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span> </div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = net->AddInputLayer(0);</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span> </div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> descriptor;</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaceb46ca115d05c51aa5a16a8867c3304">ActivationFunction::Square</a>;</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* pooling = net->AddActivationLayer(descriptor);</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = net->AddOutputLayer(0);</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span> </div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</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>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 1, 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 1, 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span> </div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <span class="comment">// optimize the network</span></div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec());</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>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Load Network"</span>);</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="comment">// Load it into the runtime. It should pass.</span></div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  std::string ignoredErrorMessage;</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">false</span>, <span class="keyword">true</span>);</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  BOOST_TEST(runtime->LoadNetwork(netId, std::move(optNet),ignoredErrorMessage, networkProperties)</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  == <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a>);</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span> </div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Generate Data"</span>);</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <span class="comment">// Creates structures for input & output</span></div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  std::vector<float> inputData</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>  1.0f, 2.0f, 3.0f, 4.0f</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>  std::vector<float> outputData(4);</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>  std::vector<float> expectedOutput</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  {</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  1.0f, 4.0f, 9.0f, 16.0f</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  };</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>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Create Network"</span>);</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</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>  {0,<a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 0), inputData.data())},</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>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</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>  {0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  };</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span> </div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Get Profiler"</span>);</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span> </div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  runtime->GetProfiler(netId)->EnableProfiling(<span class="keyword">true</span>);</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span> </div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Run Inference"</span>);</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <span class="comment">// Do the inference</span></div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  runtime->EnqueueWorkload(netId, inputTensors, outputTensors);</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span> </div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Print Profiler"</span>);</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <span class="comment">// Retrieve the Profiler.Print() output to get the workload execution</span></div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <a class="code" href="classarmnn_1_1_profiler_manager.xhtml">ProfilerManager</a>& profilerManager = <a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a>();</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  std::stringstream ss;</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  profilerManager.<a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">GetProfiler</a>()-><a class="code" href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">Print</a>(ss);;</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  std::string dump = ss.str();</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span> </div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <span class="comment">// Check there is a SyncMemGeneric workload as we exported</span></div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Find SyncMemGeneric"</span>);</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  <span class="keywordtype">int</span> count = SubStringCounter(dump, <span class="stringliteral">"SyncMemGeneric"</span>);</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  BOOST_TEST(count == 1);</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span> </div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <span class="comment">// Should be 1 CopyMemGeneric for the output as we did not import</span></div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Find CopyMemGeneric"</span>);</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  count = SubStringCounter(dump, <span class="stringliteral">"CopyMemGeneric"</span>);</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  BOOST_TEST(count == 1);</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="comment">// Check the output is correct</span></div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  BOOST_CHECK_EQUAL_COLLECTIONS(outputData.begin(), outputData.end(), expectedOutput.begin(), expectedOutput.end());</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span> }</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span> </div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> ImportAndExportWorkload(std::vector<BackendId> backends)</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span> {</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span> </div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</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">// Builds up the structure of the network.</span></div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span> </div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = net->AddInputLayer(0);</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span> </div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> descriptor;</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaceb46ca115d05c51aa5a16a8867c3304">ActivationFunction::Square</a>;</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* pooling = net->AddActivationLayer(descriptor);</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = net->AddOutputLayer(0);</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>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</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>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 1, 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 1, 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span> </div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec());</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>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Load Network"</span>);</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <span class="comment">// Load it into the runtime. It should pass.</span></div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  std::string ignoredErrorMessage;</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">true</span>, <span class="keyword">true</span>);</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  BOOST_TEST(runtime->LoadNetwork(netId, std::move(optNet),ignoredErrorMessage, networkProperties)</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  == <a class="code" href="namespacearmnn.xhtml#a67a0db04d321a74b7e7fcfd3f1a3f70ba505a83f220c02df2f85c3810cd9ceb38">Status::Success</a>);</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span> </div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Generate Data"</span>);</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <span class="comment">// Creates structures for input & output</span></div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  std::vector<float> inputData</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  {</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  1.0f, 2.0f, 3.0f, 4.0f</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  };</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span> </div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  std::vector<float> outputData(4);</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span> </div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  std::vector<float> expectedOutput</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>  1.0f, 4.0f, 9.0f, 16.0f</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> </div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Create Network"</span>);</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  {</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  {0,<a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 0), inputData.data())},</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  };</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</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>  {0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</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>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Get Profiler"</span>);</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span> </div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  runtime->GetProfiler(netId)->EnableProfiling(<span class="keyword">true</span>);</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span> </div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Run Inference"</span>);</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  <span class="comment">// Do the inference</span></div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  runtime->EnqueueWorkload(netId, inputTensors, outputTensors);</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>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Print Profiler"</span>);</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <span class="comment">// Retrieve the Profiler.Print() output to get the workload execution</span></div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <a class="code" href="classarmnn_1_1_profiler_manager.xhtml">ProfilerManager</a>& profilerManager = <a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a>();</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  std::stringstream ss;</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  profilerManager.<a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">GetProfiler</a>()-><a class="code" href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">Print</a>(ss);;</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  std::string dump = ss.str();</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span> </div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  <span class="comment">// Check there is a SyncMemGeneric workload as we exported</span></div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Find SyncMemGeneric"</span>);</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <span class="keywordtype">int</span> count = SubStringCounter(dump, <span class="stringliteral">"SyncMemGeneric"</span>);</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  BOOST_TEST(count == 1);</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>  <span class="comment">// Shouldn't be any CopyMemGeneric workloads</span></div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"Find CopyMemGeneric"</span>);</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  count = SubStringCounter(dump, <span class="stringliteral">"CopyMemGeneric"</span>);</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  BOOST_TEST(count == 0);</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="comment">// Check the output is correct</span></div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  BOOST_CHECK_EQUAL_COLLECTIONS(outputData.begin(), outputData.end(), expectedOutput.begin(), expectedOutput.end());</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> </div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> ExportOutputWithSeveralOutputSlotConnectionsTest(std::vector<BackendId> backends)</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span> {</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span> </div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  <span class="comment">// Create runtime in which test will run</span></div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options));</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span> </div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  <span class="comment">// build up the structure of the network</span></div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span> </div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = net->AddInputLayer(0);</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span> </div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> descriptor;</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaceb46ca115d05c51aa5a16a8867c3304">ActivationFunction::Square</a>;</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activation = net->AddActivationLayer(descriptor);</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span> </div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output0 = net->AddOutputLayer(0);</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output1 = net->AddOutputLayer(1);</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span> </div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(activation-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  activation-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  activation-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span> </div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 1, 4, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  activation-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 1, 4, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span> </div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  <span class="comment">// Optimize the network</span></div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec());</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="comment">// Loads it into the runtime.</span></div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  std::string ignoredErrorMessage;</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  <span class="comment">// Enable Importing</span></div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">true</span>, <span class="keyword">true</span>);</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span> </div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  <span class="comment">// Creates structures for input & output</span></div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  std::vector<float> inputData</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>  1.0f, 2.0f, 3.0f, 4.0f</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> </div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  std::vector<float> outputData0(4);</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  std::vector<float> outputData1(4);</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span> </div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  std::vector<float> expectedOutput</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  {</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  1.0f, 4.0f, 9.0f, 16.0f</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> </div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  {</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  {0,<a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 0), inputData.data())},</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>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</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>  {0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), outputData0.data())},</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  {1,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 1), outputData1.data())}</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  };</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span> </div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  <span class="comment">// The result of the inference is not important, just the fact that there</span></div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  <span class="comment">// should not be CopyMemGeneric workloads.</span></div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  runtime->GetProfiler(netId)->EnableProfiling(<span class="keyword">true</span>);</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span> </div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  <span class="comment">// Do the inference</span></div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  runtime->EnqueueWorkload(netId, inputTensors, outputTensors);</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span> </div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  <span class="comment">// Retrieve the Profiler.Print() output to get the workload execution</span></div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  <a class="code" href="classarmnn_1_1_profiler_manager.xhtml">ProfilerManager</a>& profilerManager = <a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a>();</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  std::stringstream ss;</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  profilerManager.<a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">GetProfiler</a>()-><a class="code" href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">Print</a>(ss);</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  std::string dump = ss.str();</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span> </div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  std::size_t found = std::string::npos;</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>  <span class="keywordflow">if</span> (backends[0] == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">Compute::CpuRef</a>)</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>  found = dump.find(<span class="stringliteral">"RefActivationWorkload"</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="keywordflow">else</span> <span class="keywordflow">if</span> (backends[0] == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a>)</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>  found = dump.find(<span class="stringliteral">"NeonActivationWorkload"</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="keywordflow">else</span> <span class="keywordflow">if</span> (backends[0] == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">Compute::GpuAcc</a>)</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  {</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  found = dump.find(<span class="stringliteral">"ClActivationWorkload"</span>);</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  }</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span> </div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  <span class="comment">// No contains SyncMemGeneric</span></div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  found = dump.find(<span class="stringliteral">"SyncMemGeneric"</span>);</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  BOOST_TEST(found == std::string::npos);</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  <span class="comment">// Contains CopyMemGeneric</span></div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  found = dump.find(<span class="stringliteral">"CopyMemGeneric"</span>);</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span> </div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  <span class="comment">// Check that the outputs are correct</span></div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  BOOST_CHECK_EQUAL_COLLECTIONS(outputData0.begin(), outputData0.end(),</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  expectedOutput.begin(), expectedOutput.end());</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  BOOST_CHECK_EQUAL_COLLECTIONS(outputData1.begin(), outputData1.end(),</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  expectedOutput.begin(), expectedOutput.end());</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span> }</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span> </div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> StridedSliceInvalidSliceEndToEndTest(std::vector<BackendId> backends)</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span> {</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</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="comment">// Create runtime in which test will run</span></div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options));</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span> </div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  <span class="comment">// build up the structure of the network</span></div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span> </div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = net->AddInputLayer(0);</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span> </div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  <span class="comment">// Configure a strided slice with a stride the same size as the input but with a ShrinkAxisMask on the first</span></div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  <span class="comment">// dim of the output to make it too small to hold the specified slice.</span></div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml">StridedSliceDescriptor</a> descriptor;</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  descriptor.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a118fe06b7c2599da60398ee311ede923">m_Begin</a> = {0, 0};</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  descriptor.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#aa68194dd6258ab5b04123005a066ea25">m_End</a> = {2, 3};</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  descriptor.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a0d53caff836b84204adbd1c28752a201">m_Stride</a> = {1, 1};</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  descriptor.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a61081be1483984e33db452c75d569f51">m_BeginMask</a> = 0;</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  descriptor.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#ac37e49c0d6e6e54f9d2015d0f11f8ee7">m_EndMask</a> = 0;</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  descriptor.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a6d0384878432cfc9652b7ae8bc59506f">m_ShrinkAxisMask</a> = 1;</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* stridedSlice = net->AddStridedSliceLayer(descriptor);</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output0 = net->AddOutputLayer(0);</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>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(stridedSlice-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  stridedSlice-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</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>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  stridedSlice-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span> </div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  <span class="comment">// Attempt to optimize the network and check that the correct exception is thrown</span></div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  BOOST_CHECK_THROW(<a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec()), <a class="code" href="classarmnn_1_1_layer_validation_exception.xhtml">armnn::LayerValidationException</a>);</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> </div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span> } <span class="comment">// anonymous namespace</span></div><div class="ttc" id="classarmnn_1_1_i_runtime_xhtml_ad44ecd3700748dc30dc4bbe34ba5bde7"><div class="ttname"><a href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a></div><div class="ttdeci">static IRuntimePtr Create(const CreationOptions &options)</div><div class="ttdef"><b>Definition:</b> <a href="_runtime_8cpp_source.xhtml#l00037">Runtime.cpp:37</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00062">INetwork.hpp:62</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea83c2c4e9b658ccafbcbe6309c5d84c64">armnn::Compute::CpuRef</a></div><div class="ttdoc">CPU Execution: Reference C++ kernels. </div></div>
<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_a6d0384878432cfc9652b7ae8bc59506f"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a6d0384878432cfc9652b7ae8bc59506f">armnn::StridedSliceDescriptor::m_ShrinkAxisMask</a></div><div class="ttdeci">int32_t m_ShrinkAxisMask</div><div class="ttdoc">Shrink axis mask value. If set, the nth specification shrinks the dimensionality by 1...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01128">Descriptors.hpp:1128</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#l00489">Profiling.cpp:489</a></div></div>
<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_a118fe06b7c2599da60398ee311ede923"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a118fe06b7c2599da60398ee311ede923">armnn::StridedSliceDescriptor::m_Begin</a></div><div class="ttdeci">std::vector< int > m_Begin</div><div class="ttdoc">Begin values for the input that will be sliced. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01115">Descriptors.hpp:1115</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="_quantize_helper_8hpp_xhtml"><div class="ttname"><a href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a150468a02bd7b2d2d061c4aaaee939f0"><div class="ttname"><a href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a></div><div class="ttdeci">std::unique_ptr< IRuntime, void(*)(IRuntime *runtime)> IRuntimePtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00026">IRuntime.hpp:26</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_profiler_xhtml_a038bb767bbc6abc0ee0d9b509616b896"><div class="ttname"><a href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">armnn::IProfiler::Print</a></div><div class="ttdeci">void Print(std::ostream &outStream) const</div><div class="ttdoc">Print stats for events in JSON Format to the given output stream. </div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8cpp_source.xhtml#l00522">Profiling.cpp:522</a></div></div>
<div class="ttc" id="_i_runtime_8hpp_xhtml"><div class="ttname"><a href="_i_runtime_8hpp.xhtml">IRuntime.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a0743ed5e860c316a20b68ca96301b411"><div class="ttname"><a href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a></div><div class="ttdeci">typename ResolveTypeImpl< DT >::Type ResolveType</div><div class="ttdef"><b>Definition:</b> <a href="_resolve_type_8hpp_source.xhtml#l00073">ResolveType.hpp:73</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#l00340">Tensor.hpp:340</a></div></div>
<div class="ttc" id="_resolve_type_8hpp_xhtml"><div class="ttname"><a href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a83015160d8c67d5d77735eb0d4033d9a"><div class="ttname"><a href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">armnn::NetworkId</a></div><div class="ttdeci">int NetworkId</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00020">IRuntime.hpp:20</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="structarmnn_1_1_strided_slice_descriptor_xhtml_a61081be1483984e33db452c75d569f51"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a61081be1483984e33db452c75d569f51">armnn::StridedSliceDescriptor::m_BeginMask</a></div><div class="ttdeci">int32_t m_BeginMask</div><div class="ttdoc">Begin mask value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01123">Descriptors.hpp:1123</a></div></div>
<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_ac37e49c0d6e6e54f9d2015d0f11f8ee7"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#ac37e49c0d6e6e54f9d2015d0f11f8ee7">armnn::StridedSliceDescriptor::m_EndMask</a></div><div class="ttdeci">int32_t m_EndMask</div><div class="ttdoc">End mask value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01126">Descriptors.hpp:1126</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &tensorInfo)=0</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#l00028">IRuntime.hpp:28</a></div></div>
<div class="ttc" id="classarmnn_1_1_profiler_manager_xhtml_a3756986bc88b9b212d3f983c70c5c129"><div class="ttname"><a href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">armnn::ProfilerManager::GetProfiler</a></div><div class="ttdeci">IProfiler * GetProfiler()</div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8cpp_source.xhtml#l00501">Profiling.cpp:501</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#l00306">Tensor.hpp:306</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="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00032">Types.hpp:32</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a82e98ef05fd67036d1195ba17174d685"><div class="ttname"><a href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a></div><div class="ttdeci">IOptimizedNetworkPtr Optimize(const INetwork &network, const std::vector< BackendId > &backendPreferences, const IDeviceSpec &deviceSpec, const OptimizerOptions &options=OptimizerOptions(), Optional< std::vector< std::string > &> messages=EmptyOptional())</div><div class="ttdoc">Create an optimized version of the network. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l01502">Network.cpp:1502</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_validation_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer_validation_exception.xhtml">armnn::LayerValidationException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00105">Exceptions.hpp:105</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#l00314">Tensor.hpp:314</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#l00341">Tensor.hpp:341</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a674efcf6cbdb9e831d653ff0e821fb38"><div class="ttname"><a href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a></div><div class="ttdeci">std::unique_ptr< IOptimizedNetwork, void(*)(IOptimizedNetwork *network)> IOptimizedNetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00174">INetwork.hpp:174</a></div></div>
<div class="ttc" id="classarmnn_1_1_profiler_manager_xhtml"><div class="ttname"><a href="classarmnn_1_1_profiler_manager.xhtml">armnn::ProfilerManager</a></div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8hpp_source.xhtml#l00091">Profiling.hpp:91</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a685739c4eb65a580e075282cfe6787d6"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">armnn::TensorInfo::SetQuantizationScale</a></div><div class="ttdeci">void SetQuantizationScale(float scale)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00464">Tensor.cpp:464</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a></div><div class="ttdoc">GPU Execution: OpenCL: ArmCompute. </div></div>
<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_a0d53caff836b84204adbd1c28752a201"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a0d53caff836b84204adbd1c28752a201">armnn::StridedSliceDescriptor::m_Stride</a></div><div class="ttdeci">std::vector< int > m_Stride</div><div class="ttdoc">Stride values for the input that will be sliced. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01119">Descriptors.hpp:1119</a></div></div>
<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a></div><div class="ttdoc">An ActivationDescriptor for the ActivationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00025">Descriptors.hpp:25</a></div></div>
<div class="ttc" id="_i_network_8hpp_xhtml"><div class="ttname"><a href="_i_network_8hpp.xhtml">INetwork.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_i_runtime_1_1_creation_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00043">IRuntime.hpp:43</a></div></div>
<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_aa68194dd6258ab5b04123005a066ea25"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#aa68194dd6258ab5b04123005a066ea25">armnn::StridedSliceDescriptor::m_End</a></div><div class="ttdeci">std::vector< int > m_End</div><div class="ttdoc">End values for the input that will be sliced. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01117">Descriptors.hpp:1117</a></div></div>
<div class="ttc" id="_descriptors_8hpp_xhtml"><div class="ttname"><a href="_descriptors_8hpp.xhtml">Descriptors.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a></div><div class="ttdoc">CPU Execution: NEON: ArmCompute. </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_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot & GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaaceb46ca115d05c51aa5a16a8867c3304"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaceb46ca115d05c51aa5a16a8867c3304">armnn::ActivationFunction::Square</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_strided_slice_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml">armnn::StridedSliceDescriptor</a></div><div class="ttdoc">A StridedSliceDescriptor for the StridedSliceLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01076">Descriptors.hpp:1076</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot & GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index. </div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a63cbc581012c957f9d68d224ddc3e43c"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">armnn::TensorInfo::SetQuantizationOffset</a></div><div class="ttdeci">void SetQuantizationOffset(int32_t offset)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00480">Tensor.cpp:480</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00173">INetwork.hpp:173</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &destination)=0</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_i_network_xhtml_a464f0ff87b1aabf71febaa71321dd40b"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create(NetworkOptions networkOptions={})</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00510">Network.cpp:510</a></div></div>
<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml_af10fa7883e3579950f477bee92a64844"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">armnn::ActivationDescriptor::m_Function</a></div><div class="ttdeci">ActivationFunction m_Function</div><div class="ttdoc">The activation function to use (Sigmoid, TanH, Linear, ReLu, BoundedReLu, SoftReLu, LeakyReLu, Abs, Sqrt, Square, Elu). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00048">Descriptors.hpp:48</a></div></div>
<div class="ttc" id="_common_test_utils_8hpp_xhtml_ae828f1d70436f4ad1e74a5c4ecf96929"><div class="ttname"><a href="_common_test_utils_8hpp.xhtml#ae828f1d70436f4ad1e74a5c4ecf96929">Compare</a></div><div class="ttdeci">bool Compare(T a, T b, float tolerance=0.000001f)</div><div class="ttdef"><b>Definition:</b> <a href="_common_test_utils_8hpp_source.xhtml#l00057">CommonTestUtils.hpp:57</a></div></div>
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