| 101 | <a href="_concat_end_to_end_test_impl_8hpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="preprocessor">#pragma once</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> </div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "<a class="code" href="_common_test_utils_8hpp.xhtml">CommonTestUtils.hpp</a>"</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>></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="preprocessor">#include <boost/test/unit_test.hpp></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <vector></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="keyword">namespace</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> {</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> </div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="keyword">template</span><<span class="keyword">typename</span> armnn::DataType DataType></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> CreateConcatNetwork(<span class="keyword">const</span> std::vector<TensorShape>& inputShapes,</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <span class="keyword">const</span> TensorShape &outputShape,</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatAxis,</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keyword">const</span> int32_t qOffset = 0)</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> {</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">INetwork::Create</a>());</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> descriptor;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  descriptor = <a class="code" href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">CreateDescriptorForConcatenation</a>(inputShapes.begin(),</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  inputShapes.end(),</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  concatAxis);</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* concat = net->AddConcatLayer(descriptor, <span class="stringliteral">"concat"</span>);</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputShapes.size(); ++i)</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  {</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo(inputShapes[i], <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, qScale, qOffset);</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = net->AddInputLayer(boost::numeric_cast<LayerBindingId>(i));</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, concat, inputTensorInfo, 0, i);</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> </div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, qScale, qOffset);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = net->AddOutputLayer(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(concat, output, outputTensorInfo, 0, 0);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordflow">return</span> net;</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> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="keyword">template</span><armnn::DataType ArmnnType></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> <span class="keywordtype">void</span> ConcatDim0EndToEnd(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> {</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keyword">using</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType<ArmnnType></a>;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatAxis = 0;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keyword">const</span> std::vector<TensorShape> inputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape = { 4, 3, 2, 2 };</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="comment">// Builds up the structure of the network</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateConcatNetwork<ArmnnType>(inputShapes, outputShape, concatAxis);</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> </div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"create a network"</span>);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  std::vector<T> inputData{</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  1, 2,</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  3, 4,</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  5, 6,</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  7, 8,</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  9, 10,</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  11, 12,</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  1, 2,</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  3, 4,</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  5, 6,</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  7, 8,</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  9, 10,</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  11, 12</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  };</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> </div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  std::vector<T> expectedOutput{</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  1, 2,</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  3, 4,</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  5, 6,</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  7, 8,</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  9, 10,</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  11, 12,</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  1, 2,</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  3, 4,</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  5, 6,</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  7, 8,</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  9, 10,</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  11, 12,</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  1, 2,</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  3, 4,</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  5, 6,</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  7, 8,</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  9, 10,</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  11, 12,</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  1, 2,</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  3, 4,</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  5, 6,</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  7, 8,</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  9, 10,</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  11, 12</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  };</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> </div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }, { 1,inputData }};</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  std::map<int, std::vector<T>> expectedOutputData = {{ 0,expectedOutput }};</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> </div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);</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="keyword">template</span><armnn::DataType ArmnnType></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> <span class="keywordtype">void</span> ConcatDim1EndToEnd(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> {</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keyword">using</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType<ArmnnType></a>;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> </div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatAxis = 1;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keyword">const</span> std::vector<TensorShape> inputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape = { 2, 6, 2, 2 };</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">// Builds up the structure of the network</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateConcatNetwork<ArmnnType>(inputShapes, outputShape, concatAxis);</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>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"create a network"</span>);</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  std::vector<T> inputData{</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  1, 2,</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  3, 4,</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  5, 6,</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  7, 8,</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  9, 10,</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  11, 12,</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  1, 2,</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  3, 4,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  5, 6,</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  7, 8,</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  9, 10,</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  11, 12</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  };</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> </div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  std::vector<T> expectedOutput{</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  1, 2,</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  3, 4,</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  5, 6,</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  7, 8,</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  9, 10,</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  11, 12,</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  1, 2,</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  3, 4,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  5, 6,</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  7, 8,</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  9, 10,</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  11, 12,</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  1, 2,</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  3, 4,</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  5, 6,</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  7, 8,</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  9, 10,</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  11, 12,</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  1, 2,</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  3, 4,</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  5, 6,</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  7, 8,</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  9, 10,</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  11, 12</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  };</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> </div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }, { 1,inputData }};</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  std::map<int, std::vector<T>> expectedOutputData = {{ 0,expectedOutput }};</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> </div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);</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="keyword">template</span><armnn::DataType ArmnnType></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> <span class="keywordtype">void</span> ConcatDim2EndToEnd(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> {</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keyword">using</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType<ArmnnType></a>;</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatAxis = 2;</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keyword">const</span> std::vector<TensorShape> inputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape = { 2, 3, 4, 2 };</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">// Builds up the structure of the network</span></div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateConcatNetwork<ArmnnType>(inputShapes, outputShape, concatAxis);</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>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"create a network"</span>);</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> </div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  std::vector<T> inputData{</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  1, 2,</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  3, 4,</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  5, 6,</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  7, 8,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  9, 10,</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  11, 12,</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  1, 2,</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  3, 4,</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  5, 6,</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  7, 8,</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  9, 10,</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  11, 12</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  };</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> </div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  std::vector<T> expectedOutput{</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  1, 2,</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  3, 4,</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  1, 2,</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  3, 4,</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  5, 6,</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  7, 8,</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  5, 6,</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  7, 8,</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  9, 10,</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  11, 12,</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  9, 10,</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  11, 12,</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  1, 2,</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  3, 4,</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  1, 2,</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  3, 4,</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  5, 6,</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  7, 8,</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  5, 6,</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  7, 8,</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  9, 10,</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  11, 12,</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  9, 10,</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  11, 12</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  };</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>  std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }, { 1,inputData }};</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  std::map<int, std::vector<T>> expectedOutputData = {{ 0,expectedOutput }};</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> </div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span> }</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> </div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span> <span class="keywordtype">void</span> ConcatDim3EndToEnd(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> {</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatAxis = 3;</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keyword">const</span> std::vector<TensorShape> inputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape = { 2, 3, 2, 4 };</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> </div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="comment">// Builds up the structure of the network</span></div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateConcatNetwork<ArmnnType>(inputShapes, outputShape, concatAxis);</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> </div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"create a network"</span>);</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> </div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  std::vector<T> inputData{</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  1, 2,</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  3, 4,</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  5, 6,</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  7, 8,</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  9, 10,</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  11, 12,</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  1, 2,</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  3, 4,</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  5, 6,</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  7, 8,</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  9, 10,</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  11, 12</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  };</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>  std::vector<T> expectedOutput{</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  1, 2,</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  1, 2,</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  3, 4,</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  3, 4,</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  5, 6,</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  5, 6,</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  7, 8,</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  7, 8,</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  9, 10,</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  9, 10,</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  11, 12,</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  11, 12,</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  1, 2,</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  1, 2,</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  3, 4,</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  3, 4,</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  5, 6,</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  5, 6,</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  7, 8,</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  7, 8,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  9, 10,</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  9, 10,</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  11, 12,</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  11, 12</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  };</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span> </div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }, { 1,inputData }};</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  std::map<int, std::vector<T>> expectedOutputData = {{ 0,expectedOutput }};</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> </div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span> }</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span> </div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span> } <span class="comment">// anonymous namespace</span></div><div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00061">INetwork.hpp:61</a></div></div> |