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<div class="title">AdditionTestImpl.cpp</div> </div>
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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>&#160;<span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;</div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_addition_test_impl_8hpp.html">AdditionTestImpl.hpp</a>&quot;</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;</div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_elementwise_test_impl_8hpp.html">ElementwiseTestImpl.hpp</a>&quot;</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_quantize_helper_8hpp.html">QuantizeHelper.hpp</a>&gt;</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;</div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;<span class="keyword">template</span>&lt;&gt;</div><div class="line"><a name="l00013"></a><span class="lineno"><a class="line" href="_addition_test_impl_8cpp.html#a5f3caae0b1541a904067544dd37655f0"> 13</a></span>&#160;std::unique_ptr&lt;armnn::IWorkload&gt; CreateWorkload&lt;armnn::AdditionQueueDescriptor&gt;(</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160; <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.html">armnn::WorkloadInfo</a>&amp; info,</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160; <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_addition_queue_descriptor.html">armnn::AdditionQueueDescriptor</a>&amp; descriptor)</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;{</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160; <span class="keywordflow">return</span> workloadFactory.CreateAddition(descriptor, info);</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;}</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;</div><div class="line"><a name="l00021"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#a7d30cae55fa22b1076269a211117fb43"> 21</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;float,4&gt;</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>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;{</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160; <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>&#160; <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>&#160; <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>&#160; <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>&#160;</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160; <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>&#160;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160; std::vector&lt;float&gt; input1 =</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160; {</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160; 0.0f, 2.0f, 1.0f,</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160; 0.2f, 1.0f, 2.0f,</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160; 1.0f, 2.0f, 1.0f,</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160; 0.2f, 1.0f, 2.0f,</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160;</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; 0.0f, 2.0f, 1.0f,</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; 4.2f, 1.0f, 2.0f,</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; 0.0f, 0.0f, 1.0f,</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; 0.2f, 1.0f, 2.0f,</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; };</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160;</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; std::vector&lt;float&gt; input2 =</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; {</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; 1.0f, 2.0f, 1.0f,</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; 0.0f, 1.0f, 2.0f,</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160;</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; 1.0f, 2.0f, -2.0f,</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; 0.2f, 1.0f, 2.0f,</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160;</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; 0.0f, 2.0f, 1.0f,</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; 4.2f, 0.0f, -3.0f,</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160;</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; 0.0f, 0.0f, 1.0f,</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; 0.7f, 1.0f, 5.0f,</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; };</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160;</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; std::vector&lt;float&gt; output</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; {</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; 1.0f, 4.0f, 2.0f,</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; 0.2f, 2.0f, 4.0f,</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160;</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; 2.0f, 4.0f, -1.0f,</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; 0.4f, 2.0f, 4.0f,</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; 0.0f, 4.0f, 2.0f,</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; 8.4f, 1.0f, -1.0f,</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; 0.0f, 0.0f, 2.0f,</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; 0.9f, 2.0f, 7.0f,</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; };</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; <span class="keywordflow">return</span> ElementwiseTestHelper&lt;4, armnn::AdditionQueueDescriptor, armnn::DataType::Float32&gt;(</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; workloadFactory,</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; memoryManager,</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; shape,</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; input1,</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; shape,</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; input2,</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; shape,</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; output);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160;}</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160;</div><div class="line"><a name="l00089"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#ab102e5bc3a3b04360a0f42e25ab3c898"> 89</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;float, 5&gt;</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>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160;{</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; <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>&#160; <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>&#160; <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>&#160; <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>&#160; <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>&#160;</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; <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>&#160;</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; std::vector&lt;float&gt; input1 =</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; {</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; 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>&#160; 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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>&#160;</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; 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>&#160; 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>&#160; };</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160;</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; <span class="keywordflow">return</span> ElementwiseTestHelper&lt;5, armnn::AdditionQueueDescriptor, armnn::DataType::Float32&gt;(</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; workloadFactory,</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; memoryManager,</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; shape,</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; input1,</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; shape,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; input2,</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; shape,</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; output);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160;}</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160;</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160;<span class="keyword">template</span>&lt;armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType&lt;ArmnnType&gt;&gt;</div><div class="line"><a name="l00162"></a><span class="lineno"><a class="line" href="_addition_test_impl_8cpp.html#add789f43d728a34fccf9aea235179342"> 162</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;T, 4&gt;</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>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; int32_t qOffset)</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160;{</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; boost::ignore_unused(memoryManager);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160; <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>&#160; <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>&#160; <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>&#160;</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; <span class="keywordflow">if</span> (armnn::IsQuantizedType&lt;T&gt;())</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; {</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; 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>&#160; 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>&#160; inputTensorInfo2.SetQuantizationScale(qScale);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; inputTensorInfo2.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; outputTensorInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; outputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; }</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160;</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; <span class="keyword">auto</span> input1 = MakeTensor&lt;T, 4&gt;(inputTensorInfo1, armnnUtils::QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; {</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; 0.0f,</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; 1.0f,</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160;</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; 2.0f,</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; 3.0f,</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160;</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160; 4.0f,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160; 5.0f,</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160; },</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160;</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; <span class="keyword">auto</span> input2 = MakeTensor&lt;T, 4&gt;(inputTensorInfo2, armnnUtils::QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; {</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; 0.5f, 1.5f, 2.5f,</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; 3.5f, 4.5f, 5.5f,</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; },</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160;</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160; <a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;T,4&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160; ret.<a class="code" href="struct_layer_test_result.html#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor&lt;T, 4&gt;(outputTensorInfo, armnnUtils::QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160; {</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160; 0.5f, 1.5f, 2.5f,</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>&#160; 4.5f, 5.5f, 6.5f,</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; 2.5f, 3.5f, 4.5f,</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160; 6.5f, 7.5f, 8.5f,</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160;</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; 4.5f, 5.5f, 6.5f,</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160; 8.5f, 9.5f, 10.5f,</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>&#160; },</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160;</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160; 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{</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160; 0.0f, 1.0f, 2.0f,</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160; 3.0f, 4.0f, 5.0f,</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>&#160; 6.0f, 7.0f, 8.0f,</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>&#160; 9.0f, 10.0f, 11.0f,</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>&#160; 12.0f, 13.0f, 14.0f,</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>&#160; 15.0f, 16.0f, 17.0f,</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>&#160; },</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>&#160;</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>&#160; <span class="keyword">auto</span> input2 = MakeTensor&lt;T, 4&gt;(inputTensorInfo2, armnnUtils::QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>&#160; {</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>&#160; 0.5f,</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>&#160; },</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>&#160;</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>&#160; <a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;T,4&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>&#160; ret.<a class="code" href="struct_layer_test_result.html#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor&lt;T, 4&gt;(outputTensorInfo, armnnUtils::QuantizedVector&lt;T&gt;(</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>&#160; {</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>&#160; 0.5f, 1.5f, 2.5f,</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>&#160; 3.5f, 4.5f, 5.5f,</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>&#160; 6.5f, 7.5f, 8.5f,</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>&#160; 9.5f, 10.5f, 11.5f,</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>&#160; 12.5f, 13.5f, 14.5f,</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>&#160; 15.5f, 16.5f, 17.5f,</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>&#160; },</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>&#160; qScale, qOffset));</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>&#160;</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; 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>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; 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>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; 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>&#160;</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>&#160; <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>&#160; <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>&#160; 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inputHandle2-&gt;Allocate();</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>&#160; outputHandle-&gt;Allocate();</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>&#160;</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle1.get(), &amp;input1[0][0][0][0]);</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle2.get(), &amp;input2[0][0][0][0]);</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>&#160;</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>&#160; workload-&gt;PostAllocationConfigure();</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>&#160; workload-&gt;Execute();</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>&#160;</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>&#160; 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<a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>&#160;{</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>&#160; <span class="keywordflow">return</span> AdditionBroadcastTestImpl&lt;armnn::DataType::Float32&gt;(</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>&#160; workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>&#160;}</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>&#160;</div><div class="line"><a name="l00330"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#a0946a9b1b8cf99591b03ea7f5f7e725f"> 330</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;uint8_t, 4&gt;</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>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>&#160;{</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>&#160; <span class="keywordflow">return</span> AdditionBroadcastTestImpl&lt;armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>&#160; workloadFactory, memoryManager, 2.f, 0);</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>&#160;}</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>&#160;</div><div class="line"><a name="l00338"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#a470ed90260ed36c02adc91df184fcc82"> 338</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;int16_t, 4&gt;</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>&#160; 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<a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>&#160;{</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>&#160; <span class="keywordflow">return</span> AdditionBroadcast1ElementTestImpl&lt;armnn::DataType::Float32&gt;(</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>&#160; workloadFactory, memoryManager, 0.0f, 0);</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>&#160;}</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>&#160;</div><div class="line"><a name="l00354"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#ad71ffd0e8547900b92a5d471f01cd69b"> 354</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;uint8_t, 4&gt;</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>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>&#160;{</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>&#160; <span class="keywordflow">return</span> AdditionBroadcast1ElementTestImpl&lt;armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>&#160; workloadFactory, memoryManager, 0.1333333f, 128);</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>&#160;}</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>&#160;</div><div class="line"><a name="l00362"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#a97579bb78890452730fff4d1e3e6fb4a"> 362</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;int16_t, 4&gt;</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>&#160; 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std::vector&lt;uint8_t&gt; input0(</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>&#160; {</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>&#160; 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>&#160; 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>&#160; });</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>&#160;</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>&#160; std::vector&lt;uint8_t&gt; input1(</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>&#160; {</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>&#160; 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>&#160; 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>&#160; });</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>&#160;</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>&#160; std::vector&lt;uint8_t&gt; output(</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>&#160; {</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>&#160; 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>&#160; 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>&#160; });</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>&#160;</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>&#160; <span class="keywordflow">return</span> ElementwiseTestHelper&lt;4, armnn::AdditionQueueDescriptor, armnn::DataType::QAsymmU8&gt;(</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>&#160; workloadFactory,</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>&#160; memoryManager,</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>&#160; shape0,</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>&#160; input0,</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>&#160; 7.0f,</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>&#160; 3,</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>&#160; shape1,</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>&#160; input1,</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>&#160; 7.0f,</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>&#160; 3,</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>&#160; shape0,</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>&#160; output,</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>&#160; 7.0f,</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>&#160; 3);</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>&#160;}</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>&#160;</div><div class="line"><a name="l00412"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#ae087613cdb8319fbab07d44e6eaf217d"> 412</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;int16_t, 4&gt;</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>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>&#160;{</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>&#160; <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>&#160; <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>&#160;</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>&#160; std::vector&lt;int16_t&gt; input0 =</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>&#160; {</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>&#160; 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>&#160; 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>&#160; };</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>&#160;</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>&#160; std::vector&lt;int16_t&gt; input1 =</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>&#160; {</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>&#160; 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>&#160; 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>&#160; };</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>&#160;</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>&#160; std::vector&lt;int16_t&gt; output =</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>&#160; {</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>&#160; 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>&#160; 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>&#160; };</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>&#160;</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>&#160; <span class="keywordflow">return</span> ElementwiseTestHelper&lt;4, armnn::AdditionQueueDescriptor, armnn::DataType::QSymmS16&gt;(</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>&#160; workloadFactory,</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>&#160; memoryManager,</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>&#160; shape0,</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>&#160; input0,</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>&#160; 7.0f,</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>&#160; 0,</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>&#160; shape1,</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>&#160; input1,</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>&#160; 7.0f,</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>&#160; 0,</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>&#160; shape0,</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>&#160; output,</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>&#160; 7.0f,</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>&#160; 0);</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>&#160;}</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>&#160;</div><div class="line"><a name="l00454"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#aa11fe3b8a07854e2bb9dd3ccecaa96e4"> 454</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;float, 4&gt;</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>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager)</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>&#160;{</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>&#160; boost::ignore_unused(memoryManager);</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>&#160;</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>&#160; <span class="comment">// Create Initial Tensor</span></div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>&#160; <span class="comment">// 1, 2, 3</span></div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>&#160; <span class="comment">// 4, 5, 6</span></div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>&#160; <span class="comment">// 7, 8, 9</span></div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>&#160;</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>&#160; <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>&#160; <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>&#160;</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>&#160; boost::multi_array&lt;float, 4&gt; poolingInput = MakeTensor&lt;float,4&gt;(poolingInputTensorInfo,</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>&#160; {1, 2, 3,</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>&#160; 4, 5, 6,</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>&#160; 7, 8, 9</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>&#160; });</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>&#160;</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; poolingInputHandle =</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>&#160; 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>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; poolingOutputHandle =</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>&#160; 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>&#160;</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>&#160; <span class="comment">// Apply MaxPool poolSize = 1x1, stride=2x2</span></div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>&#160; <span class="comment">// Result =</span></div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>&#160; <span class="comment">// 1, 3</span></div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>&#160; <span class="comment">// 7, 9</span></div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>&#160; <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>&#160; 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<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>&#160; 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>&#160; <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>&#160; AddInputToWorkload(queueDescriptor, workloadInfo, poolingInputTensorInfo, poolingInputHandle.get());</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>&#160; AddOutputToWorkload(queueDescriptor, workloadInfo, poolingOutputTensorInfo, poolingOutputHandle.get());</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>&#160;</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>&#160; <span class="comment">// Create the MaxPool</span></div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>&#160; std::unique_ptr&lt;armnn::IWorkload&gt; 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>&#160;</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>&#160; <span class="comment">//LayerTestResult&lt;float, 4&gt; result(poolingOutputTensorInfo);</span></div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>&#160; <span class="keyword">auto</span> shape( GetTensorShapeAsArray&lt;4&gt;(poolingOutputTensorInfo));</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>&#160; boost::multi_array&lt;float, 4&gt; resultMaxPool;</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>&#160; 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24, 28,</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>&#160; });</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>&#160;</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>&#160; <span class="comment">// Expected output tensor after MaxPool and Addition.</span></div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>&#160; <a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;float,4&gt;</a> addRet(addOutputTensorInfo);</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>&#160; addRet.outputExpected = MakeTensor&lt;float, 4&gt;(addOutputTensorInfo, std::vector&lt;float&gt;(</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>&#160; {</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>&#160; 13, 19,</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>&#160; 31, 37</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>&#160; }));</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>&#160;</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; 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>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; 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>&#160;</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>&#160; <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>&#160; <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>&#160;</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>&#160; <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>&#160; AddInputToWorkload(data, info, poolingOutputTensorInfo, poolingOutputHandle.get());</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>&#160; AddInputToWorkload(data, info, addInputTensorInfo, addInputHandle.get());</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>&#160; AddOutputToWorkload(data, info, addOutputTensorInfo, addOutputHandle.get());</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>&#160;</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>&#160; std::unique_ptr&lt;armnn::IWorkload&gt; 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<a class="code" href="_tensor_copy_utils_8cpp.html#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;resultMaxPool[0][0][0][0], poolingOutputHandle.get());</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>&#160;</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(poolingOutputHandle.get(), &amp;resultMaxPool[0][0][0][0]);</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.html#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(addInputHandle.get(), &amp;addInput[0][0][0][0]);</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>&#160;</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>&#160; workload-&gt;PostAllocationConfigure();</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>&#160; workload-&gt;Execute();</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>&#160; addWorkload-&gt;PostAllocationConfigure();</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>&#160; addWorkload-&gt;Execute();</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>&#160;</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>&#160; <a class="code" href="_tensor_copy_utils_8cpp.html#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&amp;addRet.output[0][0][0][0], addOutputHandle.get());</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>&#160;</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>&#160; <span class="keywordflow">return</span> addRet;</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>&#160;}</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>&#160;</div><div class="line"><a name="l00561"></a><span class="lineno"><a class="line" href="_addition_test_impl_8hpp.html#a50074d57c9208290be87347941e716d7"> 561</a></span>&#160;<a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;float,4&gt;</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>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; workloadFactory,</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.html#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&amp; memoryManager,</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>&#160; <a class="code" href="classarmnn_1_1_i_workload_factory.html">armnn::IWorkloadFactory</a>&amp; refWorkloadFactory)</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>&#160;{</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>&#160; boost::ignore_unused(memoryManager);</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>&#160; <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>&#160; <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>&#160; <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>&#160; <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>&#160;</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>&#160; <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>&#160; <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>&#160;</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>&#160; 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<span class="keyword">auto</span> input1 = MakeRandomTensor&lt;float, 4&gt;(inputTensorInfo1, 1232);</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>&#160; <span class="keyword">auto</span> input2 = MakeRandomTensor&lt;float, 4&gt;(inputTensorInfo2, 456);</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>&#160;</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>&#160; <a class="code" href="struct_layer_test_result.html">LayerTestResult&lt;float,4&gt;</a> ret(outputTensorInfo);</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>&#160;</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; 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>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; 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>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; 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>&#160;</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; 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>&#160; std::unique_ptr&lt;armnn::ITensorHandle&gt; 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<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&lt; T, 4 &gt; AdditionBroadcast1ElementTestImpl(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;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&lt; int16_t, 4 &gt; AdditionInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;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&lt; float, 4 &gt; AdditionTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;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&lt; float, 5 &gt; Addition5dTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;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&lt; ITensorHandle &gt; CreateTensorHandle(const TensorInfo &amp;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&lt; IMemoryManager &gt; 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&lt; T, n &gt; 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&lt; int16_t, 4 &gt; AdditionBroadcastInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;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&lt; float, 4 &gt; CompareAdditionTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;memoryManager, armnn::IWorkloadFactory &amp;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&lt; float, 4 &gt; AdditionBroadcastTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;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&lt; uint8_t, 4 &gt; AdditionBroadcastUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;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&lt; uint8_t, 4 &gt; AdditionUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;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&lt; float, 4 &gt; AdditionBroadcast1ElementTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;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&lt; float, 4 &gt; AdditionAfterMaxPoolTest(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;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&lt; T, n &gt; 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&lt; IWorkload &gt; CreatePooling2d(const Pooling2dQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;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&lt; IWorkload &gt; CreateAddition(const AdditionQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;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&lt; int16_t, 4 &gt; AdditionBroadcast1ElementInt16Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;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&lt; T, 4 &gt; AdditionBroadcastTestImpl(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;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&lt; uint8_t, 4 &gt; AdditionBroadcast1ElementUint8Test(armnn::IWorkloadFactory &amp;workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &amp;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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