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| 87 | <a href="_gather_end_to_end_test_impl_8hpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#pragma once</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include "<a class="code" href="_common_test_utils_8hpp.html">CommonTestUtils.hpp</a>"</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> </div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_i_network_8hpp.html">armnn/INetwork.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_resolve_type_8hpp.html">ResolveType.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="keyword">namespace</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> <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> CreateGatherNetwork(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>& paramsInfo,</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>& indicesInfo,</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>& outputInfo,</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  <span class="keyword">const</span> std::vector<int32_t>& indicesData)</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>  <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.html#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>());</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">armnn::IConnectableLayer</a>* paramsLayer = net->AddInputLayer(0);</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">armnn::IConnectableLayer</a>* indicesLayer = net->AddConstantLayer(<a class="code" href="classarmnn_1_1_const_tensor.html">armnn::ConstTensor</a>(indicesInfo, indicesData));</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">armnn::IConnectableLayer</a>* gatherLayer = net->AddGatherLayer(<span class="stringliteral">"gather"</span>);</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.html">armnn::IConnectableLayer</a>* outputLayer = net->AddOutputLayer(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <a class="code" href="_test_utils_8cpp.html#a0b295acb179f15eb3fb862b32008f782">Connect</a>(paramsLayer, gatherLayer, paramsInfo, 0, 0);</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <a class="code" href="_test_utils_8cpp.html#a0b295acb179f15eb3fb862b32008f782">Connect</a>(indicesLayer, gatherLayer, indicesInfo, 0, 1);</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <a class="code" href="_test_utils_8cpp.html#a0b295acb179f15eb3fb862b32008f782">Connect</a>(gatherLayer, outputLayer, outputInfo, 0, 0);</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keywordflow">return</span> net;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> }</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="keywordtype">void</span> GatherEndToEnd(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> {</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> paramsInfo({ 8 }, ArmnnType);</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> indicesInfo({ 3 }, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>);</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputInfo({ 3 }, ArmnnType);</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>  paramsInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(1.0f);</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  paramsInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(0);</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(1.0f);</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(0);</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>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  std::vector<T> paramsData{</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  1, 2, 3, 4, 5, 6, 7, 8</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> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  std::vector<int32_t> indicesData{</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  7, 6, 5</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  };</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>  std::vector<T> expectedOutput{</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  8, 7, 6</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> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="comment">// Builds up the structure of the network</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> net = CreateGatherNetwork(paramsInfo, indicesInfo, outputInfo, indicesData);</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"create a network"</span>);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> </div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  std::map<int, std::vector<T>> inputTensorData = {{ 0, paramsData }};</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput }};</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> }</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> <span class="keywordtype">void</span> GatherMultiDimEndToEnd(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> {</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> paramsInfo({ 3, 2, 3}, ArmnnType);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> indicesInfo({ 2, 3 }, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a> outputInfo({ 2, 3, 2, 3 }, ArmnnType);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  paramsInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(1.0f);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  paramsInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(0);</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(1.0f);</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(0);</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">// Creates structures for input & output.</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  std::vector<T> paramsData{</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  1, 2, 3,</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  4, 5, 6,</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  7, 8, 9,</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  10, 11, 12,</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> </div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  13, 14, 15,</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  16, 17, 18</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  };</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> </div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  std::vector<int32_t> indicesData{</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  1, 2, 1,</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  2, 1, 0</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  };</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  std::vector<T> expectedOutput{</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  7, 8, 9,</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  10, 11, 12,</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  13, 14, 15,</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  16, 17, 18,</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  7, 8, 9,</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  10, 11, 12,</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  13, 14, 15,</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  16, 17, 18,</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  7, 8, 9,</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  10, 11, 12,</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  1, 2, 3,</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  4, 5, 6</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> </div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="comment">// Builds up the structure of the network</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> net = CreateGatherNetwork(paramsInfo, indicesInfo, outputInfo, indicesData);</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> </div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"create a network"</span>);</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>  std::map<int, std::vector<T>> inputTensorData = {{ 0, paramsData }};</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput }};</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>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> }</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> </div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> } <span class="comment">// anonymous namespace</span></div><div class="ttc" id="classarmnn_1_1_const_tensor_html"><div class="ttname"><a href="classarmnn_1_1_const_tensor.html">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.html#l00199">Tensor.hpp:199</a></div></div> |
| 88 | <div class="ttc" id="classarmnn_1_1_tensor_info_html"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00053">Tensor.hpp:53</a></div></div> |
| 89 | <div class="ttc" id="_resolve_type_8hpp_html"><div class="ttname"><a href="_resolve_type_8hpp.html">ResolveType.hpp</a></div></div> |
| 90 | <div class="ttc" id="_common_test_utils_8hpp_html"><div class="ttname"><a href="_common_test_utils_8hpp.html">CommonTestUtils.hpp</a></div></div> |
| 91 | <div class="ttc" id="classarmnn_1_1_tensor_info_html_a685739c4eb65a580e075282cfe6787d6"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a685739c4eb65a580e075282cfe6787d6">armnn::TensorInfo::SetQuantizationScale</a></div><div class="ttdeci">void SetQuantizationScale(float scale)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00259">Tensor.cpp:259</a></div></div> |
| 92 | <div class="ttc" id="classarmnn_1_1_i_network_html_a706f7345af3f18f4b16e226a672214c6"><div class="ttname"><a href="classarmnn_1_1_i_network.html#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create()</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.html#l00048">Network.cpp:48</a></div></div> |
| 93 | <div class="ttc" id="namespacearmnn_html_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.html#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.html#l00085">INetwork.hpp:85</a></div></div> |
| 94 | <div class="ttc" id="namespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div> |
| 95 | <div class="ttc" id="_test_utils_8cpp_html_a0b295acb179f15eb3fb862b32008f782"><div class="ttname"><a href="_test_utils_8cpp.html#a0b295acb179f15eb3fb862b32008f782">Connect</a></div><div class="ttdeci">void Connect(armnn::IConnectableLayer *from, armnn::IConnectableLayer *to, const armnn::TensorInfo &tensorInfo, unsigned int fromIndex, unsigned int toIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8cpp_source.html#l00012">TestUtils.cpp:12</a></div></div> |
| 96 | <div class="ttc" id="classarmnn_1_1_i_connectable_layer_html"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.html">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.html#l00061">INetwork.hpp:61</a></div></div> |
| 97 | <div class="ttc" id="classarmnn_1_1_tensor_info_html_a63cbc581012c957f9d68d224ddc3e43c"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a63cbc581012c957f9d68d224ddc3e43c">armnn::TensorInfo::SetQuantizationOffset</a></div><div class="ttdeci">void SetQuantizationOffset(int32_t offset)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00275">Tensor.cpp:275</a></div></div> |
| 98 | <div class="ttc" id="_i_network_8hpp_html"><div class="ttname"><a href="_i_network_8hpp.html">INetwork.hpp</a></div></div> |
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