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<div class="title">ActivationEndToEndTestImpl.hpp</div> </div>
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<a href="_activation_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 © 2020 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="_end_to_end_test_impl_8hpp.xhtml">EndToEndTestImpl.hpp</a>"</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_types_utils_8hpp.xhtml">armnn/TypesUtils.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_common_test_utils_8hpp.xhtml">backendsCommon/test/CommonTestUtils.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a>></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> </div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <boost/test/unit_test_log.hpp></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> </div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> {</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"></span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment">/** Defines the acceptable tolerance of ActivationFunction-DataType combinations.</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> *</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * @param activationFunction The activation function used</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * @param dataType Data type used</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> *</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="comment"> * @return Tolerance depending on the activation function and data type</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="comment"> */</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="keywordtype">float</span> GetActivationTolerance(<span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9ea">armnn::ActivationFunction</a>& activationFunction, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dataType)</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> {</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  constexpr <span class="keywordtype">float</span> defaultTolerance = 1e-6f;</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keywordflow">switch</span> (activationFunction)</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  {</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="comment">// The following values are taken from ArmComputeLibrary/tests/validation/CL/ActivationLayer.cpp</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordflow">case</span> ActivationFunction::Elu:</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keywordflow">return</span> (dataType == DataType::Float16 ? 0.01f : 0.00001f);</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keywordflow">case</span> ActivationFunction::HardSwish:</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keywordflow">return</span> (dataType == DataType::Float16 ? 0.01f : defaultTolerance);</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordflow">return</span> defaultTolerance;</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  }</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> }</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="comment"></span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="comment">/** Creates a network with one layer of the activation function specified in the activation descriptor.</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="comment"> *</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="comment"> * @param inputInfo Tensor info of inputs</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="comment"> * @param outputInfo Tensor info of outputs</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="comment"> * @param descriptor Activation descriptor</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="comment"> *</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="comment"> * @return INetworkPtr A pointer to the created network</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> <span class="comment"> */</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> CreateActivationNetwork(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& inputInfo,</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& outputInfo,</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a>& descriptor)</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> {</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> </div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordtype">char</span> <span class="keyword">const</span>* ActivationName = <a class="code" href="namespacearmnn.xhtml#aa093207ea7c4e7a9c9abe40d2f57995b">GetActivationFunctionAsCString</a>(descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a>);</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</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="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = net->AddInputLayer(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* prelu = net->AddActivationLayer(descriptor, ActivationName);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = net->AddOutputLayer(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, prelu, inputInfo, 0, 0);</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(prelu, output, outputInfo, 0, 0);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keywordflow">return</span> net;</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> }</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> <span class="comment"></span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> <span class="comment">/** Specifies the implementation of end to end tests for activation functions.</span></div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> <span class="comment"> *</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> <span class="comment"> * - Converts input data and expected-output data to the data type that is desired for the test (ArmnnType)</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> <span class="comment"> * - Creates a network with one layer of the activation function specified in the activation descriptor.</span></div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> <span class="comment"> * - Executes the network on specified backends and compares results to expected output values</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> <span class="comment"> *</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> <span class="comment"> * @tparam ArmnnType The armnn data type for the input and expected-output data</span></div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> <span class="comment"> * @param backends Backends to run test on</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> <span class="comment"> * @param floatInputData Input data given as vector of float</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> <span class="comment"> * @param floatExpectedOutputData Expected output data given as vector of float</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <span class="comment"> * @param inputInfo Tensor info of inputs</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <span class="comment"> * @param outputInfo Tensor info of outputs</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> <span class="comment"> * @param descriptor Activation descriptor</span></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> <span class="comment"> */</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> <span class="keywordtype">void</span> ActivationEndToEndImpl(<span class="keyword">const</span> std::vector<armnn::BackendId>& backends,</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keyword">const</span> std::vector<float>& floatInputData,</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keyword">const</span> std::vector<float>& floatExpectedOutputData,</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& inputInfo,</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& outputInfo,</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a>& descriptor)</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> {</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</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="comment">// Selectively quantizes/transforms float values to the needed data type</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  std::vector<T> inputData = armnnUtils::QuantizedVector<T>( floatInputData,</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>());</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  std::vector<T> expectedOutputData = armnnUtils::QuantizedVector<T>( floatExpectedOutputData,</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>());</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> </div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateActivationNetwork(inputInfo, outputInfo, descriptor);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> </div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } };</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  std::map<int, std::vector<T>> expectedOutputTensorData = { { 0, expectedOutputData } };</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>  <span class="keywordtype">float</span> tolerance = GetActivationTolerance(descriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a>, ArmnnType);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> </div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net),</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  inputTensorData,</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  expectedOutputTensorData,</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  backends,</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  tolerance);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> }</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> <span class="comment"></span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> <span class="comment">/** Executes an end to end test for Elu activation with specific input and expected-output data</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> <span class="comment"> *</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> <span class="comment"> * @tparam ArmnnType The armnn data type for the input and expected-output data</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> <span class="comment"> * @param backends The backends on which to run the test</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> <span class="comment"> */</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> <span class="keywordtype">void</span> EluEndToEndTest(<span class="keyword">const</span> std::vector<BackendId>& backends)</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>  std::vector<float> floatInputData{ -2.0f, -1.0f, -0.0f, 0.0f,</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  1.0f, 2.0f, 3.0f, 4.0f };</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> </div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  std::vector<float> floatExpectedOutputData{ -0.86466471676f, -0.63212055882f, -0.0f, 0.0f,</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  1.0f , 2.0f , 3.0f, 4.0f };</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> </div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordtype">float</span> qScale = 1.0f;</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  int32_t qOffset = 0;</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({ 2, 2, 2, 1 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 2, 2, 2, 1 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> </div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a> descriptor(<a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaed67cf7d54c570e4c4891800f085f41d">ActivationFunction::Elu</a>, 1.0);</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>  ActivationEndToEndImpl<ArmnnType>(backends,</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  floatInputData,</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  floatExpectedOutputData,</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  inputInfo,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  outputInfo,</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  descriptor);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> }</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> <span class="comment"></span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> <span class="comment">/** Executes an end to end test for HardSwish activation with specific input and expected-output data</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> <span class="comment"> *</span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> <span class="comment"> * @tparam ArmnnType The armnn data type for the input and expected-output data</span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="comment"> * @param backends The backends on which to run the test</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> <span class="comment"> */</span></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> <span class="keywordtype">void</span> HardSwishEndToEndTest(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> {</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  std::vector<float> floatInputData{ -2.0f, -1.0f, -0.5f, 0.0f,</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  1.0f, 2.0f, 3.0f, 4.0f };</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> </div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  std::vector<float> floatExpectedOutputData{ -0.33333333333f, -0.33333333333f, -0.208333f, 0.0f,</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  0.66666666667f, 1.66666666667f, 3.0f , 4.0f };</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> </div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keywordtype">float</span> qScale = 1.0f;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  int32_t qOffset = 0;</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({ 2, 2, 2, 1 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 2, 2, 2, 1 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span> </div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a> descriptor(<a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186">ActivationFunction::HardSwish</a>, 1.0);</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>  ActivationEndToEndImpl<ArmnnType>(backends,</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  floatInputData,</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  floatExpectedOutputData,</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  inputInfo,</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  outputInfo,</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  descriptor);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> }</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> </div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> } <span class="comment">// anonymous namespace</span></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="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="_resolve_type_8hpp_xhtml"><div class="ttname"><a href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a></div></div>
<div class="ttc" id="_end_to_end_test_impl_8hpp_xhtml"><div class="ttname"><a href="_end_to_end_test_impl_8hpp.xhtml">EndToEndTestImpl.hpp</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="_common_test_utils_8hpp_xhtml"><div class="ttname"><a href="_common_test_utils_8hpp.xhtml">CommonTestUtils.hpp</a></div></div>
<div class="ttc" id="_types_utils_8hpp_xhtml"><div class="ttname"><a href="_types_utils_8hpp.xhtml">TypesUtils.hpp</a></div></div>
<div class="ttc" id="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="classarmnn_1_1_tensor_info_xhtml_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">armnn::TensorInfo::GetQuantizationOffset</a></div><div class="ttdeci">int32_t GetQuantizationOffset() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00469">Tensor.cpp:469</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00452">Tensor.cpp:452</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="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaaed67cf7d54c570e4c4891800f085f41d"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaed67cf7d54c570e4c4891800f085f41d">armnn::ActivationFunction::Elu</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_aa093207ea7c4e7a9c9abe40d2f57995b"><div class="ttname"><a href="namespacearmnn.xhtml#aa093207ea7c4e7a9c9abe40d2f57995b">armnn::GetActivationFunctionAsCString</a></div><div class="ttdeci">constexpr char const * GetActivationFunctionAsCString(ActivationFunction activation)</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00027">TypesUtils.hpp:27</a></div></div>
<div class="ttc" id="_test_utils_8cpp_xhtml_a0b295acb179f15eb3fb862b32008f782"><div class="ttname"><a href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a></div><div class="ttdeci">void Connect(armnn::IConnectableLayer *from, armnn::IConnectableLayer *to, const armnn::TensorInfo &tensorInfo, unsigned int fromIndex, unsigned int toIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8cpp_source.xhtml#l00012">TestUtils.cpp:12</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="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186">armnn::ActivationFunction::HardSwish</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="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9ea"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9ea">armnn::ActivationFunction</a></div><div class="ttdeci">ActivationFunction</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00056">Types.hpp:56</a></div></div>
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