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+<a href="_addition_test_impl_8cpp.html">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> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_addition_test_impl_8hpp.html">AdditionTestImpl.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include "<a class="code" href="_elementwise_test_impl_8hpp.html">ElementwiseTestImpl.hpp</a>"</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> </div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_quantize_helper_8hpp.html">QuantizeHelper.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> </div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="keyword">template</span><></div><div class="line"><a name="l00013"></a><span class="lineno"><a class="line" href="_addition_test_impl_8cpp.html#a5f3caae0b1541a904067544dd37655f0"> 13</a></span> std::unique_ptr<armnn::IWorkload> CreateWorkload<armnn::AdditionQueueDescriptor>(</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a>& info,</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_addition_queue_descriptor.html">armnn::AdditionQueueDescriptor</a>& descriptor)</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="keywordflow">return</span> workloadFactory.CreateAddition(descriptor, info);</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> }</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> </div><div class="line"><a name="l00021"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#a7d30cae55fa22b1076269a211117fb43"> 21</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float,4></a> <a class="code" href="_addition_test_impl_8cpp.html#a108165b4957f3790332ae0afedf37ccd">AdditionTest</a>(</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> {</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 2u;</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channels = 2u;</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = 2u;</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = 3u;</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape[] = { batchSize, channels, height, width };</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>  std::vector<float> input1 =</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  {</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  0.0f, 2.0f, 1.0f,</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  0.2f, 1.0f, 2.0f,</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  1.0f, 2.0f, 1.0f,</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  0.2f, 1.0f, 2.0f,</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>  0.0f, 2.0f, 1.0f,</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  4.2f, 1.0f, 2.0f,</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>  0.0f, 0.0f, 1.0f,</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  0.2f, 1.0f, 2.0f,</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  };</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  std::vector<float> input2 =</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  {</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  1.0f, 2.0f, 1.0f,</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  0.0f, 1.0f, 2.0f,</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>  1.0f, 2.0f, -2.0f,</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  0.2f, 1.0f, 2.0f,</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>  0.0f, 2.0f, 1.0f,</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  4.2f, 0.0f, -3.0f,</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>  0.0f, 0.0f, 1.0f,</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  0.7f, 1.0f, 5.0f,</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  };</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> </div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  std::vector<float> output</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  {</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  1.0f, 4.0f, 2.0f,</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  0.2f, 2.0f, 4.0f,</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>  2.0f, 4.0f, -1.0f,</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  0.4f, 2.0f, 4.0f,</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>  0.0f, 4.0f, 2.0f,</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  8.4f, 1.0f, -1.0f,</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>  0.0f, 0.0f, 2.0f,</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  0.9f, 2.0f, 7.0f,</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="keywordflow">return</span> ElementwiseTestHelper<4, armnn::AdditionQueueDescriptor, armnn::DataType::Float32>(</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  workloadFactory,</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  memoryManager,</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  shape,</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  input1,</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  shape,</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  input2,</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  shape,</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  output);</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"><a class="line" href="_addition_test_impl_8hpp.html#ab102e5bc3a3b04360a0f42e25ab3c898"> 89</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 5></a> <a class="code" href="_addition_test_impl_8cpp.html#ab102e5bc3a3b04360a0f42e25ab3c898">Addition5dTest</a>(</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> {</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth = 2u;</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 2u;</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channels = 2u;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = 2u;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = 3u;</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> </div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape[] = { depth, batchSize, channels, height, width };</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> </div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  std::vector<float> input1 =</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  {</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  2.6f, 4.0f, 4.4f, 2.7f, 4.6f, 2.8f,</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  2.3f, 1.9f, 3.4f, 2.9f, 2.2f, 4.5f,</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>  2.8f, 1.9f, 2.3f, 2.6f, 4.7f, 3.5f,</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  0.4f, 1.5f, 2.1f, 0.7f, 5.0f, 1.1f,</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> </div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  1.0f, 2.7f, 0.0f, 0.6f, 0.8f, 0.9f,</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  1.0f, 2.6f, 0.4f, 3.8f, 0.4f, 0.8f,</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> </div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  0.5f, 4.3f, 3.1f, 4.4f, 0.7f, 1.4f,</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  0.4f, 4.4f, 0.7f, 0.6f, 4.7f, 1.2f,</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  };</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  std::vector<float> input2 =</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>  4.4f, 3.0f, 1.0f, 0.0f, 3.9f, 3.1f,</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  1.7f, 2.9f, 1.3f, 0.4f, 0.4f, 4.3f,</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> </div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  4.5f, 0.2f, 2.2f, 4.1f, 3.9f, 3.0f,</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  0.1f, 2.5f, 4.1f, 4.6f, 1.5f, 0.0f,</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> </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>  0.5f, 4.9f, 2.5f, 1.5f, 3.4f, 4.5f,</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  2.0f, 3.0f, 4.9f, 1.6f, 2.4f, 3.4f,</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>  3.6f, 1.8f, 1.3f, 2.6f, 2.1f, 4.8f,</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  2.0f, 4.3f, 4.0f, 0.2f, 0.6f, 4.4f,</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> </div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  std::vector<float> output =</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  {</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  7.0f, 7.0f, 5.4f, 2.7f, 8.5f, 5.9f,</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  4.0f, 4.8f, 4.7f, 3.3f, 2.6f, 8.8f,</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>  7.3f, 2.1f, 4.5f, 6.7f, 8.6f, 6.5f,</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  0.5f, 4.0f, 6.2f, 5.3f, 6.5f, 1.1f,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> </div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> </div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  1.5f, 7.6f, 2.5f, 2.1f, 4.2f, 5.4f,</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  3.0f, 5.6f, 5.3f, 5.4f, 2.8f, 4.2f,</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> </div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  4.1f, 6.1f, 4.4f, 7.0f, 2.8f, 6.2f,</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  2.4f, 8.7f, 4.7f, 0.8f, 5.3f, 5.6f,</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  };</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> </div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keywordflow">return</span> ElementwiseTestHelper<5, armnn::AdditionQueueDescriptor, armnn::DataType::Float32>(</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  workloadFactory,</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  memoryManager,</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  shape,</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  input1,</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  shape,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  input2,</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  shape,</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  output);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> }</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> </div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00162"></a><span class="lineno"><a class="line" href="_addition_test_impl_8cpp.html#add789f43d728a34fccf9aea235179342"> 162</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> <a class="code" href="_addition_test_impl_8cpp.html#add789f43d728a34fccf9aea235179342">AdditionBroadcastTestImpl</a>(</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  int32_t qOffset)</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> {</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  boost::ignore_unused(memoryManager);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo1 = <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>({1, 3, 2, 1}, ArmnnType);</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo2 = <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>({1, 1, 2, 3}, ArmnnType);</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>({1, 3, 2, 3}, ArmnnType);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> </div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keywordflow">if</span> (armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  {</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  inputTensorInfo1.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  inputTensorInfo1.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  inputTensorInfo2.SetQuantizationScale(qScale);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  inputTensorInfo2.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  outputTensorInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  outputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  }</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> </div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keyword">auto</span> input1 = MakeTensor<T, 4>(inputTensorInfo1, armnnUtils::QuantizedVector<T>(</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  {</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  0.0f,</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  1.0f,</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> </div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  2.0f,</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  3.0f,</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>  4.0f,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  5.0f,</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>  qScale, qOffset));</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">auto</span> input2 = MakeTensor<T, 4>(inputTensorInfo2, armnnUtils::QuantizedVector<T>(</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>  0.5f, 1.5f, 2.5f,</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  3.5f, 4.5f, 5.5f,</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  },</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  qScale, qOffset));</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> </div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <a class="code" href="struct_layer_test_result.html">LayerTestResult<T,4></a> ret(outputTensorInfo);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  ret.<a class="code" href="struct_layer_test_result.html#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 4>(outputTensorInfo, armnnUtils::QuantizedVector<T>(</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  {</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  0.5f, 1.5f, 2.5f,</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  4.5f, 5.5f, 6.5f,</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span> </div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  2.5f, 3.5f, 4.5f,</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  6.5f, 7.5f, 8.5f,</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> </div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  4.5f, 5.5f, 6.5f,</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  8.5f, 9.5f, 10.5f,</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  },</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  qScale, qOffset));</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span> </div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo1);</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo2);</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> </div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <a class="code" href="structarmnn_1_1_addition_queue_descriptor.html">armnn::AdditionQueueDescriptor</a> data;</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <a class="code" href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> </div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#acf187617ed4cdd6625b396d6b194923e">CreateAddition</a>(data, info);</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span> </div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  inputHandle1->Allocate();</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  inputHandle2->Allocate();</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  outputHandle->Allocate();</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>  <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle1.get(), &input1[0][0][0][0]);</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle2.get(), &input2[0][0][0][0]);</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>  workload->PostAllocationConfigure();</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  workload->Execute();</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>  <a class="code" href="_tensor_copy_utils_8cpp.html#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.<a class="code" href="struct_layer_test_result.html#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> </div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span> }</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> </div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00245"></a><span class="lineno"><a class="line" href="_addition_test_impl_8cpp.html#ad6a320dc43ad2384cf2d7288cf9c0823"> 245</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<T, 4></a> <a class="code" href="_addition_test_impl_8cpp.html#ad6a320dc43ad2384cf2d7288cf9c0823">AdditionBroadcast1ElementTestImpl</a>(</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  int32_t qOffset)</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> {</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  boost::ignore_unused(memoryManager);</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo1 = <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>({1, 3, 2, 3}, ArmnnType);</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo2 = <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>({1, 1, 1, 1}, ArmnnType);</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>({1, 3, 2, 3}, ArmnnType);</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> </div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keywordflow">if</span> (armnn::IsQuantizedType<T>())</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>  inputTensorInfo1.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  inputTensorInfo1.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  inputTensorInfo2.SetQuantizationScale(qScale);</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  inputTensorInfo2.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  outputTensorInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  outputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  }</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> </div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keyword">auto</span> input1 = MakeTensor<T, 4>(inputTensorInfo1, armnnUtils::QuantizedVector<T>(</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  {</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  0.0f, 1.0f, 2.0f,</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  3.0f, 4.0f, 5.0f,</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  6.0f, 7.0f, 8.0f,</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  9.0f, 10.0f, 11.0f,</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  12.0f, 13.0f, 14.0f,</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  15.0f, 16.0f, 17.0f,</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  },</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  qScale, qOffset));</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span> </div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keyword">auto</span> input2 = MakeTensor<T, 4>(inputTensorInfo2, armnnUtils::QuantizedVector<T>(</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  {</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  0.5f,</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>  qScale, qOffset));</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> </div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <a class="code" href="struct_layer_test_result.html">LayerTestResult<T,4></a> ret(outputTensorInfo);</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  ret.<a class="code" href="struct_layer_test_result.html#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 4>(outputTensorInfo, armnnUtils::QuantizedVector<T>(</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  {</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  0.5f, 1.5f, 2.5f,</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  3.5f, 4.5f, 5.5f,</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  6.5f, 7.5f, 8.5f,</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  9.5f, 10.5f, 11.5f,</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  12.5f, 13.5f, 14.5f,</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  15.5f, 16.5f, 17.5f,</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  },</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  qScale, qOffset));</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>  std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo1);</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo2);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span> </div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <a class="code" href="structarmnn_1_1_addition_queue_descriptor.html">armnn::AdditionQueueDescriptor</a> data;</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <a class="code" href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span> </div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#acf187617ed4cdd6625b396d6b194923e">CreateAddition</a>(data, info);</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span> </div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  inputHandle1->Allocate();</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  inputHandle2->Allocate();</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  outputHandle->Allocate();</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>  <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle1.get(), &input1[0][0][0][0]);</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle2.get(), &input2[0][0][0][0]);</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span> </div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  workload->PostAllocationConfigure();</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  workload->Execute();</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>  <a class="code" href="_tensor_copy_utils_8cpp.html#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.<a class="code" href="struct_layer_test_result.html#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span> </div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> }</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span> </div><div class="line"><a name="l00322"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#a9591268a5a6c7d0a0b91098deab4fe34"> 322</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_addition_test_impl_8cpp.html#a9591268a5a6c7d0a0b91098deab4fe34">AdditionBroadcastTest</a>(</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</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>  <span class="keywordflow">return</span> AdditionBroadcastTestImpl<armnn::DataType::Float32>(</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  workloadFactory, memoryManager, 0.0f, 0);</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"><a class="line" href="_addition_test_impl_8hpp.html#a0946a9b1b8cf99591b03ea7f5f7e725f"> 330</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_addition_test_impl_8cpp.html#a0946a9b1b8cf99591b03ea7f5f7e725f">AdditionBroadcastUint8Test</a>(</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</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="keywordflow">return</span> AdditionBroadcastTestImpl<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  workloadFactory, memoryManager, 2.f, 0);</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span> }</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> </div><div class="line"><a name="l00338"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#a470ed90260ed36c02adc91df184fcc82"> 338</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_addition_test_impl_8cpp.html#a470ed90260ed36c02adc91df184fcc82">AdditionBroadcastInt16Test</a>(</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span> {</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keywordflow">return</span> AdditionBroadcastTestImpl<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  workloadFactory, memoryManager, 2.f, 0);</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span> }</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> </div><div class="line"><a name="l00346"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#a4bff97bff3f9fb4cf473812dee810de0"> 346</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_addition_test_impl_8cpp.html#a4bff97bff3f9fb4cf473812dee810de0">AdditionBroadcast1ElementTest</a>(</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span> {</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keywordflow">return</span> AdditionBroadcast1ElementTestImpl<armnn::DataType::Float32>(</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span> }</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span> </div><div class="line"><a name="l00354"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#ad71ffd0e8547900b92a5d471f01cd69b"> 354</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_addition_test_impl_8cpp.html#ad71ffd0e8547900b92a5d471f01cd69b">AdditionBroadcast1ElementUint8Test</a>(</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> {</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="keywordflow">return</span> AdditionBroadcast1ElementTestImpl<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  workloadFactory, memoryManager, 0.1333333f, 128);</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span> }</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span> </div><div class="line"><a name="l00362"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#a97579bb78890452730fff4d1e3e6fb4a"> 362</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_addition_test_impl_8cpp.html#a97579bb78890452730fff4d1e3e6fb4a">AdditionBroadcast1ElementInt16Test</a>(</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</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="keywordflow">return</span> AdditionBroadcast1ElementTestImpl<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  workloadFactory, memoryManager, 0.1333333f, 0);</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> </div><div class="line"><a name="l00370"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#a4b5e20456506426ba2e4ea9616df978f"> 370</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<uint8_t, 4></a> <a class="code" href="_addition_test_impl_8cpp.html#a4b5e20456506426ba2e4ea9616df978f">AdditionUint8Test</a>(</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span> {</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape0[] = { 1, 2, 2, 3 };</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape1[] = { 1, 2, 2, 3 };</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span> </div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  std::vector<uint8_t> input0(</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>  63, 35, 77, 70, 56, 112, <span class="comment">// 420, 224, 518, 469, 371, 763</span></div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  203, 28, 252, 168, 245, 91 <span class="comment">// 1400, 175, 1743, 1155, 1694, 616</span></div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  });</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>  std::vector<uint8_t> input1(</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>  21, 7, 175, 231, 175, 210, <span class="comment">// 126, 28, 1204, 1596, 1204, 1449</span></div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  126, 161, 63, 21, 105, 126 <span class="comment">// 861, 1106, 420, 126, 714, 861</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> </div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  std::vector<uint8_t> output(</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  {</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  81, 39, 249, 255, 228, 255, <span class="comment">// 546, 252, 1722, 2065(clamped), 1575, 2212(clamped)</span></div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  255, 186, 255, 186, 255, 214, <span class="comment">// 2261(clamped), 1281, 2163(clamped), 1281, 2408(clamped), 1477</span></div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  });</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span> </div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="keywordflow">return</span> ElementwiseTestHelper<4, armnn::AdditionQueueDescriptor, armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  workloadFactory,</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  memoryManager,</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  shape0,</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  input0,</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  7.0f,</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  3,</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  shape1,</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  input1,</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  7.0f,</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  3,</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  shape0,</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  output,</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  7.0f,</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  3);</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> </div><div class="line"><a name="l00412"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#ae087613cdb8319fbab07d44e6eaf217d"> 412</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<int16_t, 4></a> <a class="code" href="_addition_test_impl_8cpp.html#ae087613cdb8319fbab07d44e6eaf217d">AdditionInt16Test</a>(</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span> {</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape0[] = { 1, 2, 2, 3 };</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape1[] = { 1, 2, 2, 3 };</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>  std::vector<int16_t> input0 =</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  {</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  63, 35, 77, 70, 56, 112, <span class="comment">// 441, 245, 539, 490, 392, 184</span></div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  203, 28, 252, 168, 245, 91 <span class="comment">// 1421, 196, 1764, 1176, 1715, 637</span></div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  };</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>  std::vector<int16_t> input1 =</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>  21, 7, 175, 231, 175, 210, <span class="comment">// 126, 28, 1204, 1596, 1204, 1449</span></div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  126, 161, 63, 21, 105, 126 <span class="comment">// 861, 1106, 420, 126, 714, 861</span></div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  };</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>  std::vector<int16_t> output =</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>  84, 42, 252, 301, 231, 322, <span class="comment">// 588, 294, 1764, 2107(clamped), 1617, 2254(clamped)</span></div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  329, 189, 315, 189, 350, 217, <span class="comment">// 2303(clamped), 1323, 2205(clamped), 1323, 2450(clamped), 1519</span></div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  };</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span> </div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <span class="keywordflow">return</span> ElementwiseTestHelper<4, armnn::AdditionQueueDescriptor, armnn::DataType::QSymmS16>(</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  workloadFactory,</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  memoryManager,</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  shape0,</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  input0,</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  7.0f,</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  0,</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  shape1,</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  input1,</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  7.0f,</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  0,</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  shape0,</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  output,</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  7.0f,</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  0);</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span> }</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span> </div><div class="line"><a name="l00454"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#aa11fe3b8a07854e2bb9dd3ccecaa96e4"> 454</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float, 4></a> <a class="code" href="_addition_test_impl_8cpp.html#aa11fe3b8a07854e2bb9dd3ccecaa96e4">AdditionAfterMaxPoolTest</a>(</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> {</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  boost::ignore_unused(memoryManager);</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span> </div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <span class="comment">// Create Initial Tensor</span></div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <span class="comment">// 1, 2, 3</span></div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="comment">// 4, 5, 6</span></div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="comment">// 7, 8, 9</span></div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span> </div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> poolingInputTensorInfo({ 1, 1, 3, 3}, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> poolingOutputTensorInfo({ 1, 1, 2, 2}, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span> </div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  boost::multi_array<float, 4> poolingInput = MakeTensor<float,4>(poolingInputTensorInfo,</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  {1, 2, 3,</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  4, 5, 6,</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  7, 8, 9</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>  std::unique_ptr<armnn::ITensorHandle> poolingInputHandle =</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(poolingInputTensorInfo);</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  std::unique_ptr<armnn::ITensorHandle> poolingOutputHandle =</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(poolingOutputTensorInfo);</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span> </div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="comment">// Apply MaxPool poolSize = 1x1, stride=2x2</span></div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <span class="comment">// Result =</span></div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  <span class="comment">// 1, 3</span></div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <span class="comment">// 7, 9</span></div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a> descriptor;</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 1;</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = 1;</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.html#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">armnn::PoolingAlgorithm::Max</a>;</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span> </div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  <a class="code" href="structarmnn_1_1_pooling2d_queue_descriptor.html">armnn::Pooling2dQueueDescriptor</a> queueDescriptor;</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  queueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a> = descriptor;</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  <a class="code" href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  AddInputToWorkload(queueDescriptor, workloadInfo, poolingInputTensorInfo, poolingInputHandle.get());</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  AddOutputToWorkload(queueDescriptor, workloadInfo, poolingOutputTensorInfo, poolingOutputHandle.get());</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span> </div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <span class="comment">// Create the MaxPool</span></div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a6e95afd9a55700cbf6f9e8db8089f2f2">CreatePooling2d</a>(queueDescriptor, workloadInfo);</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">//LayerTestResult<float, 4> result(poolingOutputTensorInfo);</span></div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <span class="keyword">auto</span> shape( GetTensorShapeAsArray<4>(poolingOutputTensorInfo));</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  boost::multi_array<float, 4> resultMaxPool;</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  resultMaxPool.resize(shape);</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span> </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="comment">// Create addition with another tensor the same size</span></div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  <span class="comment">// This would be the result to apply a Conv2d with kernel ones(2) and stride 1x1</span></div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <span class="comment">// with the initial tensor.</span></div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <span class="comment">// 12, 16</span></div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <span class="comment">// 24, 28</span></div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span> </div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> addInputTensorInfo({ 1,1,2,2}, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> addOutputTensorInfo({ 1,1,2,2}, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span> </div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  boost::multi_array<float, 4> addInput = MakeTensor<float,4>(addInputTensorInfo,</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  {12, 16,</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  24, 28,</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  });</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> </div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  <span class="comment">// Expected output tensor after MaxPool and Addition.</span></div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  <a class="code" href="struct_layer_test_result.html">LayerTestResult<float,4></a> addRet(addOutputTensorInfo);</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  addRet.outputExpected = MakeTensor<float, 4>(addOutputTensorInfo, std::vector<float>(</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  {</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  13, 19,</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  31, 37</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  }));</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>  std::unique_ptr<armnn::ITensorHandle> addInputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(addInputTensorInfo);</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  std::unique_ptr<armnn::ITensorHandle> addOutputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(addOutputTensorInfo);</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>  <a class="code" href="structarmnn_1_1_addition_queue_descriptor.html">armnn::AdditionQueueDescriptor</a> data;</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <a class="code" href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span> </div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="comment">// Add the output of the MaxPool and the new tensor</span></div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  AddInputToWorkload(data, info, poolingOutputTensorInfo, poolingOutputHandle.get());</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  AddInputToWorkload(data, info, addInputTensorInfo, addInputHandle.get());</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  AddOutputToWorkload(data, info, addOutputTensorInfo, addOutputHandle.get());</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>  std::unique_ptr<armnn::IWorkload> addWorkload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#acf187617ed4cdd6625b396d6b194923e">CreateAddition</a>(data, info);</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span> </div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  poolingInputHandle->Allocate();</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  poolingOutputHandle->Allocate();</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  addInputHandle->Allocate();</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  addOutputHandle->Allocate();</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>  <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(poolingInputHandle.get(), &poolingInput[0][0][0][0]);</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&resultMaxPool[0][0][0][0], poolingOutputHandle.get());</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span> </div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(poolingOutputHandle.get(), &resultMaxPool[0][0][0][0]);</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(addInputHandle.get(), &addInput[0][0][0][0]);</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>  workload->PostAllocationConfigure();</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  workload->Execute();</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  addWorkload->PostAllocationConfigure();</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  addWorkload->Execute();</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span> </div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&addRet.output[0][0][0][0], addOutputHandle.get());</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span> </div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <span class="keywordflow">return</span> addRet;</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span> }</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span> </div><div class="line"><a name="l00561"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#a50074d57c9208290be87347941e716d7"> 561</a></span> <a class="code" href="struct_layer_test_result.html">LayerTestResult<float,4></a> <a class="code" href="_addition_test_impl_8cpp.html#a557c464592942eb098f63aa0f91e4d24">CompareAdditionTest</a>(</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>& refWorkloadFactory)</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::ignore_unused(memoryManager);</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 4;</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channels = 1;</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = 2;</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = 3;</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span> </div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> inputTensorInfo1, inputTensorInfo2;</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span> </div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape[] = {batchSize, channels, height, width};</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>  inputTensorInfo1 = <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>(4, shape, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  inputTensorInfo2 = <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>(4, shape, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>(4, shape, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span> </div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  <span class="keyword">auto</span> input1 = MakeRandomTensor<float, 4>(inputTensorInfo1, 1232);</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <span class="keyword">auto</span> input2 = MakeRandomTensor<float, 4>(inputTensorInfo2, 456);</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span> </div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  <a class="code" href="struct_layer_test_result.html">LayerTestResult<float,4></a> ret(outputTensorInfo);</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span> </div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo1);</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo2);</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</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>  std::unique_ptr<armnn::ITensorHandle> inputHandle1Ref = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo1);</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle2Ref = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo2);</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span> </div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <a class="code" href="structarmnn_1_1_addition_queue_descriptor.html">armnn::AdditionQueueDescriptor</a> data;</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <a class="code" href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span> </div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  <a class="code" href="structarmnn_1_1_addition_queue_descriptor.html">armnn::AdditionQueueDescriptor</a> refData = data;</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <a class="code" href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a> refInfo = info;</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  SetWorkloadInput(refData, refInfo, 0, inputTensorInfo1, inputHandle1Ref.get());</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  SetWorkloadInput(refData, refInfo, 1, inputTensorInfo2, inputHandle2Ref.get());</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span> </div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#acf187617ed4cdd6625b396d6b194923e">CreateAddition</a>(data, info);</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.html#acf187617ed4cdd6625b396d6b194923e">CreateAddition</a>(refData, refInfo);</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>  inputHandle1->Allocate();</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  inputHandle2->Allocate();</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  outputHandle->Allocate();</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  inputHandle1Ref->Allocate();</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  inputHandle2Ref->Allocate();</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  outputHandleRef->Allocate();</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span> </div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle1.get(), &input1[0][0][0][0]);</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle2.get(), &input2[0][0][0][0]);</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle1Ref.get(), &input1[0][0][0][0]);</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle2Ref.get(), &input2[0][0][0][0]);</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span> </div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  workload->PostAllocationConfigure();</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  workload->Execute();</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  workloadRef->PostAllocationConfigure();</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  workloadRef->Execute();</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span> </div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  <a class="code" href="_tensor_copy_utils_8cpp.html#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.outputExpected[0][0][0][0], outputHandleRef.get());</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>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span> }</div><div class="ttc" id="namespacearmnn_html_ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233"><div class="ttname"><a href="namespacearmnn.html#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">armnn::ArgMinMaxFunction::Max</a></div></div>
+<div class="ttc" id="_addition_test_impl_8cpp_html_ad6a320dc43ad2384cf2d7288cf9c0823"><div class="ttname"><a href="_addition_test_impl_8cpp.html#ad6a320dc43ad2384cf2d7288cf9c0823">AdditionBroadcast1ElementTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > AdditionBroadcast1ElementTestImpl(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_addition_test_impl_8cpp_source.html#l00245">AdditionTestImpl.cpp:245</a></div></div>
+<div class="ttc" id="_addition_test_impl_8cpp_html_ae087613cdb8319fbab07d44e6eaf217d"><div class="ttname"><a href="_addition_test_impl_8cpp.html#ae087613cdb8319fbab07d44e6eaf217d">AdditionInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > AdditionInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_addition_test_impl_8cpp_source.html#l00412">AdditionTestImpl.cpp:412</a></div></div>
+<div class="ttc" id="_addition_test_impl_8cpp_html_a108165b4957f3790332ae0afedf37ccd"><div class="ttname"><a href="_addition_test_impl_8cpp.html#a108165b4957f3790332ae0afedf37ccd">AdditionTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > AdditionTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_addition_test_impl_8cpp_source.html#l00021">AdditionTestImpl.cpp:21</a></div></div>
+<div class="ttc" id="classarmnn_1_1_i_workload_factory_html"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8hpp_source.html#l00021">WorkloadFactory.hpp:21</a></div></div>
+<div class="ttc" id="_tensor_copy_utils_8cpp_html_ae15f1a3c55d2db87683577de9fa4437c"><div class="ttname"><a href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a></div><div class="ttdeci">void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.html#l00009">TensorCopyUtils.cpp:9</a></div></div>
+<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_html_a5699e8606c37d18c03910b242cd1b010"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#a5699e8606c37d18c03910b242cd1b010">armnn::Pooling2dDescriptor::m_PoolHeight</a></div><div class="ttdeci">uint32_t m_PoolHeight</div><div class="ttdoc">Pooling height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00359">Descriptors.hpp:359</a></div></div>
+<div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_html_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.html#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00049">WorkloadData.hpp:49</a></div></div>
+<div class="ttc" id="classarmnn_1_1_tensor_info_html"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00053">Tensor.hpp:53</a></div></div>
+<div class="ttc" id="_addition_test_impl_8cpp_html_ab102e5bc3a3b04360a0f42e25ab3c898"><div class="ttname"><a href="_addition_test_impl_8cpp.html#ab102e5bc3a3b04360a0f42e25ab3c898">Addition5dTest</a></div><div class="ttdeci">LayerTestResult< float, 5 > Addition5dTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_addition_test_impl_8cpp_source.html#l00089">AdditionTestImpl.cpp:89</a></div></div>
+<div class="ttc" id="classarmnn_1_1_i_workload_factory_html_a15c140be4ddceffee16436f009d3ed94"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.html#a15c140be4ddceffee16436f009d3ed94">armnn::IWorkloadFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo, const bool IsMemoryManaged=true) const =0</div></div>
+<div class="ttc" id="struct_layer_test_result_html"><div class="ttname"><a href="struct_layer_test_result.html">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.html#l00029">LayerTestResult.hpp:29</a></div></div>
+<div class="ttc" id="_tensor_copy_utils_8cpp_html_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.html#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.html#l00014">TensorCopyUtils.cpp:14</a></div></div>
+<div class="ttc" id="_addition_test_impl_8hpp_html"><div class="ttname"><a href="_addition_test_impl_8hpp.html">AdditionTestImpl.hpp</a></div></div>
+<div class="ttc" id="_elementwise_test_impl_8hpp_html"><div class="ttname"><a href="_elementwise_test_impl_8hpp.html">ElementwiseTestImpl.hpp</a></div></div>
+<div class="ttc" id="classarmnn_1_1_i_backend_internal_html_a693b40e6b94e958836aeb0410ca186bd"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a></div><div class="ttdeci">std::shared_ptr< IMemoryManager > IMemoryManagerSharedPtr</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.html#l00090">IBackendInternal.hpp:90</a></div></div>
+<div class="ttc" id="struct_layer_test_result_html_ac9d44d346bb7c89f7a7aa31d2bee947f"><div class="ttname"><a href="struct_layer_test_result.html#ac9d44d346bb7c89f7a7aa31d2bee947f">LayerTestResult::output</a></div><div class="ttdeci">boost::multi_array< T, n > output</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.html#l00040">LayerTestResult.hpp:40</a></div></div>
+<div class="ttc" id="_addition_test_impl_8cpp_html_a470ed90260ed36c02adc91df184fcc82"><div class="ttname"><a href="_addition_test_impl_8cpp.html#a470ed90260ed36c02adc91df184fcc82">AdditionBroadcastInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > AdditionBroadcastInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_addition_test_impl_8cpp_source.html#l00338">AdditionTestImpl.cpp:338</a></div></div>
+<div class="ttc" id="structarmnn_1_1_workload_info_html"><div class="ttname"><a href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.html#l00016">WorkloadInfo.hpp:16</a></div></div>
+<div class="ttc" id="_addition_test_impl_8cpp_html_a557c464592942eb098f63aa0f91e4d24"><div class="ttname"><a href="_addition_test_impl_8cpp.html#a557c464592942eb098f63aa0f91e4d24">CompareAdditionTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > CompareAdditionTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::IWorkloadFactory &refWorkloadFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_addition_test_impl_8cpp_source.html#l00561">AdditionTestImpl.cpp:561</a></div></div>
+<div class="ttc" id="namespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
+<div class="ttc" id="_addition_test_impl_8cpp_html_a9591268a5a6c7d0a0b91098deab4fe34"><div class="ttname"><a href="_addition_test_impl_8cpp.html#a9591268a5a6c7d0a0b91098deab4fe34">AdditionBroadcastTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > AdditionBroadcastTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_addition_test_impl_8cpp_source.html#l00322">AdditionTestImpl.cpp:322</a></div></div>
+<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_html_a6d8fb685cc1ff224f25aa127fcf62c86"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#a6d8fb685cc1ff224f25aa127fcf62c86">armnn::Pooling2dDescriptor::m_PoolWidth</a></div><div class="ttdeci">uint32_t m_PoolWidth</div><div class="ttdoc">Pooling width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00357">Descriptors.hpp:357</a></div></div>
+<div class="ttc" id="_addition_test_impl_8cpp_html_a0946a9b1b8cf99591b03ea7f5f7e725f"><div class="ttname"><a href="_addition_test_impl_8cpp.html#a0946a9b1b8cf99591b03ea7f5f7e725f">AdditionBroadcastUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > AdditionBroadcastUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_addition_test_impl_8cpp_source.html#l00330">AdditionTestImpl.cpp:330</a></div></div>
+<div class="ttc" id="_addition_test_impl_8cpp_html_a4b5e20456506426ba2e4ea9616df978f"><div class="ttname"><a href="_addition_test_impl_8cpp.html#a4b5e20456506426ba2e4ea9616df978f">AdditionUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > AdditionUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_addition_test_impl_8cpp_source.html#l00370">AdditionTestImpl.cpp:370</a></div></div>
+<div class="ttc" id="_addition_test_impl_8cpp_html_a4bff97bff3f9fb4cf473812dee810de0"><div class="ttname"><a href="_addition_test_impl_8cpp.html#a4bff97bff3f9fb4cf473812dee810de0">AdditionBroadcast1ElementTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > AdditionBroadcast1ElementTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_addition_test_impl_8cpp_source.html#l00346">AdditionTestImpl.cpp:346</a></div></div>
+<div class="ttc" id="classarmnn_1_1_tensor_info_html_a685739c4eb65a580e075282cfe6787d6"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#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.html#l00259">Tensor.cpp:259</a></div></div>
+<div class="ttc" id="_addition_test_impl_8cpp_html_aa11fe3b8a07854e2bb9dd3ccecaa96e4"><div class="ttname"><a href="_addition_test_impl_8cpp.html#aa11fe3b8a07854e2bb9dd3ccecaa96e4">AdditionAfterMaxPoolTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > AdditionAfterMaxPoolTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_addition_test_impl_8cpp_source.html#l00454">AdditionTestImpl.cpp:454</a></div></div>
+<div class="ttc" id="struct_layer_test_result_html_a73610ea6c776cc66e5a78dd842a39b8b"><div class="ttname"><a href="struct_layer_test_result.html#a73610ea6c776cc66e5a78dd842a39b8b">LayerTestResult::outputExpected</a></div><div class="ttdeci">boost::multi_array< T, n > outputExpected</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.html#l00041">LayerTestResult.hpp:41</a></div></div>
+<div class="ttc" id="_quantize_helper_8hpp_html"><div class="ttname"><a href="_quantize_helper_8hpp.html">QuantizeHelper.hpp</a></div></div>
+<div class="ttc" id="classarmnn_1_1_i_workload_factory_html_a6e95afd9a55700cbf6f9e8db8089f2f2"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.html#a6e95afd9a55700cbf6f9e8db8089f2f2">armnn::IWorkloadFactory::CreatePooling2d</a></div><div class="ttdeci">virtual std::unique_ptr< IWorkload > CreatePooling2d(const Pooling2dQueueDescriptor &descriptor, const WorkloadInfo &info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.html#l01323">WorkloadFactory.cpp:1323</a></div></div>
+<div class="ttc" id="classarmnn_1_1_i_workload_factory_html_acf187617ed4cdd6625b396d6b194923e"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.html#acf187617ed4cdd6625b396d6b194923e">armnn::IWorkloadFactory::CreateAddition</a></div><div class="ttdeci">virtual std::unique_ptr< IWorkload > CreateAddition(const AdditionQueueDescriptor &descriptor, const WorkloadInfo &info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.html#l01088">WorkloadFactory.cpp:1088</a></div></div>
+<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_html_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Pooling2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00363">Descriptors.hpp:363</a></div></div>
+<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_html_a0031997bf43bd2747656c31e4977793a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#a0031997bf43bd2747656c31e4977793a">armnn::Pooling2dDescriptor::m_PoolType</a></div><div class="ttdeci">PoolingAlgorithm m_PoolType</div><div class="ttdoc">The pooling algorithm to use (Max. Average, L2). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00347">Descriptors.hpp:347</a></div></div>
+<div class="ttc" id="structarmnn_1_1_addition_queue_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_addition_queue_descriptor.html">armnn::AdditionQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00216">WorkloadData.hpp:216</a></div></div>
+<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a></div><div class="ttdoc">A Pooling2dDescriptor for the Pooling2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00313">Descriptors.hpp:313</a></div></div>
+<div class="ttc" id="_addition_test_impl_8cpp_html_a97579bb78890452730fff4d1e3e6fb4a"><div class="ttname"><a href="_addition_test_impl_8cpp.html#a97579bb78890452730fff4d1e3e6fb4a">AdditionBroadcast1ElementInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > AdditionBroadcast1ElementInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_addition_test_impl_8cpp_source.html#l00362">AdditionTestImpl.cpp:362</a></div></div>
+<div class="ttc" id="_addition_test_impl_8cpp_html_add789f43d728a34fccf9aea235179342"><div class="ttname"><a href="_addition_test_impl_8cpp.html#add789f43d728a34fccf9aea235179342">AdditionBroadcastTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > AdditionBroadcastTestImpl(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_addition_test_impl_8cpp_source.html#l00162">AdditionTestImpl.cpp:162</a></div></div>
+<div class="ttc" id="classarmnn_1_1_tensor_info_html_a63cbc581012c957f9d68d224ddc3e43c"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#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.html#l00275">Tensor.cpp:275</a></div></div>
+<div class="ttc" id="structarmnn_1_1_pooling2d_queue_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_pooling2d_queue_descriptor.html">armnn::Pooling2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.html#l00162">WorkloadData.hpp:162</a></div></div>
+<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_html_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">armnn::Pooling2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00361">Descriptors.hpp:361</a></div></div>
+<div class="ttc" id="_addition_test_impl_8cpp_html_ad71ffd0e8547900b92a5d471f01cd69b"><div class="ttname"><a href="_addition_test_impl_8cpp.html#ad71ffd0e8547900b92a5d471f01cd69b">AdditionBroadcast1ElementUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > AdditionBroadcast1ElementUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_addition_test_impl_8cpp_source.html#l00354">AdditionTestImpl.cpp:354</a></div></div>
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