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<div class="title">NeonFallbackTests.cpp</div> </div>
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<a href="_neon_fallback_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 © 2020 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 <<a class="code" href="_common_test_utils_8hpp.xhtml">backendsCommon/test/CommonTestUtils.hpp</a>></span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include <<a class="code" href="_mock_import_backend_8hpp.xhtml">backendsCommon/test/mockBackend/MockImportBackend.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="_graph_utils_8hpp.xhtml">test/GraphUtils.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 <boost/test/unit_test.hpp></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> <a class="code" href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a>(NeonFallback)</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"><a class="line" href="_neon_fallback_tests_8cpp.xhtml#aa58a47ff11eeb9ea65568f1197fb83d4"> 15</a></span> <a class="code" href="_neon_fallback_tests_8cpp.xhtml#aa58a47ff11eeb9ea65568f1197fb83d4">BOOST_AUTO_TEST_CASE</a>(FallbackImportToCpuAcc)</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">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</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>  <span class="comment">// Create a mock backend object</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  <a class="code" href="classarmnn_1_1_mock_import_backend_initialiser.xhtml">MockImportBackendInitialiser</a> initialiser; <span class="comment">// Register the Mock Backend</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  <span class="keyword">auto</span> backendObjPtr = <a class="code" href="_common_test_utils_8cpp.xhtml#a7a4090354279f08b1e27244bab25aa86">CreateBackendObject</a>(<a class="code" href="namespacearmnn.xhtml#ac38529227e1adeef300d31625826382b">MockImportBackendId</a>());</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  BOOST_TEST((backendObjPtr != <span class="keyword">nullptr</span>));</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> </div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <a class="code" href="namespacearmnn.xhtml#a1854d9cda81304325664363c1fd0fb27">BackendIdSet</a> backendIds = <a class="code" href="namespacearmnn.xhtml#ac2807505b850738bc8a1991ce669dd47">BackendRegistryInstance</a>().<a class="code" href="classarmnn_1_1_backend_registry.xhtml#a570cb1835ec73000a7954ba75257904f">GetBackendIds</a>();</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keywordflow">if</span> (backendIds.find(<span class="stringliteral">"MockRef"</span>) == backendIds.end())</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>  std::string message = <span class="stringliteral">"Cannot load MockRef"</span>;</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  BOOST_FAIL(message);</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> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="comment">// Create runtime in which test will run and allow fallback to CpuRef.</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = net->AddInputLayer(0, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = net->AddInputLayer(1, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input2 = net->AddInputLayer(2, <span class="stringliteral">"input2"</span>);</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>* add = net->AddAdditionLayer(<span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* sub = net->AddSubtractionLayer(<span class="stringliteral">"sub"</span>);</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</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="l00044"></a><span class="lineno"> 44</span> </div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  input0-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  input1-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  input2-><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>(sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  add-><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>(sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  sub-><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>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 3, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</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>  input0-><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>(info);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  input1-><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>(info);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  input2-><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>(info);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  add-><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>(info);</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  sub-><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>(info);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="comment">// optimize the network</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  std::vector<BackendId> backends = { <span class="stringliteral">"MockRef"</span>, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a> };</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="structarmnn_1_1_optimizer_options.xhtml">OptimizerOptions</a> optOptions;</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  optOptions.<a class="code" href="structarmnn_1_1_optimizer_options.xhtml#a05c1bba6ba3ecc1339d4c4c10c0d8890">m_ImportEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec(), optOptions);</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>  <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="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer0 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer1 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer2 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input2"</span>);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer3 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer4 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"[ add (0) -> sub (1) ]"</span>);</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer5 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"sub"</span>);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer6 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> </div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="comment">// Checks order is valid.</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer0, layer1));</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer1, layer2));</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer2, layer3));</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer3, layer4));</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer4, layer5));</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer5, layer6));</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>  <span class="comment">// Load it into the runtime. It should pass.</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  std::string ignoredErrorMessage;</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">true</span>, <span class="keyword">true</span>);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> </div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="comment">// Creates structures for input & output</span></div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  std::vector<float> inputData0</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>  1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f</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>  std::vector<float> inputData1</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>  0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  };</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  std::vector<float> inputData2</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>  12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f</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> </div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  std::vector<float> outputData(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>  std::vector<float> expectedOutput</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>  11.0f, 9.0f, 7.0f, 5.0f, 3.0f, 1.0f, -1.0f, -3.0f, -5.0f, -7.0f, -9.0f, -11.0f</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> </div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  {</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  { 0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) },</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  { 1, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) },</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  { 2, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) }</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>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</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>  { 0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) }</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  };</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>  runtime->GetProfiler(netId)->EnableProfiling(<span class="keyword">true</span>);</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>  <span class="comment">// Do the inference</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  runtime->EnqueueWorkload(netId, inputTensors, outputTensors);</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> </div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="comment">// Retrieve the Profiler.Print() output to get the workload execution</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <a class="code" href="classarmnn_1_1_profiler_manager.xhtml">ProfilerManager</a>& profilerManager = <a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a>();</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  std::stringstream ss;</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  profilerManager.<a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">GetProfiler</a>()-><a class="code" href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">Print</a>(ss);;</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  std::string dump = ss.str();</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>  <span class="comment">// Contains ImportMemGeneric</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  std::size_t found = dump.find(<span class="stringliteral">"ImportMemGeneric"</span>);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> </div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="comment">// Contains SyncMemGeneric</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  found = dump.find(<span class="stringliteral">"SyncMemGeneric"</span>);</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="comment">// Does not contain CopyMemGeneric</span></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  found = dump.find(<span class="stringliteral">"CopyMemGeneric"</span>);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  BOOST_TEST(found == std::string::npos);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="comment">// Use memory import between backends</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  BOOST_TEST((layer4-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a115bfc5d633eae55c67f9588acdd2bf9">LayerType::MemImport</a>));</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="comment">// Check output is as expected</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  BOOST_TEST(outputData == expectedOutput);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> }</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> </div><div class="line"><a name="l00152"></a><span class="lineno"><a class="line" href="_neon_fallback_tests_8cpp.xhtml#a7c907180763a8801535ef690a3931ecd"> 152</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FallbackPaddingCopyToCpuAcc)</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> {</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> </div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="comment">// Create a mock backend object</span></div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <a class="code" href="classarmnn_1_1_mock_import_backend_initialiser.xhtml">MockImportBackendInitialiser</a> initialiser; <span class="comment">// Register the Mock Backend</span></div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keyword">auto</span> backendObjPtr = <a class="code" href="_common_test_utils_8cpp.xhtml#a7a4090354279f08b1e27244bab25aa86">CreateBackendObject</a>(<a class="code" href="namespacearmnn.xhtml#ac38529227e1adeef300d31625826382b">MockImportBackendId</a>());</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  BOOST_TEST((backendObjPtr != <span class="keyword">nullptr</span>));</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> </div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <a class="code" href="namespacearmnn.xhtml#a1854d9cda81304325664363c1fd0fb27">BackendIdSet</a> backendIds = <a class="code" href="namespacearmnn.xhtml#ac2807505b850738bc8a1991ce669dd47">BackendRegistryInstance</a>().<a class="code" href="classarmnn_1_1_backend_registry.xhtml#a570cb1835ec73000a7954ba75257904f">GetBackendIds</a>();</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keywordflow">if</span> (backendIds.find(<span class="stringliteral">"MockRef"</span>) == backendIds.end())</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  {</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  std::string message = <span class="stringliteral">"Cannot load MockRef"</span>;</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  BOOST_FAIL(message);</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  }</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>  <span class="comment">// Create runtime in which test will run and allow fallback to CpuRef.</span></div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</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>  <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> </div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> desc;</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = net->AddInputLayer(0, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = net->AddInputLayer(1, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* add = net->AddAdditionLayer(<span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* pooling = net->AddPooling2dLayer(desc, <span class="stringliteral">"pooling"</span>);</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</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="l00182"></a><span class="lineno"> 182</span> </div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  input0-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  input1-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  add-><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>(pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  pooling-><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>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> </div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 3, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> poolingInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 1, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> </div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  input0-><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>(info);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  input1-><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>(info);</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  add-><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>(info);</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  pooling-><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>(poolingInfo);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> </div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="comment">// optimize the network</span></div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  std::vector<BackendId> backends = { <span class="stringliteral">"MockRef"</span>, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a> };</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <a class="code" href="structarmnn_1_1_optimizer_options.xhtml">OptimizerOptions</a> optOptions;</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  optOptions.<a class="code" href="structarmnn_1_1_optimizer_options.xhtml#a05c1bba6ba3ecc1339d4c4c10c0d8890">m_ImportEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec(), optOptions);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> </div><div class="line"><a name="l00202"></a><span class="lineno"> 202</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="l00203"></a><span class="lineno"> 203</span> </div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer0 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer1 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer2 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer3 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"[ add (0) -> pooling (0) ]"</span>);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer4 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"pooling"</span>);</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer5 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"output"</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>  <span class="comment">// Checks order is valid.</span></div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer0, layer1));</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer1, layer2));</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer2, layer3));</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer3, layer4));</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer4, layer5));</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="comment">// Load it into the runtime. It should pass.</span></div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  std::string ignoredErrorMessage;</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">true</span>, <span class="keyword">true</span>);</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span> </div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);</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">// Creates structures for input & output</span></div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  std::vector<float> inputData0</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>  1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  };</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  std::vector<float> inputData1</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>  0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  };</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>  std::vector<float> outputData(2);</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>  std::vector<float> expectedOutput</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  {</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  6.0f, 12.0f</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> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</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>  { 0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) },</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  { 1, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) }</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>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</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>  { 0, <a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) }</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>  runtime->GetProfiler(netId)->EnableProfiling(<span class="keyword">true</span>);</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span> </div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="comment">// Do the inference</span></div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  runtime->EnqueueWorkload(netId, inputTensors, outputTensors);</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>  <span class="comment">// Retrieve the Profiler.Print() output to get the workload execution</span></div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <a class="code" href="classarmnn_1_1_profiler_manager.xhtml">ProfilerManager</a>& profilerManager = <a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a>();</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  std::stringstream ss;</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  profilerManager.<a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">GetProfiler</a>()-><a class="code" href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">Print</a>(ss);;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  std::string dump = ss.str();</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> </div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="comment">// Contains CopyMemGeneric between the backends</span></div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  std::size_t found = dump.find(<span class="stringliteral">"CopyMemGeneric"</span>);</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  BOOST_TEST(found != std::string::npos);</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>  <span class="comment">// Contains SyncMemGeneric for the output</span></div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  found = dump.find(<span class="stringliteral">"SyncMemGeneric"</span>);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> </div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="comment">// Does not contain ImportMemGeneric</span></div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  found = dump.find(<span class="stringliteral">"ImportMemGeneric"</span>);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  BOOST_TEST(found == std::string::npos);</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="comment">// Use memory import between backends</span></div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  BOOST_TEST((layer3-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">LayerType::MemCopy</a>));</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span> </div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="comment">// Check output is as expected</span></div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  BOOST_TEST(outputData == expectedOutput);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> }</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> </div><div class="line"><a name="l00282"></a><span class="lineno"><a class="line" href="_neon_fallback_tests_8cpp.xhtml#ad3df6190d73ec3575e8274994e65f058"> 282</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FallbackImportFromCpuAcc)</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>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> </div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="comment">// Create a mock backend object</span></div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <a class="code" href="classarmnn_1_1_mock_import_backend_initialiser.xhtml">MockImportBackendInitialiser</a> initialiser; <span class="comment">// Register the Mock Backend</span></div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keyword">auto</span> backendObjPtr = <a class="code" href="_common_test_utils_8cpp.xhtml#a7a4090354279f08b1e27244bab25aa86">CreateBackendObject</a>(<a class="code" href="namespacearmnn.xhtml#ac38529227e1adeef300d31625826382b">MockImportBackendId</a>());</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  BOOST_TEST((backendObjPtr != <span class="keyword">nullptr</span>));</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> </div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <a class="code" href="namespacearmnn.xhtml#a1854d9cda81304325664363c1fd0fb27">BackendIdSet</a> backendIds = <a class="code" href="namespacearmnn.xhtml#ac2807505b850738bc8a1991ce669dd47">BackendRegistryInstance</a>().<a class="code" href="classarmnn_1_1_backend_registry.xhtml#a570cb1835ec73000a7954ba75257904f">GetBackendIds</a>();</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="keywordflow">if</span> (backendIds.find(<span class="stringliteral">"MockRef"</span>) == backendIds.end())</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>  std::string message = <span class="stringliteral">"Cannot load MockRef"</span>;</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  BOOST_FAIL(message);</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> </div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="comment">// Create runtime in which test will run and allow fallback to CpuRef.</span></div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> </div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span> </div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = net->AddInputLayer(0, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = net->AddInputLayer(1, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input2 = net->AddInputLayer(2, <span class="stringliteral">"input2"</span>);</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* sub = net->AddSubtractionLayer(<span class="stringliteral">"sub"</span>);</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* add = net->AddAdditionLayer(<span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</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="l00311"></a><span class="lineno"> 311</span> </div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  input0-><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>(sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  input1-><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>(sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  input2-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  sub-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  add-><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>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span> </div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 3, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span> </div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  input0-><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>(info);</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  input1-><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>(info);</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  input2-><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>(info);</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  sub-><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>(info);</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  add-><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>(info);</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> </div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <span class="comment">// optimize the network</span></div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  std::vector<BackendId> backends = { <span class="stringliteral">"MockRef"</span>, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a> };</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <a class="code" href="structarmnn_1_1_optimizer_options.xhtml">OptimizerOptions</a> optOptions;</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  optOptions.<a class="code" href="structarmnn_1_1_optimizer_options.xhtml#a05c1bba6ba3ecc1339d4c4c10c0d8890">m_ImportEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec(), optOptions);</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span> </div><div class="line"><a name="l00332"></a><span class="lineno"> 332</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="l00333"></a><span class="lineno"> 333</span> </div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer0 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer1 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer2 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input2"</span>);</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer3 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"sub"</span>);</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer4 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"[ sub (0) -> add (1) ]"</span>);</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer5 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer6 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span> </div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="comment">// Checks order is valid.</span></div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer0, layer1));</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer1, layer2));</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer2, layer3));</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer3, layer4));</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer4, layer5));</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer5, layer6));</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span> </div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="comment">// Load it into the runtime. It should pass.</span></div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  std::string ignoredErrorMessage;</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">true</span>, <span class="keyword">true</span>);</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> </div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span> </div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <span class="comment">// Creates structures for input & output</span></div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  std::vector<float> inputData0</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  {</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 0.0f</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  };</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  std::vector<float> inputData1</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  {</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  };</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  std::vector<float> inputData2</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  {</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  };</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span> </div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  std::vector<float> outputData(12);</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span> </div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  std::vector<float> expectedOutput</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  {</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  13.0f, 11.0f, 11.0f, 9.0f, 7.0f, 7.0f, 7.0f, 5.0f, 5.0f, 3.0f, 3.0f, -5.0f</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  };</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span> </div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  {</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  { 0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) },</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  { 1, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) },</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  { 2, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) }</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  };</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  {</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  { 0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) }</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  };</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> </div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  runtime->GetProfiler(netId)->EnableProfiling(<span class="keyword">true</span>);</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> </div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="comment">// Do the inference</span></div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  runtime->EnqueueWorkload(netId, inputTensors, outputTensors);</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span> </div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="comment">// Retrieve the Profiler.Print() output to get the workload execution</span></div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <a class="code" href="classarmnn_1_1_profiler_manager.xhtml">ProfilerManager</a>& profilerManager = <a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a>();</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  std::stringstream ss;</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  profilerManager.<a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">GetProfiler</a>()-><a class="code" href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">Print</a>(ss);;</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  std::string dump = ss.str();</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span> </div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="comment">// Contains ImportMemGeneric</span></div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  std::size_t found = dump.find(<span class="stringliteral">"ImportMemGeneric"</span>);</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span> </div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="comment">// Contains SyncMemGeneric</span></div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  found = dump.find(<span class="stringliteral">"SyncMemGeneric"</span>);</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span> </div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="comment">// Does not contain CopyMemGeneric</span></div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  found = dump.find(<span class="stringliteral">"CopyMemGeneric"</span>);</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  BOOST_TEST(found == std::string::npos);</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span> </div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <span class="comment">// Use memory import between backends</span></div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  BOOST_TEST((layer4-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a115bfc5d633eae55c67f9588acdd2bf9">LayerType::MemImport</a>));</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span> </div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <span class="comment">// Check output is as expected</span></div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  BOOST_TEST(outputData == expectedOutput);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span> }</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> </div><div class="line"><a name="l00419"></a><span class="lineno"><a class="line" href="_neon_fallback_tests_8cpp.xhtml#a11077d8b798e0d6e0bb552e1bde0e62a"> 419</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FallbackPaddingCopyFromCpuAcc)</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span> {</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span> </div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="comment">// Create a mock backend object</span></div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <a class="code" href="classarmnn_1_1_mock_import_backend_initialiser.xhtml">MockImportBackendInitialiser</a> initialiser; <span class="comment">// Register the Mock Backend</span></div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keyword">auto</span> backendObjPtr = <a class="code" href="_common_test_utils_8cpp.xhtml#a7a4090354279f08b1e27244bab25aa86">CreateBackendObject</a>(<a class="code" href="namespacearmnn.xhtml#ac38529227e1adeef300d31625826382b">MockImportBackendId</a>());</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  BOOST_TEST((backendObjPtr != <span class="keyword">nullptr</span>));</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span> </div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <a class="code" href="namespacearmnn.xhtml#a1854d9cda81304325664363c1fd0fb27">BackendIdSet</a> backendIds = <a class="code" href="namespacearmnn.xhtml#ac2807505b850738bc8a1991ce669dd47">BackendRegistryInstance</a>().<a class="code" href="classarmnn_1_1_backend_registry.xhtml#a570cb1835ec73000a7954ba75257904f">GetBackendIds</a>();</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="keywordflow">if</span> (backendIds.find(<span class="stringliteral">"MockRef"</span>) == backendIds.end())</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  {</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  std::string message = <span class="stringliteral">"Cannot load MockRef"</span>;</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  BOOST_FAIL(message);</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  }</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span> </div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="comment">// Create runtime in which test will run and allow fallback to CpuRef.</span></div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span> </div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span> </div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> desc;</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span> </div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = net->AddInputLayer(0, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = net->AddInputLayer(1, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* pooling = net->AddPooling2dLayer(desc, <span class="stringliteral">"pooling"</span>);</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* add = net->AddAdditionLayer(<span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</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="l00449"></a><span class="lineno"> 449</span> </div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  input0-><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>(pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  input1-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  pooling-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  add-><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>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span> </div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 3, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> poolingInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 1, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> </div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  input0-><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="l00459"></a><span class="lineno"> 459</span>  input1-><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>(poolingInfo);</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  pooling-><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>(poolingInfo);</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  add-><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>(poolingInfo);</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span> </div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="comment">// optimize the network</span></div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  std::vector<BackendId> backends = { <span class="stringliteral">"MockRef"</span>, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a> };</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <a class="code" href="structarmnn_1_1_optimizer_options.xhtml">OptimizerOptions</a> optOptions;</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  optOptions.<a class="code" href="structarmnn_1_1_optimizer_options.xhtml#a05c1bba6ba3ecc1339d4c4c10c0d8890">m_ImportEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec(), optOptions);</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span> </div><div class="line"><a name="l00469"></a><span class="lineno"> 469</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="l00470"></a><span class="lineno"> 470</span> </div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer0 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer1 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer2 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"pooling"</span>);</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer3 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"[ pooling (0) -> add (0) ]"</span>);</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer4 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer5 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span> </div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <span class="comment">// Checks order is valid.</span></div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer0, layer1));</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer1, layer2));</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer2, layer3));</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer3, layer4));</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer4, layer5));</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span> </div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <span class="comment">// Load it into the runtime. It should pass.</span></div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  std::string ignoredErrorMessage;</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">true</span>, <span class="keyword">true</span>);</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span> </div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span> </div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  <span class="comment">// Creates structures for input & output</span></div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  std::vector<float> inputData0</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  {</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  };</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  std::vector<float> inputData1</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  {</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  -1.0f, 3.0f</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  };</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span> </div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  std::vector<float> outputData(2);</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span> </div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  std::vector<float> expectedOutput</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  {</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  5.0f, 15.0f</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  };</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span> </div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  {</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  { 0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) },</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  { 1, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) }</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  };</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  {</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  { 0, <a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) }</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  };</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> </div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  runtime->GetProfiler(netId)->EnableProfiling(<span class="keyword">true</span>);</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span> </div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <span class="comment">// Do the inference</span></div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  runtime->EnqueueWorkload(netId, inputTensors, outputTensors);</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span> </div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  <span class="comment">// Retrieve the Profiler.Print() output to get the workload execution</span></div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  <a class="code" href="classarmnn_1_1_profiler_manager.xhtml">ProfilerManager</a>& profilerManager = <a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a>();</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  std::stringstream ss;</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  profilerManager.<a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">GetProfiler</a>()-><a class="code" href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">Print</a>(ss);;</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  std::string dump = ss.str();</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span> </div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <span class="comment">// Contains CopyMemGeneric between the backends</span></div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  std::size_t found = dump.find(<span class="stringliteral">"CopyMemGeneric"</span>);</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span> </div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="comment">// Contains SyncMemGeneric for the output</span></div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  found = dump.find(<span class="stringliteral">"SyncMemGeneric"</span>);</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span> </div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <span class="comment">// Does not contain ImportMemGeneric</span></div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  found = dump.find(<span class="stringliteral">"ImportMemGeneric"</span>);</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  BOOST_TEST(found == std::string::npos);</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span> </div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="comment">// Use memory import between backends</span></div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  BOOST_TEST((layer3-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">LayerType::MemCopy</a>));</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span> </div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <span class="comment">// Check output is as expected</span></div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  BOOST_TEST(outputData == expectedOutput);</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span> }</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span> </div><div class="line"><a name="l00549"></a><span class="lineno"><a class="line" href="_neon_fallback_tests_8cpp.xhtml#af1c239387f7cbdbfd173126ab8b5b24f"> 549</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FallbackDisableImportFromCpuAcc)</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span> {</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span> </div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <span class="comment">// Create a mock backend object</span></div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <a class="code" href="classarmnn_1_1_mock_import_backend_initialiser.xhtml">MockImportBackendInitialiser</a> initialiser; <span class="comment">// Register the Mock Backend</span></div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  <span class="keyword">auto</span> backendObjPtr = <a class="code" href="_common_test_utils_8cpp.xhtml#a7a4090354279f08b1e27244bab25aa86">CreateBackendObject</a>(<a class="code" href="namespacearmnn.xhtml#ac38529227e1adeef300d31625826382b">MockImportBackendId</a>());</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  BOOST_TEST((backendObjPtr != <span class="keyword">nullptr</span>));</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span> </div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <a class="code" href="namespacearmnn.xhtml#a1854d9cda81304325664363c1fd0fb27">BackendIdSet</a> backendIds = <a class="code" href="namespacearmnn.xhtml#ac2807505b850738bc8a1991ce669dd47">BackendRegistryInstance</a>().<a class="code" href="classarmnn_1_1_backend_registry.xhtml#a570cb1835ec73000a7954ba75257904f">GetBackendIds</a>();</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <span class="keywordflow">if</span> (backendIds.find(<span class="stringliteral">"MockRef"</span>) == backendIds.end())</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  {</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  std::string message = <span class="stringliteral">"Cannot load MockRef"</span>;</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  BOOST_FAIL(message);</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  }</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span> </div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <span class="comment">// Create runtime in which test will run and allow fallback to CpuRef.</span></div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span> </div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span> </div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = net->AddInputLayer(0, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = net->AddInputLayer(1, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input2 = net->AddInputLayer(2, <span class="stringliteral">"input2"</span>);</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* sub = net->AddSubtractionLayer(<span class="stringliteral">"sub"</span>);</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* add = net->AddAdditionLayer(<span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</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="l00578"></a><span class="lineno"> 578</span> </div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  input0-><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>(sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  input1-><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>(sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  input2-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  sub-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  add-><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>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span> </div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 3, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span> </div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  input0-><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>(info);</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  input1-><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>(info);</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  input2-><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>(info);</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  sub-><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>(info);</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  add-><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>(info);</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span> </div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <span class="comment">// optimize the network</span></div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  std::vector<BackendId> backends = { <span class="stringliteral">"MockRef"</span>, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a> };</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec());</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span> </div><div class="line"><a name="l00597"></a><span class="lineno"> 597</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="l00598"></a><span class="lineno"> 598</span> </div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer0 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer1 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer2 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input2"</span>);</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer3 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"sub"</span>);</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer4 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"[ sub (0) -> add (1) ]"</span>);</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer5 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer6 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span> </div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <span class="comment">// Checks order is valid.</span></div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer0, layer1));</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer1, layer2));</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer2, layer3));</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer3, layer4));</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer4, layer5));</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer5, layer6));</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span> </div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <span class="comment">// Load it into the runtime. It should pass.</span></div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  std::string ignoredErrorMessage;</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">false</span>, <span class="keyword">false</span>);</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span> </div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span> </div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  <span class="comment">// Creates structures for input & output</span></div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  std::vector<float> inputData0</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  {</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 0.0f</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  };</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  std::vector<float> inputData1</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  {</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  };</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  std::vector<float> inputData2</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  {</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  };</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span> </div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  std::vector<float> outputData(12);</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span> </div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  std::vector<float> expectedOutput</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  {</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  13.0f, 11.0f, 11.0f, 9.0f, 7.0f, 7.0f, 7.0f, 5.0f, 5.0f, 3.0f, 3.0f, -5.0f</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  };</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span> </div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  {</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  { 0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) },</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  { 1, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) },</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  { 2, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) }</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  };</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  {</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  { 0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) }</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  };</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span> </div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  runtime->GetProfiler(netId)->EnableProfiling(<span class="keyword">true</span>);</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span> </div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  <span class="comment">// Do the inference</span></div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  runtime->EnqueueWorkload(netId, inputTensors, outputTensors);</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span> </div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <span class="comment">// Retrieve the Profiler.Print() output to get the workload execution</span></div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <a class="code" href="classarmnn_1_1_profiler_manager.xhtml">ProfilerManager</a>& profilerManager = <a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a>();</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  std::stringstream ss;</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  profilerManager.<a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">GetProfiler</a>()-><a class="code" href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">Print</a>(ss);;</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  std::string dump = ss.str();</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span> </div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <span class="comment">// Contains CopyMemGeneric between the backends</span></div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  std::size_t found = dump.find(<span class="stringliteral">"CopyMemGeneric"</span>);</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span> </div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  <span class="comment">// Does not contain ImportMemGeneric</span></div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  found = dump.find(<span class="stringliteral">"ImportMemGeneric"</span>);</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  BOOST_TEST(found == std::string::npos);</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span> </div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <span class="comment">// Use memory import between backends</span></div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  BOOST_TEST((layer4-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">LayerType::MemCopy</a>));</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span> </div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  <span class="comment">// Check output is as expected</span></div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  BOOST_TEST(outputData == expectedOutput);</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span> }</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span> </div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span> <span class="preprocessor">#if defined(ARMCOMPUTECL_ENABLED)</span></div><div class="line"><a name="l00681"></a><span class="lineno"><a class="line" href="_neon_fallback_tests_8cpp.xhtml#a488c0cd8e420b5e6f0d5d27e5dc05773"> 681</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(NeonImportEnabledFallbackToCl)</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span> {</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span> </div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span> </div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span> </div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = net->AddInputLayer(0, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = net->AddInputLayer(1, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input2 = net->AddInputLayer(2, <span class="stringliteral">"input2"</span>);</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* add = net->AddAdditionLayer(<span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* sub = net->AddSubtractionLayer(<span class="stringliteral">"sub"</span>);</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</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="l00697"></a><span class="lineno"> 697</span> </div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  input0-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  input1-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  input2-><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>(sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  add-><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>(sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  sub-><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>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span> </div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 3, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span> </div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  input0-><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>(info);</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  input1-><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>(info);</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  input2-><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>(info);</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  add-><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>(info);</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  sub-><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>(info);</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span> </div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  std::vector<BackendId> backends = { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a>, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">Compute::GpuAcc</a> };</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  <span class="comment">// Use BackendSelectionHint to specify GpuAcc for Subtraction layer</span></div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a833170f92e96b3ef414b6cf6e5720d2b">BackendSelectionHint</a>(backends[1]);</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span> </div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  <span class="comment">// optimize the network</span></div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  <a class="code" href="structarmnn_1_1_optimizer_options.xhtml">OptimizerOptions</a> optOptions;</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  optOptions.<a class="code" href="structarmnn_1_1_optimizer_options.xhtml#a05c1bba6ba3ecc1339d4c4c10c0d8890">m_ImportEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec(), optOptions);</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span> </div><div class="line"><a name="l00721"></a><span class="lineno"> 721</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="l00722"></a><span class="lineno"> 722</span> </div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer0 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer1 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer2 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input2"</span>);</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer3 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer4 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"[ add (0) -> sub (1) ]"</span>);</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer5 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"sub"</span>);</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer6 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span> </div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  <span class="comment">// Checks order is valid.</span></div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer0, layer1));</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer1, layer2));</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer2, layer3));</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer3, layer4));</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer4, layer5));</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer5, layer6));</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span> </div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  <span class="comment">// Use memory import between backends</span></div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  BOOST_TEST((layer4-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">LayerType::MemCopy</a>));</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span> </div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  <span class="comment">// Correctly use backend hint</span></div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  BOOST_TEST((layer5-><a class="code" href="classarmnn_1_1_layer.xhtml#afdb1d37740e7a083b625d669588b6a0e">GetBackendId</a>() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">Compute::GpuAcc</a> ));</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span> </div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  <span class="comment">// Load it into the runtime. It should pass.</span></div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  std::string ignoredErrorMessage;</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">true</span>, <span class="keyword">true</span>);</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span> </div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span> </div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  <span class="comment">// Creates structures for input & output</span></div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  std::vector<float> inputData0</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  {</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  };</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  std::vector<float> inputData1</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  {</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  };</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  std::vector<float> inputData2</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  {</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  };</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span> </div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  std::vector<float> outputData(12);</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span> </div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  std::vector<float> expectedOutput</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  {</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  11.0f, 9.0f, 7.0f, 5.0f, 3.0f, 1.0f, -1.0f, -3.0f, -5.0f, -7.0f, -9.0f, -11.0f</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  };</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span> </div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  {</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  { 0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) },</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  { 1, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) },</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  { 2, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) }</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  };</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  {</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  { 0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) }</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  };</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span> </div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  runtime->GetProfiler(netId)->EnableProfiling(<span class="keyword">true</span>);</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span> </div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  <span class="comment">// Do the inference</span></div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  runtime->EnqueueWorkload(netId, inputTensors, outputTensors);</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span> </div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  <span class="comment">// Retrieve the Profiler.Print() output to get the workload execution</span></div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  <a class="code" href="classarmnn_1_1_profiler_manager.xhtml">ProfilerManager</a>& profilerManager = <a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a>();</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  std::stringstream ss;</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  profilerManager.<a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">GetProfiler</a>()-><a class="code" href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">Print</a>(ss);;</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  std::string dump = ss.str();</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span> </div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  <span class="comment">// Executed Subtraction using GpuAcc</span></div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  std::size_t found = dump.find(<span class="stringliteral">"ClSubtractionWorkload_Execute"</span>);</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span> </div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  <span class="comment">// Contain CopyMemGeneric</span></div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  found = dump.find(<span class="stringliteral">"CopyMemGeneric"</span>);</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span> </div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  <span class="comment">// Check output is as expected</span></div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  BOOST_TEST(outputData == expectedOutput);</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span> }</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span> </div><div class="line"><a name="l00807"></a><span class="lineno"><a class="line" href="_neon_fallback_tests_8cpp.xhtml#a4ff167001b91b636bb12692d0d12f408"> 807</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(NeonImportDisabledFallbackToCl)</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span> {</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span> </div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span> </div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span> </div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = net->AddInputLayer(0, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = net->AddInputLayer(1, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input2 = net->AddInputLayer(2, <span class="stringliteral">"input2"</span>);</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* add = net->AddAdditionLayer(<span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* sub = net->AddSubtractionLayer(<span class="stringliteral">"sub"</span>);</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</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="l00823"></a><span class="lineno"> 823</span> </div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  input0-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  input1-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  input2-><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>(sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  add-><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>(sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  sub-><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>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span> </div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 3, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span> </div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  input0-><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>(info);</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  input1-><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>(info);</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  input2-><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>(info);</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  add-><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>(info);</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  sub-><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>(info);</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span> </div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  std::vector<BackendId> backends = { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a>, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">Compute::GpuAcc</a> };</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  <span class="comment">// Use BackendSelectionHint to specify GpuAcc for Subtraction layer</span></div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a833170f92e96b3ef414b6cf6e5720d2b">BackendSelectionHint</a>(backends[1]);</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span> </div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  <span class="comment">// optimize the network</span></div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  <a class="code" href="structarmnn_1_1_optimizer_options.xhtml">OptimizerOptions</a> optOptions;</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec(), optOptions);</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span> </div><div class="line"><a name="l00846"></a><span class="lineno"> 846</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="l00847"></a><span class="lineno"> 847</span> </div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer0 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer1 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer2 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input2"</span>);</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer3 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer4 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"[ add (0) -> sub (1) ]"</span>);</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer5 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"sub"</span>);</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer6 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span> </div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  <span class="comment">// Checks order is valid.</span></div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer0, layer1));</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer1, layer2));</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer2, layer3));</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer3, layer4));</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer4, layer5));</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer5, layer6));</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span> </div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  <span class="comment">// Use memory import between backends</span></div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  BOOST_TEST((layer4-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">LayerType::MemCopy</a>));</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span> </div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  <span class="comment">// Correctly use backend hint</span></div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  BOOST_TEST((layer5-><a class="code" href="classarmnn_1_1_layer.xhtml#afdb1d37740e7a083b625d669588b6a0e">GetBackendId</a>() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">Compute::GpuAcc</a> ));</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span> </div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  <span class="comment">// Load it into the runtime. It should pass.</span></div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  runtime->LoadNetwork(netId, std::move(optNet));</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span> </div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  <span class="comment">// Creates structures for input & output</span></div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  std::vector<float> inputData0</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  {</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  };</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  std::vector<float> inputData1</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  {</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  };</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  std::vector<float> inputData2</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  {</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  };</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span> </div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  std::vector<float> outputData(12);</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span> </div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>  std::vector<float> expectedOutput</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  {</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  11.0f, 9.0f, 7.0f, 5.0f, 3.0f, 1.0f, -1.0f, -3.0f, -5.0f, -7.0f, -9.0f, -11.0f</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  };</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span> </div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  {</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  { 0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) },</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  { 1, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) },</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  { 2, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) }</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  };</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  {</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  { 0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) }</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  };</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span> </div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  runtime->GetProfiler(netId)->EnableProfiling(<span class="keyword">true</span>);</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span> </div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  <span class="comment">// Do the inference</span></div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  runtime->EnqueueWorkload(netId, inputTensors, outputTensors);</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span> </div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  <span class="comment">// Retrieve the Profiler.Print() output to get the workload execution</span></div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  <a class="code" href="classarmnn_1_1_profiler_manager.xhtml">ProfilerManager</a>& profilerManager = <a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a>();</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  std::stringstream ss;</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  profilerManager.<a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">GetProfiler</a>()-><a class="code" href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">Print</a>(ss);;</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span>  std::string dump = ss.str();</div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span> </div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  <span class="comment">// Executed Subtraction using GpuAcc</span></div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  std::size_t found = dump.find(<span class="stringliteral">"ClSubtractionWorkload_Execute"</span>);</div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span> </div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  <span class="comment">// Contain CopyMemGeneric</span></div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  found = dump.find(<span class="stringliteral">"CopyMemGeneric"</span>);</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span> </div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  <span class="comment">// Check output is as expected</span></div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  BOOST_TEST(outputData == expectedOutput);</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span> }</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span> </div><div class="line"><a name="l00929"></a><span class="lineno"><a class="line" href="_neon_fallback_tests_8cpp.xhtml#a08e845ad78d87d2bb781823ceb1e5ad2"> 929</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(NeonImportEnabledFallbackSubgraphToCl)</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span> {</div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span> </div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span> </div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span> </div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> desc;</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span> </div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = net->AddInputLayer(0, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = net->AddInputLayer(1, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input2 = net->AddInputLayer(2, <span class="stringliteral">"input2"</span>);</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* add = net->AddAdditionLayer(<span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* sub = net->AddSubtractionLayer(<span class="stringliteral">"sub"</span>);</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* pooling = net->AddPooling2dLayer(desc, <span class="stringliteral">"pooling"</span>);</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</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="l00948"></a><span class="lineno"> 948</span> </div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  input0-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  input1-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  input2-><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>(sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  add-><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>(sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  sub-><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>(pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  pooling-><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>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span> </div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 3, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> poolingInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 1, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span> </div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  input0-><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>(info);</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  input1-><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>(info);</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  input2-><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>(info);</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  add-><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>(info);</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  sub-><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>(info);</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  pooling-><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>(poolingInfo);</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span> </div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  std::vector<BackendId> backends = { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a>, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">Compute::GpuAcc</a> };</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  <span class="comment">// Use BackendSelectionHint to specify GpuAcc for Subtraction layer</span></div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a833170f92e96b3ef414b6cf6e5720d2b">BackendSelectionHint</a>(backends[1]);</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span> </div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  <span class="comment">// optimize the network</span></div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  <a class="code" href="structarmnn_1_1_optimizer_options.xhtml">OptimizerOptions</a> optOptions;</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  optOptions.<a class="code" href="structarmnn_1_1_optimizer_options.xhtml#a05c1bba6ba3ecc1339d4c4c10c0d8890">m_ImportEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec(), optOptions);</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span> </div><div class="line"><a name="l00975"></a><span class="lineno"> 975</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="l00976"></a><span class="lineno"> 976</span> </div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer0 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer1 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer2 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input2"</span>);</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer3 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer4 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"[ add (0) -> sub (1) ]"</span>);</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer5 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"sub"</span>);</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer6 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"[ sub (0) -> pooling (0) ]"</span>);</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer7 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"pooling"</span>);</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer8 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span> </div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  <span class="comment">// Checks order is valid.</span></div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer0, layer1));</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer1, layer2));</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer2, layer3));</div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer3, layer4));</div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer4, layer5));</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer5, layer6));</div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer6, layer7));</div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer7, layer8));</div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span> </div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  <span class="comment">// Use memory import between backends</span></div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  BOOST_TEST((layer4-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">LayerType::MemCopy</a>));</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  BOOST_TEST((layer6-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">LayerType::MemCopy</a>));</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span> </div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  <span class="comment">// Correctly use backend hint</span></div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  BOOST_TEST((layer5-><a class="code" href="classarmnn_1_1_layer.xhtml#afdb1d37740e7a083b625d669588b6a0e">GetBackendId</a>() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">Compute::GpuAcc</a> ));</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span> </div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>  <span class="comment">// Load it into the runtime. It should pass.</span></div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  std::string ignoredErrorMessage;</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  <a class="code" href="structarmnn_1_1_i_network_properties.xhtml">INetworkProperties</a> networkProperties(<span class="keyword">true</span>, <span class="keyword">true</span>);</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span> </div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span> </div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  <span class="comment">// Creates structures for input & output</span></div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  std::vector<float> inputData0</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  {</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  };</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  std::vector<float> inputData1</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  {</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>  0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>  };</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  std::vector<float> inputData2</div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>  {</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  };</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span> </div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>  std::vector<float> outputData(2);</div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span> </div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  std::vector<float> expectedOutput{ 11.0f, -1.0f };</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span> </div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  {</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  { 0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) },</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>  { 1, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) },</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>  { 2, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) }</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>  };</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  {</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>  { 0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) }</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>  };</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span> </div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>  runtime->GetProfiler(netId)->EnableProfiling(<span class="keyword">true</span>);</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span> </div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  <span class="comment">// Do the inference</span></div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  runtime->EnqueueWorkload(netId, inputTensors, outputTensors);</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span> </div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>  <span class="comment">// Retrieve the Profiler.Print() output to get the workload execution</span></div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>  <a class="code" href="classarmnn_1_1_profiler_manager.xhtml">ProfilerManager</a>& profilerManager = <a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a>();</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  std::stringstream ss;</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>  profilerManager.<a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">GetProfiler</a>()-><a class="code" href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">Print</a>(ss);;</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>  std::string dump = ss.str();</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span> </div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  <span class="comment">// Executed Subtraction using GpuAcc</span></div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>  std::size_t found = dump.find(<span class="stringliteral">"ClSubtractionWorkload_Execute"</span>);</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span> </div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  <span class="comment">// Correctly switch back to CpuAcc</span></div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>  found = dump.find(<span class="stringliteral">"NeonPooling2dWorkload_Execute"</span>);</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span> </div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>  <span class="comment">// Contain CopyMemGeneric</span></div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  found = dump.find(<span class="stringliteral">"CopyMemGeneric"</span>);</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span> </div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>  <span class="comment">// Contains SyncMemGeneric for output</span></div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  found = dump.find(<span class="stringliteral">"SyncMemGeneric"</span>);</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span> </div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>  <span class="comment">// Check output is as expected</span></div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>  BOOST_TEST(outputData == expectedOutput);</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span> }</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span> </div><div class="line"><a name="l01071"></a><span class="lineno"><a class="line" href="_neon_fallback_tests_8cpp.xhtml#acea91581a4070c23df3b95c34c820262"> 1071</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(NeonImportDisableFallbackSubgraphToCl)</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span> {</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span> </div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a> options;</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">IRuntime::Create</a>(options));</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span> </div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>  <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>());</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span> </div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> desc;</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span> </div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input0 = net->AddInputLayer(0, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input1 = net->AddInputLayer(1, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input2 = net->AddInputLayer(2, <span class="stringliteral">"input2"</span>);</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* add = net->AddAdditionLayer(<span class="stringliteral">"add"</span>);</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* sub = net->AddSubtractionLayer(<span class="stringliteral">"sub"</span>);</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* pooling = net->AddPooling2dLayer(desc, <span class="stringliteral">"pooling"</span>);</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</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="l01090"></a><span class="lineno"> 1090</span> </div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  input0-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  input1-><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>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>  input2-><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>(sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  add-><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>(sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  sub-><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>(pooling-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  pooling-><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>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span> </div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 3, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> poolingInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 1, 2, 1, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span> </div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>  input0-><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>(info);</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>  input1-><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>(info);</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>  input2-><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>(info);</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  add-><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>(info);</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>  sub-><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>(info);</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>  pooling-><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>(poolingInfo);</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span> </div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>  std::vector<BackendId> backends = { <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">Compute::CpuAcc</a>, <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">Compute::GpuAcc</a> };</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  <span class="comment">// Use BackendSelectionHint to specify GpuAcc for Subtraction layer</span></div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  sub-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a833170f92e96b3ef414b6cf6e5720d2b">BackendSelectionHint</a>(backends[1]);</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span> </div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  <span class="comment">// optimize the network</span></div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>  <a class="code" href="structarmnn_1_1_optimizer_options.xhtml">OptimizerOptions</a> optOptions;</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*net, backends, runtime->GetDeviceSpec(), optOptions);</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span> </div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</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="l01117"></a><span class="lineno"> 1117</span> </div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer0 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer1 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer2 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"input2"</span>);</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer3 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"add"</span>);</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer4 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"[ add (0) -> sub (1) ]"</span>);</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer5 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"sub"</span>);</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer6 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"[ sub (0) -> pooling (0) ]"</span>);</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer7 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"pooling"</span>);</div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer8 = <a class="code" href="_graph_utils_8cpp.xhtml#a5f17e02e0054dac0a691685a0464ed36">GetFirstLayerWithName</a>(graph, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span> </div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  <span class="comment">// Checks order is valid.</span></div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer0, layer1));</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer1, layer2));</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer2, layer3));</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer3, layer4));</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer4, layer5));</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer5, layer6));</div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer6, layer7));</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  BOOST_TEST(<a class="code" href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a>(graph, layer7, layer8));</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span> </div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>  <span class="comment">// Use memory import between backends</span></div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>  BOOST_TEST((layer4-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">LayerType::MemCopy</a>));</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  BOOST_TEST((layer6-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">LayerType::MemCopy</a>));</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span> </div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>  <span class="comment">// Correctly use backend hint</span></div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  BOOST_TEST((layer5-><a class="code" href="classarmnn_1_1_layer.xhtml#afdb1d37740e7a083b625d669588b6a0e">GetBackendId</a>() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">Compute::GpuAcc</a> ));</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span> </div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  <span class="comment">// Load it into the runtime. It should pass.</span></div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> netId;</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  runtime->LoadNetwork(netId, std::move(optNet));</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span> </div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  <span class="comment">// Creates structures for input & output</span></div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>  std::vector<float> inputData0</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>  {</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  };</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  std::vector<float> inputData1</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  {</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  };</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  std::vector<float> inputData2</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  {</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>  12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  };</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span> </div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>  std::vector<float> outputData(2);</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span> </div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>  std::vector<float> expectedOutput{ 11.0f, -1.0f };</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span> </div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensors</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  {</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  { 0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) },</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  { 1, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) },</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  { 2, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) }</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  };</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensors</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  {</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  { 0,<a class="code" href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a>(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) }</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  };</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span> </div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  runtime->GetProfiler(netId)->EnableProfiling(<span class="keyword">true</span>);</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span> </div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  <span class="comment">// Do the inference</span></div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  runtime->EnqueueWorkload(netId, inputTensors, outputTensors);</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span> </div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  <span class="comment">// Retrieve the Profiler.Print() output to get the workload execution</span></div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  <a class="code" href="classarmnn_1_1_profiler_manager.xhtml">ProfilerManager</a>& profilerManager = <a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a>();</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  std::stringstream ss;</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>  profilerManager.<a class="code" href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">GetProfiler</a>()-><a class="code" href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">Print</a>(ss);;</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  std::string dump = ss.str();</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span> </div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>  <span class="comment">// Executed Subtraction using GpuAcc</span></div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  std::size_t found = dump.find(<span class="stringliteral">"ClSubtractionWorkload_Execute"</span>);</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span> </div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  <span class="comment">// Correctly switch back to CpuAcc</span></div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  found = dump.find(<span class="stringliteral">"NeonPooling2dWorkload_Execute"</span>);</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span> </div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  <span class="comment">// Contain CopyMemGeneric</span></div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  found = dump.find(<span class="stringliteral">"CopyMemGeneric"</span>);</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  BOOST_TEST(found != std::string::npos);</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span> </div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  <span class="comment">// Check output is as expected</span></div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  BOOST_TEST(outputData == expectedOutput);</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span> }</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span> </div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span> <a class="code" href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a>()</div><div class="ttc" id="_output_shape_of_squeeze_8cpp_xhtml_ae3a6cb217a792718f2bd0e8f45e3ca9e"><div class="ttname"><a href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE(TensorflowLiteParser)</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#l00037">Runtime.cpp:37</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#l00062">INetwork.hpp:62</a></div></div>
<div class="ttc" id="classarmnn_1_1_backend_registry_xhtml_a570cb1835ec73000a7954ba75257904f"><div class="ttname"><a href="classarmnn_1_1_backend_registry.xhtml#a570cb1835ec73000a7954ba75257904f">armnn::BackendRegistry::GetBackendIds</a></div><div class="ttdeci">BackendIdSet GetBackendIds() const</div><div class="ttdef"><b>Definition:</b> <a href="_backend_registry_8cpp_source.xhtml#l00072">BackendRegistry.cpp:72</a></div></div>
<div class="ttc" id="classarmnn_1_1_mock_import_backend_initialiser_xhtml"><div class="ttname"><a href="classarmnn_1_1_mock_import_backend_initialiser.xhtml">armnn::MockImportBackendInitialiser</a></div><div class="ttdef"><b>Definition:</b> <a href="_mock_import_backend_8hpp_source.xhtml#l00014">MockImportBackend.hpp:14</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a1854d9cda81304325664363c1fd0fb27"><div class="ttname"><a href="namespacearmnn.xhtml#a1854d9cda81304325664363c1fd0fb27">armnn::BackendIdSet</a></div><div class="ttdeci">std::unordered_set< BackendId > BackendIdSet</div><div class="ttdef"><b>Definition:</b> <a href="_backend_id_8hpp_source.xhtml#l00191">BackendId.hpp:191</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_profiler_manager_xhtml_a93857080c2523bf3395e7aa7e6024d5c"><div class="ttname"><a href="classarmnn_1_1_profiler_manager.xhtml#a93857080c2523bf3395e7aa7e6024d5c">armnn::ProfilerManager::GetInstance</a></div><div class="ttdeci">static ProfilerManager & GetInstance()</div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8cpp_source.xhtml#l00489">Profiling.cpp:489</a></div></div>
<div class="ttc" id="_graph_utils_8cpp_xhtml_a21d963c71be62057ed99b5007e7bbbfd"><div class="ttname"><a href="_graph_utils_8cpp.xhtml#a21d963c71be62057ed99b5007e7bbbfd">CheckOrder</a></div><div class="ttdeci">bool CheckOrder(const armnn::Graph &graph, const armnn::Layer *first, const armnn::Layer *second)</div><div class="ttdoc">Checks that first comes before second in the order. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00068">GraphUtils.cpp:68</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a833170f92e96b3ef414b6cf6e5720d2b"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a833170f92e96b3ef414b6cf6e5720d2b">armnn::IConnectableLayer::BackendSelectionHint</a></div><div class="ttdeci">virtual void BackendSelectionHint(Optional< BackendId > backend)=0</div><div class="ttdoc">Provide a hint for the optimizer as to which backend to prefer for this layer. </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="_neon_fallback_tests_8cpp_xhtml_aa58a47ff11eeb9ea65568f1197fb83d4"><div class="ttname"><a href="_neon_fallback_tests_8cpp.xhtml#aa58a47ff11eeb9ea65568f1197fb83d4">BOOST_AUTO_TEST_CASE</a></div><div class="ttdeci">BOOST_AUTO_TEST_CASE(FallbackImportToCpuAcc)</div><div class="ttdef"><b>Definition:</b> <a href="_neon_fallback_tests_8cpp_source.xhtml#l00015">NeonFallbackTests.cpp:15</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#l00026">IRuntime.hpp:26</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_profiler_xhtml_a038bb767bbc6abc0ee0d9b509616b896"><div class="ttname"><a href="classarmnn_1_1_i_profiler.xhtml#a038bb767bbc6abc0ee0d9b509616b896">armnn::IProfiler::Print</a></div><div class="ttdeci">void Print(std::ostream &outStream) const</div><div class="ttdoc">Print stats for events in JSON Format to the given output stream. </div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8cpp_source.xhtml#l00522">Profiling.cpp:522</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ac2807505b850738bc8a1991ce669dd47"><div class="ttname"><a href="namespacearmnn.xhtml#ac2807505b850738bc8a1991ce669dd47">armnn::BackendRegistryInstance</a></div><div class="ttdeci">BackendRegistry & BackendRegistryInstance()</div><div class="ttdef"><b>Definition:</b> <a href="_backend_registry_8cpp_source.xhtml#l00013">BackendRegistry.cpp:13</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#l00340">Tensor.hpp:340</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a83015160d8c67d5d77735eb0d4033d9a"><div class="ttname"><a href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">armnn::NetworkId</a></div><div class="ttdeci">int NetworkId</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00020">IRuntime.hpp:20</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__software__tools_8dox_source.xhtml#l00006">01_00_software_tools.dox:6</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="namespacearmnn_xhtml_ac38529227e1adeef300d31625826382b"><div class="ttname"><a href="namespacearmnn.xhtml#ac38529227e1adeef300d31625826382b">armnn::MockImportBackendId</a></div><div class="ttdeci">constexpr const char * MockImportBackendId()</div><div class="ttdef"><b>Definition:</b> <a href="_mock_import_backend_8hpp_source.xhtml#l00012">MockImportBackend.hpp:12</a></div></div>
<div class="ttc" id="structarmnn_1_1_i_network_properties_xhtml"><div class="ttname"><a href="structarmnn_1_1_i_network_properties.xhtml">armnn::INetworkProperties</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00028">IRuntime.hpp:28</a></div></div>
<div class="ttc" id="classarmnn_1_1_profiler_manager_xhtml_a3756986bc88b9b212d3f983c70c5c129"><div class="ttname"><a href="classarmnn_1_1_profiler_manager.xhtml#a3756986bc88b9b212d3f983c70c5c129">armnn::ProfilerManager::GetProfiler</a></div><div class="ttdeci">IProfiler * GetProfiler()</div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8cpp_source.xhtml#l00501">Profiling.cpp:501</a></div></div>
<div class="ttc" id="_common_test_utils_8hpp_xhtml"><div class="ttname"><a href="_common_test_utils_8hpp.xhtml">CommonTestUtils.hpp</a></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#l00306">Tensor.hpp:306</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#l01502">Network.cpp:1502</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#l00314">Tensor.hpp:314</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#l00341">Tensor.hpp:341</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_ad8e15c530c929ab823d89ae9fd2d3f11"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">armnn::Layer::GetType</a></div><div class="ttdeci">LayerType GetType() const override</div><div class="ttdoc">Returns the armnn::LayerType of this layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00265">Layer.hpp:265</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">armnn::LayerType::MemCopy</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#l00174">INetwork.hpp:174</a></div></div>
<div class="ttc" id="classarmnn_1_1_profiler_manager_xhtml"><div class="ttname"><a href="classarmnn_1_1_profiler_manager.xhtml">armnn::ProfilerManager</a></div><div class="ttdef"><b>Definition:</b> <a href="_profiling_8hpp_source.xhtml#l00091">Profiling.hpp:91</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="_graph_utils_8hpp_xhtml"><div class="ttname"><a href="_graph_utils_8hpp.xhtml">GraphUtils.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a10d15f3df1ab52b3b915a4be1dbf386b"><div class="ttname"><a href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">armnn::BOOST_AUTO_TEST_CASE</a></div><div class="ttdeci">BOOST_AUTO_TEST_CASE(CheckConvolution2dLayer)</div><div class="ttdef"><b>Definition:</b> <a href="_const_tensor_layer_visitor_8cpp_source.xhtml#l00268">ConstTensorLayerVisitor.cpp:268</a></div></div>
<div class="ttc" id="structarmnn_1_1_optimizer_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_optimizer_options.xhtml">armnn::OptimizerOptions</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00110">INetwork.hpp:110</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_afdb1d37740e7a083b625d669588b6a0e"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#afdb1d37740e7a083b625d669588b6a0e">armnn::Layer::GetBackendId</a></div><div class="ttdeci">const BackendId & GetBackendId() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00269">Layer.hpp:269</a></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#l00029">Graph.hpp:29</a></div></div>
<div class="ttc" id="structarmnn_1_1_optimizer_options_xhtml_a05c1bba6ba3ecc1339d4c4c10c0d8890"><div class="ttname"><a href="structarmnn_1_1_optimizer_options.xhtml#a05c1bba6ba3ecc1339d4c4c10c0d8890">armnn::OptimizerOptions::m_ImportEnabled</a></div><div class="ttdeci">bool m_ImportEnabled</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00165">INetwork.hpp:165</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#l00043">IRuntime.hpp:43</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#l00025">TestUtils.cpp:25</a></div></div>
<div class="ttc" id="_profiler_tests_8cpp_xhtml_af7f71af5c6c124222dd1c42c5df892f4"><div class="ttname"><a href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE_END()</div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a115bfc5d633eae55c67f9588acdd2bf9"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a115bfc5d633eae55c67f9588acdd2bf9">armnn::LayerType::MemImport</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</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="_mock_import_backend_8hpp_xhtml"><div class="ttname"><a href="_mock_import_backend_8hpp.xhtml">MockImportBackend.hpp</a></div></div>
<div class="ttc" id="_common_test_utils_8cpp_xhtml_a7a4090354279f08b1e27244bab25aa86"><div class="ttname"><a href="_common_test_utils_8cpp.xhtml#a7a4090354279f08b1e27244bab25aa86">CreateBackendObject</a></div><div class="ttdeci">armnn::IBackendInternalUniquePtr CreateBackendObject(const armnn::BackendId &backendId)</div><div class="ttdef"><b>Definition:</b> <a href="_common_test_utils_8cpp_source.xhtml#l00045">CommonTestUtils.cpp:45</a></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="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_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#l00173">INetwork.hpp:173</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="structarmnn_1_1_pooling2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a></div><div class="ttdoc">A Pooling2dDescriptor for the Pooling2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00329">Descriptors.hpp:329</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#l00510">Network.cpp:510</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#l00210">Layer.hpp:210</a></div></div>
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