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<div class="title">ReduceMultipleAxesTests.cpp</div> </div>
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<a href="_reduce_multiple_axes_tests_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include <GraphUtils.hpp></span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include <TestUtils.hpp></span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <doctest/doctest.h></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> {</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#if defined(ARMCOMPUTENEON_ENABLED)||defined(ARMCOMPUTECL_ENABLED)</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> CreateSimpleReduceNetwork(<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml">ReduceDescriptor</a> reduceDescriptor,</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape,</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape)</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> {</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <span class="comment">// Create a network</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"reduce_layer"</span>);</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> </div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> reduceLayer = network->AddReduceLayer(reduceDescriptor, layerName.c_str());</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> outputLayer1 = network->AddOutputLayer(0);</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> outputLayer2 = network->AddOutputLayer(1);</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>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  reduceLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</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>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(reduceLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  reduceLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  reduceLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer2-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keywordflow">return</span> network;</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> </div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="keywordtype">void</span> ReduceWithMultipleAxesTest(<a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a>& network,</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape,</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">const</span> std::vector<float>& inputData,</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">const</span> std::vector<float>& expectedOutput,</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> numOfAxes,</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456ae">Compute</a> backendId)</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> {</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="comment">// Create ArmNN runtime</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> run = <a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(<a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a>());</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> </div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="comment">// Optimise ArmNN network</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*network, {backendId}, run->GetDeviceSpec());</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& graph = <a class="code" href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">GetGraphForTesting</a>(optNet.get());</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordflow">if</span> (numOfAxes == 2)</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  {</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  CHECK(graph.GetNumLayers() == 5);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  CHECK(<a class="code" href="est_utils_2_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.cbegin(),</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  graph.cend(),</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  &IsLayerOfType<ReduceLayer>,</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  &IsLayerOfType<ReduceLayer>,</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  &IsLayerOfType<OutputLayer>,</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  } <span class="keywordflow">else</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  {</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  CHECK(graph.GetNumLayers() == 6);</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  CHECK(<a class="code" href="est_utils_2_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.cbegin(),</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  graph.cend(),</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  &IsLayerOfType<ReduceLayer>,</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  &IsLayerOfType<ReduceLayer>,</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  &IsLayerOfType<ReduceLayer>,</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  &IsLayerOfType<OutputLayer>,</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  }</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="comment">// Get last layer in new chain, layers name follow 0, 1, 2 pattern</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  std::string layerName = <span class="stringliteral">"reduce_layer_"</span> + std::to_string(numOfAxes - 1);</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> reduceLayer = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, layerName);</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  CHECK(reduceLayer);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keyword">auto</span> reduceTensorInfo = reduceLayer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>();</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> </div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="comment">// Tensorshape and the data type are correct</span></div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  CHECK((reduceTensorInfo.GetShape() == outputShape));</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  CHECK((reduceTensorInfo.GetDataType() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="comment">// Load network into runtime</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <a class="code" href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">NetworkId</a> networkIdentifier;</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  run->LoadNetwork(networkIdentifier, std::move(optNet));</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="comment">// Create input and output tensors</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  std::vector<float> outputData(expectedOutput.size());</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0);</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">SetConstant</a>(<span class="keyword">true</span>);</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</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>  {0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(inputTensorInfo, inputData.data())}</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>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  {</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  {0, <a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(run->GetOutputTensorInfo(networkIdentifier, 0), outputData.data())},</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  {1, <a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(run->GetOutputTensorInfo(networkIdentifier, 1), outputData.data())}</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  };</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> </div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="comment">// Run inference</span></div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> </div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="comment">// Checks the results</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  CHECK(outputData == expectedOutput);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> }</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> <span class="keywordtype">void</span> ReduceSumWithTwoAxesKeepDimsTest(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456ae">Compute</a> backendId)</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>  <a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml">armnn::ReduceDescriptor</a> reduceDescriptor;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  reduceDescriptor.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#aa1c6fc8c96404252f1072632fc5acb59">m_vAxis</a> = {1, 2};</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  reduceDescriptor.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#a28e0548abfc4e79c48f29a3d11a062e9">m_KeepDims</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  reduceDescriptor.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#aa57c67b1da0011b1abb30170146e870f">m_ReduceOperation</a> = <a class="code" href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0aa0ec87054b5e5b7847d0d8780a01a3d5">armnn::ReduceOperation::Sum</a>;</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>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = {1, 3, 2, 4};</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape = {1, 1, 1, 4};</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>  <span class="comment">// Construct ArmNN network</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = CreateSimpleReduceNetwork(reduceDescriptor, inputShape, outputShape);</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> </div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keyword">const</span> std::vector<float> inputData({1.0f, 2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  5.0f, 6.0f, 7.0f, 8.0f,</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>  10.0f, 20.0f, 30.0f, 40.0f,</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  50.0f, 60.0f, 70.0f, 80.0f,</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>  100.0f, 200.0f, 300.0f, 400.0f,</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  500.0f, 600.0f, 700.0f, 800.0f});</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keyword">const</span> std::vector<float> expectedOutput({666.0f, 888.0f, 1110.0f, 1332.0f});</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> </div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  ReduceWithMultipleAxesTest(network,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  outputShape,</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  inputData,</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  expectedOutput,</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  reduceDescriptor.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#aa1c6fc8c96404252f1072632fc5acb59">m_vAxis</a>.size(),</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  backendId);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> }</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> </div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="keywordtype">void</span> ReduceSumWithTwoAxesTest(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456ae">Compute</a> backendId)</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>  <a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml">armnn::ReduceDescriptor</a> reduceDescriptor;</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  reduceDescriptor.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#aa1c6fc8c96404252f1072632fc5acb59">m_vAxis</a> = {1, 2};</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  reduceDescriptor.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#a28e0548abfc4e79c48f29a3d11a062e9">m_KeepDims</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  reduceDescriptor.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#aa57c67b1da0011b1abb30170146e870f">m_ReduceOperation</a> = <a class="code" href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0aa0ec87054b5e5b7847d0d8780a01a3d5">armnn::ReduceOperation::Sum</a>;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = {1, 3, 2, 4};</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape = {1, 4};</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="comment">// Construct ArmNN network</span></div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = CreateSimpleReduceNetwork(reduceDescriptor, inputShape, outputShape);</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="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keyword">const</span> std::vector<float> inputData({1.0f, 2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  5.0f, 6.0f, 7.0f, 8.0f,</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> </div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  10.0f, 20.0f, 30.0f, 40.0f,</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  50.0f, 60.0f, 70.0f, 80.0f,</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>  100.0f, 200.0f, 300.0f, 400.0f,</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  500.0f, 600.0f, 700.0f, 800.0f});</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keyword">const</span> std::vector<float> expectedOutput({666.0f, 888.0f, 1110.0f, 1332.0f});</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> </div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  ReduceWithMultipleAxesTest(network,</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  outputShape,</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  inputData,</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  expectedOutput,</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  reduceDescriptor.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#aa1c6fc8c96404252f1072632fc5acb59">m_vAxis</a>.size(),</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  backendId);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> }</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> </div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> <span class="keywordtype">void</span> ReduceSumWithThreeAxesKeepDimsTest(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456ae">Compute</a> backendId)</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>  <a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml">armnn::ReduceDescriptor</a> reduceDescriptor;</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  reduceDescriptor.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#aa1c6fc8c96404252f1072632fc5acb59">m_vAxis</a> = {0, 2, 3};</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  reduceDescriptor.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#a28e0548abfc4e79c48f29a3d11a062e9">m_KeepDims</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  reduceDescriptor.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#aa57c67b1da0011b1abb30170146e870f">m_ReduceOperation</a> = <a class="code" href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0aa0ec87054b5e5b7847d0d8780a01a3d5">armnn::ReduceOperation::Sum</a>;</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> </div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = {2, 2, 2, 2};</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape = {1, 2, 1, 1};</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> </div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="comment">// Construct ArmNN network</span></div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = CreateSimpleReduceNetwork(reduceDescriptor, inputShape, outputShape);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> </div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keyword">const</span> std::vector<float> inputData({1.0f, 2.0f,</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  3.0f, 4.0f,</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  5.0f, 6.0f,</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  7.0f, 8.0f,</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> </div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  10.0f, 20.0f,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  30.0f, 40.0f,</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>  50.0f, 60.0f,</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  70.0f, 80.0f});</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keyword">const</span> std::vector<float> expectedOutput({110.0f, 286.0f});</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> </div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  ReduceWithMultipleAxesTest(network,</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  outputShape,</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  inputData,</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  expectedOutput,</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  reduceDescriptor.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#aa1c6fc8c96404252f1072632fc5acb59">m_vAxis</a>.size(),</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  backendId);</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span> }</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> <span class="keywordtype">void</span> ReduceSumWithThreeAxesTest(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456ae">Compute</a> backendId)</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>  <a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml">armnn::ReduceDescriptor</a> reduceDescriptor;</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  reduceDescriptor.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#aa1c6fc8c96404252f1072632fc5acb59">m_vAxis</a> = {0, 2, 3};</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  reduceDescriptor.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#a28e0548abfc4e79c48f29a3d11a062e9">m_KeepDims</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  reduceDescriptor.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#aa57c67b1da0011b1abb30170146e870f">m_ReduceOperation</a> = <a class="code" href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0aa0ec87054b5e5b7847d0d8780a01a3d5">armnn::ReduceOperation::Sum</a>;</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> </div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = {2, 2, 2, 2};</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape = {2};</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> </div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="comment">// Construct ArmNN network</span></div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = CreateSimpleReduceNetwork(reduceDescriptor, inputShape, outputShape);</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> </div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keyword">const</span> std::vector<float> inputData({1.0f, 2.0f,</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  3.0f, 4.0f,</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> </div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  5.0f, 6.0f,</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  7.0f, 8.0f,</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span> </div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  10.0f, 20.0f,</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  30.0f, 40.0f,</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> </div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  50.0f, 60.0f,</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  70.0f, 80.0f});</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keyword">const</span> std::vector<float> expectedOutput({110.0f, 286.0f});</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  ReduceWithMultipleAxesTest(network,</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  outputShape,</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  inputData,</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  expectedOutput,</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  reduceDescriptor.<a class="code" href="structarmnn_1_1_reduce_descriptor.xhtml#aa1c6fc8c96404252f1072632fc5acb59">m_vAxis</a>.size(),</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  backendId);</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> }</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span> <span class="preprocessor">#endif</span></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> </div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> <span class="preprocessor">#if defined(ARMCOMPUTENEON_ENABLED)</span></div><div class="line"><a name="l00253"></a><span class="lineno"><a class="line" href="_reduce_multiple_axes_tests_8cpp.xhtml#aa689d1fb4d03d7fbc7acaef10259fe7a"> 253</a></span> <a class="code" href="namespacearmnn.xhtml#a1621fb2f10314c394c9023d3e090d394">TEST_SUITE</a>(<span class="stringliteral">"Optimizer_ReduceMultipleAxesCpu"</span>)</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span> {</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> TEST_CASE(<span class="stringliteral">"ReduceSumWithTwoAxesKeepDimsCpuAccTest"</span>)</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> {</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  ReduceSumWithTwoAxesKeepDimsTest(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a>);</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> }</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> </div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> TEST_CASE(<span class="stringliteral">"ReduceSumWithTwoAxesCpuAccTest"</span>)</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> {</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  ReduceSumWithTwoAxesTest(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a>);</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> }</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> TEST_CASE(<span class="stringliteral">"ReduceSumWithThreeAxesKeepDimsCpuAccTest"</span>)</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> {</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  ReduceSumWithThreeAxesKeepDimsTest(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a>);</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span> }</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> </div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> TEST_CASE(<span class="stringliteral">"ReduceSumWithThreeAxesCpuAccTest"</span>)</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span> {</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  ReduceSumWithThreeAxesTest(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a>);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span> }</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> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span> </div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span> <span class="preprocessor">#if defined(ARMCOMPUTECL_ENABLED)</span></div><div class="line"><a name="l00278"></a><span class="lineno"><a class="line" href="_reduce_multiple_axes_tests_8cpp.xhtml#a325d29e1f371291bba7b956bb0b79ee2"> 278</a></span> <a class="code" href="namespacearmnn.xhtml#a1621fb2f10314c394c9023d3e090d394">TEST_SUITE</a>(<span class="stringliteral">"Optimizer_ReduceMultipleAxesGpu"</span>)</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span> {</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> TEST_CASE(<span class="stringliteral">"ReduceSumWithTwoAxesKeepDimsGpuAccTest"</span>)</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> {</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  ReduceSumWithTwoAxesKeepDimsTest(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">Compute::GpuAcc</a>);</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> }</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span> </div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> TEST_CASE(<span class="stringliteral">"ReduceSumWithTwoAxesGpuAccTest"</span>)</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span> {</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  ReduceSumWithTwoAxesTest(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">Compute::GpuAcc</a>);</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span> }</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> </div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> TEST_CASE(<span class="stringliteral">"ReduceSumWithThreeAxesKeepDimsGpuAccTest"</span>)</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span> {</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  ReduceSumWithThreeAxesKeepDimsTest(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">Compute::GpuAcc</a>);</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span> }</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> TEST_CASE(<span class="stringliteral">"ReduceSumWithThreeAxesGpuAccTest"</span>)</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span> {</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  ReduceSumWithThreeAxesTest(<a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">Compute::GpuAcc</a>);</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> }</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span> <span class="preprocessor">#endif</span></div><div class="ttc" id="namespacearmnn_xhtml_a1621fb2f10314c394c9023d3e090d394"><div class="ttname"><a href="namespacearmnn.xhtml#a1621fb2f10314c394c9023d3e090d394">armnn::TEST_SUITE</a></div><div class="ttdeci">TEST_SUITE("TestConstTensorLayerVisitor")</div><div class="ttdef"><b>Definition:</b> <a href="_const_tensor_layer_visitor_8cpp_source.xhtml#l00110">ConstTensorLayerVisitor.cpp:110</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_runtime_xhtml_ad44ecd3700748dc30dc4bbe34ba5bde7"><div class="ttname"><a href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a></div><div class="ttdeci">static IRuntimePtr Create(const CreationOptions &options)</div><div class="ttdef"><b>Definition:</b> <a href="_runtime_8cpp_source.xhtml#l00040">Runtime.cpp:40</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00066">INetwork.hpp:66</a></div></div>
<div class="ttc" id="est_utils_2_test_utils_8hpp_xhtml_a0eedb278f57355b47fa983450d4e378c"><div class="ttname"><a href="est_utils_2_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a></div><div class="ttdeci">bool CheckSequence(const armnn::Graph::ConstIterator first, const armnn::Graph::ConstIterator last)</div><div class="ttdef"><b>Definition:</b> <a href="est_utils_2_test_utils_8hpp_source.xhtml#l00021">TestUtils.hpp:21</a></div></div>
<div class="ttc" id="_graph_utils_8cpp_xhtml_a5f17e02e0054dac0a691685a0464ed36"><div class="ttname"><a href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a></div><div class="ttdeci">armnn::Layer * GetFirstLayerWithName(armnn::Graph &graph, const std::string &name)</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00022">GraphUtils.cpp:22</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="structarmnn_1_1_reduce_descriptor_xhtml_a28e0548abfc4e79c48f29a3d11a062e9"><div class="ttname"><a href="structarmnn_1_1_reduce_descriptor.xhtml#a28e0548abfc4e79c48f29a3d11a062e9">armnn::ReduceDescriptor::m_KeepDims</a></div><div class="ttdeci">bool m_KeepDims</div><div class="ttdoc">if true then output shape has no change. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01499">Descriptors.hpp:1499</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a150468a02bd7b2d2d061c4aaaee939f0"><div class="ttname"><a href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a></div><div class="ttdeci">std::unique_ptr< IRuntime, void(*)(IRuntime *runtime)> IRuntimePtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00031">IRuntime.hpp:31</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_aa01bce88f89975a5a031db4cc8861527"><div class="ttname"><a href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">armnn::InputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class ConstTensor > > InputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00392">Tensor.hpp:392</a></div></div>
<div class="ttc" id="structarmnn_1_1_reduce_descriptor_xhtml_aa57c67b1da0011b1abb30170146e870f"><div class="ttname"><a href="structarmnn_1_1_reduce_descriptor.xhtml#aa57c67b1da0011b1abb30170146e870f">armnn::ReduceDescriptor::m_ReduceOperation</a></div><div class="ttdeci">ReduceOperation m_ReduceOperation</div><div class="ttdoc">Specifies the reduction operation to execute. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01503">Descriptors.hpp:1503</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__quick__start_8dox_source.xhtml#l00006">01_00_quick_start.dox:6</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &tensorInfo)=0</div></div>
<div class="ttc" id="classarmnn_1_1_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and a mutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00319">Tensor.hpp:319</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456ae"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456ae">armnn::Compute</a></div><div class="ttdeci">Compute</div><div class="ttdoc">The Compute enum is now deprecated and it is now being replaced by BackendId. </div><div class="ttdef"><b>Definition:</b> <a href="_backend_id_8hpp_source.xhtml#l00021">BackendId.hpp:21</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a82e98ef05fd67036d1195ba17174d685"><div class="ttname"><a href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a></div><div class="ttdeci">IOptimizedNetworkPtr Optimize(const INetwork &network, const std::vector< BackendId > &backendPreferences, const IDeviceSpec &deviceSpec, const OptimizerOptions &options=OptimizerOptions(), Optional< std::vector< std::string > &> messages=EmptyOptional())</div><div class="ttdoc">Create an optimized version of the network. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l01680">Network.cpp:1680</a></div></div>
<div class="ttc" id="structarmnn_1_1_reduce_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_reduce_descriptor.xhtml">armnn::ReduceDescriptor</a></div><div class="ttdoc">A ReduceDescriptor for the REDUCE operators. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01483">Descriptors.hpp:1483</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a0d8160388a127c1a23b37bc88dc6e2ec"><div class="ttname"><a href="namespacearmnn.xhtml#a0d8160388a127c1a23b37bc88dc6e2ec">armnn::NetworkId</a></div><div class="ttdeci">int NetworkId</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00025">IRuntime.hpp:25</a></div></div>
<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00327">Tensor.hpp:327</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a8f091a512915d1cb29a4ebf13dfc53ea"><div class="ttname"><a href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">armnn::OutputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class Tensor > > OutputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00393">Tensor.hpp:393</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a674efcf6cbdb9e831d653ff0e821fb38"><div class="ttname"><a href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a></div><div class="ttdeci">std::unique_ptr< IOptimizedNetwork, void(*)(IOptimizedNetwork *network)> IOptimizedNetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00242">INetwork.hpp:242</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a></div><div class="ttdoc">GPU Execution: OpenCL: ArmCompute. </div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml">armnn::Graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00030">Graph.hpp:30</a></div></div>
<div class="ttc" id="_i_network_8hpp_xhtml"><div class="ttname"><a href="_i_network_8hpp.xhtml">INetwork.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_reduce_descriptor_xhtml_aa1c6fc8c96404252f1072632fc5acb59"><div class="ttname"><a href="structarmnn_1_1_reduce_descriptor.xhtml#aa1c6fc8c96404252f1072632fc5acb59">armnn::ReduceDescriptor::m_vAxis</a></div><div class="ttdeci">std::vector< uint32_t > m_vAxis</div><div class="ttdoc">The indices of the dimensions to reduce. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01501">Descriptors.hpp:1501</a></div></div>
<div class="ttc" id="structarmnn_1_1_i_runtime_1_1_creation_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00075">IRuntime.hpp:75</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a6a2659750d6161b693d0e51616791959"><div class="ttname"><a href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">armnn::GetGraphForTesting</a></div><div class="ttdeci">Graph & GetGraphForTesting(IOptimizedNetwork *optNet)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8cpp_source.xhtml#l00047">TestUtils.cpp:47</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a></div><div class="ttdoc">CPU Execution: NEON: ArmCompute. </div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot & GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8ffca1e21bdfa7f945617acd606aac91"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8ffca1e21bdfa7f945617acd606aac91">armnn::TensorInfo::SetConstant</a></div><div class="ttdeci">void SetConstant(const bool IsConstant=true)</div><div class="ttdoc">Marks the data corresponding to this tensor info as constant. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00516">Tensor.cpp:516</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_a0e36688a43c35668d8db5257274c68fe"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">armnn::Layer::GetOutputSlot</a></div><div class="ttdeci">const OutputSlot & GetOutputSlot(unsigned int index=0) const override</div><div class="ttdoc">Get the const output slot handle by slot index. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00323">Layer.hpp:323</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot & GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index. </div></div>
<div class="ttc" id="namespacearmnn_xhtml_abc05539fc6e7907f32ef0fb242e3b3b0aa0ec87054b5e5b7847d0d8780a01a3d5"><div class="ttname"><a href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0aa0ec87054b5e5b7847d0d8780a01a3d5">armnn::ReduceOperation::Sum</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00241">INetwork.hpp:241</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &destination)=0</div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_ada2ad7d1caeeb4ef6195c8925fad6a65"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#ada2ad7d1caeeb4ef6195c8925fad6a65">armnn::OutputSlot::GetTensorInfo</a></div><div class="ttdeci">const TensorInfo & GetTensorInfo() const override</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00066">Layer.cpp:66</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a464f0ff87b1aabf71febaa71321dd40b"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create(NetworkOptions networkOptions={})</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00492">Network.cpp:492</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml">armnn::Layer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00215">Layer.hpp:215</a></div></div>
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