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<div class="title">OptimizerTests.cpp</div> </div>
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<a href="_optimizer_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 © 2017 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="_test_utils_8hpp.xhtml">TestUtils.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_backend_settings_8hpp.xhtml">BackendSettings.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_graph_8hpp.xhtml">Graph.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_network_8hpp.xhtml">Network.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_optimizer_8hpp.xhtml">Optimizer.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_backend_registry_8hpp.xhtml">armnn/BackendRegistry.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <<a class="code" href="_layer_visitor_base_8hpp.xhtml">armnn/LayerVisitorBase.hpp</a>></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <<a class="code" href="_floating_point_converter_8hpp.xhtml">armnnUtils/FloatingPointConverter.hpp</a>></span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <<a class="code" href="_polymorphic_downcast_8hpp.xhtml">armnn/utility/PolymorphicDowncast.hpp</a>></span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> </div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include <<a class="code" href="_cpu_tensor_handle_8hpp.xhtml">backendsCommon/CpuTensorHandle.hpp</a>></span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include <<a class="code" href="src_2backends_2backends_common_2_i_backend_internal_8hpp.xhtml">backendsCommon/IBackendInternal.hpp</a>></span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="preprocessor">#include <<a class="code" href="_layer_support_base_8hpp.xhtml">backendsCommon/LayerSupportBase.hpp</a>></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> <span class="preprocessor">#include <boost/test/unit_test.hpp></span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> </div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="keyword">namespace</span></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="keywordtype">void</span> CreateLSTMLayerHelper(<a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> &graph, <span class="keywordtype">bool</span> CifgEnabled)</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> {</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">LstmDescriptor</a> layerDesc;</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  layerDesc.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> = 4;</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  layerDesc.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 0.2f;</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  layerDesc.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a> = 0.4f;</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  layerDesc.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = CifgEnabled;</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  layerDesc.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  layerDesc.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <a class="code" href="classarmnn_1_1_lstm_layer.xhtml">LstmLayer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_lstm_layer.xhtml">LstmLayer</a>>(layerDesc, <span class="stringliteral">"layer"</span>);</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 3;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 2;</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numUnits = 4;</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 4;</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a5a0d8af26a6aad1e5be521ea7dc550eb">m_InputToForgetWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a14ab2bc78421c417c4f97a65b0bd78f9">m_InputToCellWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#ae8d897b8d282f25a6eb784c4aaa98df6">m_InputToOutputWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a3d5f129421bbe6479a66d4ed1356bf68">m_RecurrentToForgetWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a6e8c3db3c5474f0760553ff93fbc39e6">m_RecurrentToCellWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a91dda74af4085ae43913746ad817795a">m_RecurrentToOutputWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a0e0e17d5b494993407cb75d614455ddd">m_ForgetGateBias</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a51255889cbc063130a3d691c1781c5d3">m_CellBias</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#aacb55e0992b6781a7bd3225ab6e6bb2f">m_OutputGateBias</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a5a0d8af26a6aad1e5be521ea7dc550eb">m_InputToForgetWeights</a>->Allocate();</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a14ab2bc78421c417c4f97a65b0bd78f9">m_InputToCellWeights</a>->Allocate();</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#ae8d897b8d282f25a6eb784c4aaa98df6">m_InputToOutputWeights</a>->Allocate();</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a3d5f129421bbe6479a66d4ed1356bf68">m_RecurrentToForgetWeights</a>->Allocate();</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a6e8c3db3c5474f0760553ff93fbc39e6">m_RecurrentToCellWeights</a>->Allocate();</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a91dda74af4085ae43913746ad817795a">m_RecurrentToOutputWeights</a>->Allocate();</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a0e0e17d5b494993407cb75d614455ddd">m_ForgetGateBias</a>->Allocate();</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a51255889cbc063130a3d691c1781c5d3">m_CellBias</a>->Allocate();</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#aacb55e0992b6781a7bd3225ab6e6bb2f">m_OutputGateBias</a>->Allocate();</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">if</span> (!layerDesc.m_CifgEnabled)</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  {</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a0e940dfa428f4eb429f8bc0d138b20af">m_CifgParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_cifg_parameters.xhtml#a4d731c5e73638c7cf7e63f65e9f8b550">m_InputToInputWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a0e940dfa428f4eb429f8bc0d138b20af">m_CifgParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_cifg_parameters.xhtml#a6e8971757790a032e5936da7847ba14b">m_RecurrentToInputWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a0e940dfa428f4eb429f8bc0d138b20af">m_CifgParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_cifg_parameters.xhtml#a9945bc99f8a7400c0724117e29cb3abb">m_InputGateBias</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a0e940dfa428f4eb429f8bc0d138b20af">m_CifgParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_cifg_parameters.xhtml#a4d731c5e73638c7cf7e63f65e9f8b550">m_InputToInputWeights</a>->Allocate();</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a0e940dfa428f4eb429f8bc0d138b20af">m_CifgParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_cifg_parameters.xhtml#a6e8971757790a032e5936da7847ba14b">m_RecurrentToInputWeights</a>->Allocate();</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a0e940dfa428f4eb429f8bc0d138b20af">m_CifgParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_cifg_parameters.xhtml#a9945bc99f8a7400c0724117e29cb3abb">m_InputGateBias</a>->Allocate();</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> </div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">if</span> (layerDesc.m_ProjectionEnabled)</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  {</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a3d3e6d0c3e6e570d9f831489c3bd14ce">m_ProjectionParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_projection_parameters.xhtml#a3ec2885c48ce888516e27c8b75a8cb83">m_ProjectionWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ outputSize, numUnits }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a3d3e6d0c3e6e570d9f831489c3bd14ce">m_ProjectionParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_projection_parameters.xhtml#aa9f2880e4e2a1eb731f61c1e0941c6a7">m_ProjectionBias</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a3d3e6d0c3e6e570d9f831489c3bd14ce">m_ProjectionParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_projection_parameters.xhtml#a3ec2885c48ce888516e27c8b75a8cb83">m_ProjectionWeights</a>->Allocate();</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a3d3e6d0c3e6e570d9f831489c3bd14ce">m_ProjectionParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_projection_parameters.xhtml#aa9f2880e4e2a1eb731f61c1e0941c6a7">m_ProjectionBias</a>->Allocate();</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  }</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> </div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keywordflow">if</span> (layerDesc.m_PeepholeEnabled)</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>  <span class="keywordflow">if</span> (!layerDesc.m_CifgEnabled)</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>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a4efa0f4d46817ab94e36c8507c26f276">m_PeepholeParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_peephole_parameters.xhtml#a658f4245732f95c9fe756a934d370ca8">m_CellToInputWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a4efa0f4d46817ab94e36c8507c26f276">m_PeepholeParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_peephole_parameters.xhtml#a658f4245732f95c9fe756a934d370ca8">m_CellToInputWeights</a>->Allocate();</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  }</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a4efa0f4d46817ab94e36c8507c26f276">m_PeepholeParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_peephole_parameters.xhtml#a5d0ebbbb11b727a67877df40b59a628c">m_CellToForgetWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a4efa0f4d46817ab94e36c8507c26f276">m_PeepholeParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_peephole_parameters.xhtml#a310e133b0b51b93a74b83008893792e9">m_CellToOutputWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a4efa0f4d46817ab94e36c8507c26f276">m_PeepholeParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_peephole_parameters.xhtml#a5d0ebbbb11b727a67877df40b59a628c">m_CellToForgetWeights</a>->Allocate();</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a4efa0f4d46817ab94e36c8507c26f276">m_PeepholeParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_peephole_parameters.xhtml#a310e133b0b51b93a74b83008893792e9">m_CellToOutputWeights</a>->Allocate();</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  }</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> </div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="comment">// create input and output layers</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> outputStateIn = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(1, <span class="stringliteral">"outputStateIn"</span>);</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> cellStateIn = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(2, <span class="stringliteral">"cellStateIn"</span>);</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> scratchBuffer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"scratchBuffer"</span>);</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> outputStateOut = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(1, <span class="stringliteral">"outputStateOut"</span>);</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> cellStateOut = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(2, <span class="stringliteral">"cellStateOut"</span>);</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(3, <span class="stringliteral">"output"</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">// connect up</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfo1({ batchSize, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfo2({ batchSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfo3({ batchSize, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfoScratchBuff({ batchSize, numUnits * (layerDesc.m_CifgEnabled ? 3 : 4) },</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, lstmTensorInfo1, 0, 0);</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(cellStateIn, layer, lstmTensorInfo2, 0, 1);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(outputStateIn, layer, lstmTensorInfo3, 0, 2);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, scratchBuffer, lstmTensorInfoScratchBuff, 0, 0);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, outputStateOut, lstmTensorInfo3, 1, 0);</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, cellStateOut, lstmTensorInfo2, 2, 0);</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, lstmTensorInfo3, 3, 0);</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> }</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> </div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> }</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> </div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> <a class="code" href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a>(<a class="code" href="classarmnn_1_1_optimizer.xhtml">Optimizer</a>)</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn_1_1optimizations.xhtml">armnn::optimizations</a>;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#a8839099137f1031b504d76090074142c"> 145</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(LSTMValidateTensorShapesFromInputsCIFGDisabledTest)</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> {</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="comment">//Helper function creates graph containing LSTM layer with required input and output layers</span></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  CreateLSTMLayerHelper(graph, <span class="keyword">false</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"> 152</span>  <span class="comment">//This function used to call ValidateShapesFromInputs();</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  BOOST_CHECK_NO_THROW(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a2ceda8d369e861997d558fac74d79c33">InferTensorInfos</a>());</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> }</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> </div><div class="line"><a name="l00156"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#a10342b734f73496052047f2b74b38cca"> 156</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(LSTMValidateTensorShapesFromInputsCIFGEnabledTest)</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> {</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> </div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="comment">//Helper function creates graph containing LSTM layer with required input and output layers</span></div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  CreateLSTMLayerHelper(graph, <span class="keyword">true</span>);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> </div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="comment">//This function used to call ValidateShapesFromInputs();</span></div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  BOOST_CHECK_NO_THROW(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a2ceda8d369e861997d558fac74d79c33">InferTensorInfos</a>());</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> }</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> </div><div class="line"><a name="l00167"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#a09fa0a0ab3199f1a7bfc169bce93925d"> 167</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(InsertConvertersTest)</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> {</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({ 1, 5, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a>);</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> </div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a> graph;</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> </div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> inputId = 0;</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="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* head = graph.AddLayer<<a class="code" href="classarmnn_1_1_output_layer.xhtml">armnn::OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</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>  head = graph.InsertNewLayer<<a class="code" href="classarmnn_1_1_addition_layer.xhtml">armnn::AdditionLayer</a>>(head-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0), <span class="stringliteral">""</span>);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  head-><a class="code" href="classarmnn_1_1_layer.xhtml#af2c0edc7ea62a8baaec4d3d9b2b09256">GetOutputHandler</a>().<a class="code" href="classarmnn_1_1_output_handler.xhtml#a97db12c41024f5545ef5cc4153e5443b">SetTensorInfo</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> </div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  graph.InsertNewLayer<<a class="code" href="classarmnn_1_1_input_layer.xhtml">armnn::InputLayer</a>>(head-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(1), inputId++, <span class="stringliteral">""</span>)</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  ->GetOutputHandler().SetTensorInfo(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</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>  head = graph.InsertNewLayer<<a class="code" href="classarmnn_1_1_floor_layer.xhtml">armnn::FloorLayer</a>>(head-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0), <span class="stringliteral">""</span>);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  head-><a class="code" href="classarmnn_1_1_layer.xhtml#af2c0edc7ea62a8baaec4d3d9b2b09256">GetOutputHandler</a>().<a class="code" href="classarmnn_1_1_output_handler.xhtml#a97db12c41024f5545ef5cc4153e5443b">SetTensorInfo</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  head = graph.InsertNewLayer<<a class="code" href="classarmnn_1_1_mem_copy_layer.xhtml">armnn::MemCopyLayer</a>>(head-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0), <span class="stringliteral">""</span>);</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  head-><a class="code" href="classarmnn_1_1_layer.xhtml#af2c0edc7ea62a8baaec4d3d9b2b09256">GetOutputHandler</a>().<a class="code" href="classarmnn_1_1_output_handler.xhtml#a97db12c41024f5545ef5cc4153e5443b">SetTensorInfo</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> </div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  graph.InsertNewLayer<<a class="code" href="classarmnn_1_1_input_layer.xhtml">armnn::InputLayer</a>>(head-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0), inputId++, <span class="stringliteral">""</span>)</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  ->GetOutputHandler().SetTensorInfo(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> </div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="comment">// Check graph layer sequence before inserting convert layers</span></div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.cbegin(),</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  graph.cend(),</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  &IsLayerOfType<armnn::InputLayer>,</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  &IsLayerOfType<armnn::InputLayer>,</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  &IsLayerOfType<armnn::MemCopyLayer>,</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  &IsLayerOfType<armnn::FloorLayer>,</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  &IsLayerOfType<armnn::AdditionLayer>,</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  &IsLayerOfType<armnn::OutputLayer>));</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>  <span class="comment">// Check layers have Float16 DataType</span></div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>& layer : graph)</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  {</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keywordflow">if</span>(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>()==<a class="code" href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">LayerType::Floor</a> || layer-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f">LayerType::Addition</a>)</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  {</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>);</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  }</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  }</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> </div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="comment">// Insert convert layers either side of unsupported layer</span></div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>& layer : graph)</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  {</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keywordflow">if</span>(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>()==<a class="code" href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">LayerType::Floor</a> || layer-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f">LayerType::Addition</a>)</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  {</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <a class="code" href="namespacearmnn.xhtml#ad31c56533e4f9f9f51719599fbfcf7bb">InsertConvertFp16ToFp32LayersBefore</a>(graph, *layer);</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <a class="code" href="namespacearmnn.xhtml#abf625e50a5eaeafce5b39580dc95a9d3">InsertConvertFp32ToFp16LayersAfter</a>(graph, *layer);</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  }</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  }</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> </div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="comment">// Check layers have correct DataType after inserting convert layers</span></div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>& layer : graph)</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="keywordflow">if</span> (layer-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>()==<a class="code" href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">LayerType::Floor</a> || layer-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f">LayerType::Addition</a>)</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  {</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (layer-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a912a4b4d73726c282e3f79aa2c390d6c">LayerType::ConvertFp16ToFp32</a>)</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>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>);</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (layer-><a class="code" href="classarmnn_1_1_layer.xhtml#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4addf4f83b056acd5549949fc0350e9aad">LayerType::ConvertFp32ToFp16</a>)</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>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>);</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  }</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>  <span class="comment">// Check sequence of layers after inserting convert layers</span></div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.cbegin(),</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  graph.cend(),</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  &IsLayerOfType<armnn::InputLayer>,</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  &IsLayerOfType<armnn::InputLayer>,</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  &IsLayerOfType<armnn::ConvertFp16ToFp32Layer>,</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  &IsLayerOfType<armnn::MemCopyLayer>,</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  &IsLayerOfType<armnn::ConvertFp16ToFp32Layer>,</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  &IsLayerOfType<armnn::FloorLayer>,</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  &IsLayerOfType<armnn::ConvertFp32ToFp16Layer>,</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  &IsLayerOfType<armnn::ConvertFp16ToFp32Layer>,</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  &IsLayerOfType<armnn::AdditionLayer>,</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  &IsLayerOfType<armnn::ConvertFp32ToFp16Layer>,</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  &IsLayerOfType<armnn::OutputLayer>));</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> </div><div class="line"><a name="l00258"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#a5065b32dd0aa2c08ef75e953ebedbc16"> 258</a></span> <span class="keywordtype">void</span> <a class="code" href="_optimizer_tests_8cpp.xhtml#a5065b32dd0aa2c08ef75e953ebedbc16">CreateConvolution2dGraph</a>(<a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> &graph, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>* inputShape,</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>* weightsShape, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>* outputShape,</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> {</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span> </div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  std::vector<float> weightsVector(90);</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, weightsShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), weightsVector);</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> </div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> desc;</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span> </div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  input-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span> </div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">Convolution2dLayer</a>* layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">Convolution2dLayer</a>>(desc, <span class="stringliteral">"conv2d"</span>);</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  layer-><a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml#a2664044e28e69309ea08ef385fe53903">m_Weight</a> = std::make_unique<armnn::ScopedCpuTensorHandle>(weights);</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(outputInfo);</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>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  input-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(output-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span> }</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> </div><div class="line"><a name="l00286"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#a6b7bebf2c0d384c3297a6c3b19346555"> 286</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(Conv2dValidateTensorShapesFromInputs)</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> {</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { 1, 3, 8, 16 };</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = { 2, 3, 5, 3 };</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { 1, 2, 4, 14 };</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <a class="code" href="_optimizer_tests_8cpp.xhtml#a5065b32dd0aa2c08ef75e953ebedbc16">CreateConvolution2dGraph</a>(graph, inputShape, weightsShape, outputShape);</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>  BOOST_CHECK_NO_THROW(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a2ceda8d369e861997d558fac74d79c33">InferTensorInfos</a>());</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span> }</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span> </div><div class="line"><a name="l00297"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#a67738cd9d506e11aa4f4f43b9dc30e2c"> 297</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(Conv2dValidateTensorShapesFromInputsNhwc)</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span> {</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { 1, 8, 16, 3 };</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = { 2, 5, 3, 3 };</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { 1, 4, 14, 2 };</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <a class="code" href="_optimizer_tests_8cpp.xhtml#a5065b32dd0aa2c08ef75e953ebedbc16">CreateConvolution2dGraph</a>(graph, inputShape, weightsShape, outputShape, <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</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>  BOOST_CHECK_NO_THROW(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a2ceda8d369e861997d558fac74d79c33">InferTensorInfos</a>());</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span> }</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span> </div><div class="line"><a name="l00308"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#acd97facea671e23ec3e8b33c6c2ea321"> 308</a></span> <span class="keywordtype">void</span> <a class="code" href="_optimizer_tests_8cpp.xhtml#acd97facea671e23ec3e8b33c6c2ea321">CreateDepthwiseConvolution2dGraph</a>(<a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> &graph, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>* inputShape,</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>* weightsShape, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>* outputShape,</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span> </div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  std::vector<float> weightsVector(18);</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, weightsShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), weightsVector);</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="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> desc;</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> </div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  input-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> </div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <a class="code" href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml">DepthwiseConvolution2dLayer</a>* layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml">DepthwiseConvolution2dLayer</a>>(desc, <span class="stringliteral">"depthwiseConv2d"</span>);</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  layer-><a class="code" href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml#a2664044e28e69309ea08ef385fe53903">m_Weight</a> = std::make_unique<armnn::ScopedCpuTensorHandle>(weights);</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> </div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  input-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(output-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span> }</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span> </div><div class="line"><a name="l00336"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#a93b5cb4143e78fec858fe77e86472bec"> 336</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(DepthwiseConv2dValidateTensorShapesFromInputs)</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> {</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { 1, 2, 3, 3 };</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = { 1, 2, 3, 3 };</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { 1, 2, 1, 1 };</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <a class="code" href="_optimizer_tests_8cpp.xhtml#acd97facea671e23ec3e8b33c6c2ea321">CreateDepthwiseConvolution2dGraph</a>(graph, inputShape, weightsShape, outputShape);</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span> </div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  BOOST_CHECK_NO_THROW(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a2ceda8d369e861997d558fac74d79c33">InferTensorInfos</a>());</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> }</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span> </div><div class="line"><a name="l00347"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#aa678f54308701529660f9ee2a70bd042"> 347</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(DepthwiseConv2dValidateTensorShapesFromInputsNhwc)</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span> {</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { 1, 3, 3, 2 };</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = { 1, 2, 3, 3 };</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { 1, 1, 1, 2 };</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <a class="code" href="_optimizer_tests_8cpp.xhtml#acd97facea671e23ec3e8b33c6c2ea321">CreateDepthwiseConvolution2dGraph</a>(graph, inputShape, weightsShape, outputShape, <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>);</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>  BOOST_CHECK_NO_THROW(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a2ceda8d369e861997d558fac74d79c33">InferTensorInfos</a>());</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> </div><div class="line"><a name="l00358"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#a4756218150e4ca0da09d0ecc390a7a17"> 358</a></span> <span class="keywordtype">void</span> <a class="code" href="_optimizer_tests_8cpp.xhtml#a4756218150e4ca0da09d0ecc390a7a17">CreatePooling2dGraph</a>(<a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& graph, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>* inputShape, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>* outputShape,</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span> {</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</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>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> desc;</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 100;</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 5;</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 50;</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 50;</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 50;</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 50;</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</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>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  input-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(inputInfo);</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="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>* layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>>(desc, <span class="stringliteral">"pooling2d"</span>);</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span> </div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  input-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(output-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> }</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span> </div><div class="line"><a name="l00386"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#a8457188fd0859ae6a91c09c3266f58a5"> 386</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(Pooling2dValidateTensorShapesFromInputs)</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>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { 5, 3, 52, 60 };</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { 5, 3, 11, 13 };</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <a class="code" href="_optimizer_tests_8cpp.xhtml#a4756218150e4ca0da09d0ecc390a7a17">CreatePooling2dGraph</a>(graph, inputShape, outputShape, <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>);</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span> </div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  BOOST_CHECK_NO_THROW(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a2ceda8d369e861997d558fac74d79c33">InferTensorInfos</a>());</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span> }</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span> </div><div class="line"><a name="l00396"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#a1c7c22035e9b339dad1aedcf1d9c49e9"> 396</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(Pooling2dValidateTensorShapesFromInputsNhwc)</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span> {</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { 5, 52, 60, 3 };</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { 5, 11, 13, 3 };</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <a class="code" href="_optimizer_tests_8cpp.xhtml#a4756218150e4ca0da09d0ecc390a7a17">CreatePooling2dGraph</a>(graph, inputShape, outputShape, <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>);</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span> </div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  BOOST_CHECK_NO_THROW(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a2ceda8d369e861997d558fac74d79c33">InferTensorInfos</a>());</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> }</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span> </div><div class="line"><a name="l00406"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#aefb2c7f14f687a9432490a1bdee05458"> 406</a></span> <span class="keywordtype">void</span> <a class="code" href="_optimizer_tests_8cpp.xhtml#aefb2c7f14f687a9432490a1bdee05458">CreateResizeBilinearGraph</a>(<a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& graph, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>* inputShape, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>* outputShape,</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span> {</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</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>  <a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">ResizeDescriptor</a> desc;</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  desc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">m_Method</a> = <a class="code" href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f">ResizeMethod::Bilinear</a>;</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  desc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">m_TargetHeight</a> = 3;</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  desc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">m_TargetWidth</a> = 4;</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  desc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</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>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  input-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(inputInfo);</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>  <a class="code" href="classarmnn_1_1_resize_layer.xhtml">ResizeLayer</a>* layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_resize_layer.xhtml">ResizeLayer</a>>(desc, <span class="stringliteral">"resizeBilinear"</span>);</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span> </div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  input-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(output-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</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> </div><div class="line"><a name="l00429"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#acee9ba1427bd42cc38a0402969dd0d35"> 429</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(ResizeBilinearValidateTensorShapesFromInputs)</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>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { 1, 2, 4, 5 };</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { 1, 2, 3, 4 };</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <a class="code" href="_optimizer_tests_8cpp.xhtml#aefb2c7f14f687a9432490a1bdee05458">CreateResizeBilinearGraph</a>(graph, inputShape, outputShape);</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span> </div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  BOOST_CHECK_NO_THROW(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a2ceda8d369e861997d558fac74d79c33">InferTensorInfos</a>());</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span> }</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span> </div><div class="line"><a name="l00439"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#abbc42a4387722b0b5e0c00038288dd4e"> 439</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(ResizeBilinearValidateTensorShapesFromInputsNhwc)</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span> {</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { 1, 4, 5, 2 };</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { 1, 3, 4, 2 };</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <a class="code" href="_optimizer_tests_8cpp.xhtml#aefb2c7f14f687a9432490a1bdee05458">CreateResizeBilinearGraph</a>(graph, inputShape, outputShape, <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>);</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span> </div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  BOOST_CHECK_NO_THROW(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a2ceda8d369e861997d558fac74d79c33">InferTensorInfos</a>());</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span> }</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span> </div><div class="line"><a name="l00449"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#aa4e793c84e5dfea800d4dba921651e5b"> 449</a></span> <span class="keywordtype">void</span> <a class="code" href="_optimizer_tests_8cpp.xhtml#aa4e793c84e5dfea800d4dba921651e5b">CreateGatherGraph</a>(<a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& graph, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& paramsInfo, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& indicesInfo,</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& outputInfo)</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span> {</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* input0 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"params"</span>);</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  input0-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(paramsInfo);</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_layer.xhtml">Layer</a>* input1 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(1, <span class="stringliteral">"indices"</span>);</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  input1-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(indicesInfo);</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>  <a class="code" href="structarmnn_1_1_gather_descriptor.xhtml">GatherDescriptor</a> descriptor;</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <a class="code" href="classarmnn_1_1_gather_layer.xhtml">GatherLayer</a>* layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_gather_layer.xhtml">GatherLayer</a>>(descriptor, <span class="stringliteral">"gather"</span>);</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span> </div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  input0-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  input1-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(1));</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(output-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span> }</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span> </div><div class="line"><a name="l00468"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#a57aa1f639ec35a976735c91889d463a4"> 468</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(GatherValidateTensorShapesFromInputs)</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span> {</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> paramsInfo({10, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> indicesInfo({3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>);</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({3, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span> </div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <a class="code" href="_optimizer_tests_8cpp.xhtml#aa4e793c84e5dfea800d4dba921651e5b">CreateGatherGraph</a>(graph, paramsInfo, indicesInfo, outputInfo);</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span> </div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  BOOST_CHECK_NO_THROW(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a2ceda8d369e861997d558fac74d79c33">InferTensorInfos</a>());</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span> }</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span> </div><div class="line"><a name="l00480"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#af31e0d94eb9ec7e72b9d6d70da3070ec"> 480</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(GatherValidateTensorShapesFromInputs1DParams)</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span> {</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> paramsInfo({8}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> indicesInfo({5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>);</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo( {5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span> </div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <a class="code" href="_optimizer_tests_8cpp.xhtml#aa4e793c84e5dfea800d4dba921651e5b">CreateGatherGraph</a>(graph, paramsInfo, indicesInfo, outputInfo);</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span> </div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  BOOST_CHECK_NO_THROW(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a2ceda8d369e861997d558fac74d79c33">InferTensorInfos</a>());</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span> }</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span> </div><div class="line"><a name="l00492"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#a65b5e30de580e14475b51da9b93c908b"> 492</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(GatherValidateTensorShapesFromInputsMultiDimIndices)</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span> {</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> paramsInfo({3, 2, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> indicesInfo({2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>);</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({2, 2, 2, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</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>  <a class="code" href="_optimizer_tests_8cpp.xhtml#aa4e793c84e5dfea800d4dba921651e5b">CreateGatherGraph</a>(graph, paramsInfo, indicesInfo, outputInfo);</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>  BOOST_CHECK_NO_THROW(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a2ceda8d369e861997d558fac74d79c33">InferTensorInfos</a>());</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span> }</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span> </div><div class="line"><a name="l00504"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#a0ee7b0e1f8d1dd9a9e001720e69086eb"> 504</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(DetectionPostProcessValidateTensorShapes)</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>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> boxEncodingsInfo({1, 10, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="_neon_end_to_end_tests_8cpp.xhtml#a64c1dd1b6dd60be9f4a16db9c8f427a5">scoresInfo</a>({1, 10, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  std::vector<uint8_t> anchorsVector(40);</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> <a class="code" href="_neon_end_to_end_tests_8cpp.xhtml#ac0981848e4ae57729f14f72bd4caa9f8">anchors</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({10, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>), anchorsVector);</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span> </div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> detectionBoxesInfo({1, 3, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> detectionScoresInfo({1, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> detectionClassesInfo({1, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> numDetectionInfo({1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span> </div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* input0 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"boxEncodings"</span>);</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  input0-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(boxEncodingsInfo);</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span> </div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* input1 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(1, <span class="stringliteral">"score"</span>);</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  input1-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(<a class="code" href="_neon_end_to_end_tests_8cpp.xhtml#a64c1dd1b6dd60be9f4a16db9c8f427a5">scoresInfo</a>);</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span> </div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml">DetectionPostProcessDescriptor</a> descriptor;</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae72089bcab60ac175557f4241b16a014">m_MaxDetections</a> = 3;</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span> </div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  <a class="code" href="classarmnn_1_1_detection_post_process_layer.xhtml">DetectionPostProcessLayer</a>* layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_detection_post_process_layer.xhtml">DetectionPostProcessLayer</a>>(descriptor, <span class="stringliteral">"detectionPostProcess"</span>);</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  layer-><a class="code" href="classarmnn_1_1_detection_post_process_layer.xhtml#a6844fecab0edaf324de5a57fee8b65f1">m_Anchors</a> = std::make_unique<armnn::ScopedCpuTensorHandle>(<a class="code" href="_neon_end_to_end_tests_8cpp.xhtml#ac0981848e4ae57729f14f72bd4caa9f8">anchors</a>);</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(detectionBoxesInfo);</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(detectionScoresInfo);</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(detectionClassesInfo);</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(3).<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(numDetectionInfo);</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span> </div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  input0->GetOutputSlot().Connect(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  input1-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(layer-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(1));</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span> </div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  BOOST_CHECK_NO_THROW(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a2ceda8d369e861997d558fac74d79c33">InferTensorInfos</a>());</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> </div><div class="line"><a name="l00539"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#a4ef49bab7a1b82c389b3b45ffb767833"> 539</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FoldPadLayerIntoConvolution2dLayer)</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span> {</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { 1, 2, 2, 3 };</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> paddedShape[] = { 1, 6, 6, 3 };</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = { 1, 2, 3, 3 };</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { 1, 2, 1, 1 };</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span> </div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> paddedInfo(4, paddedShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</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>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  input-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span> </div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor({{ 0, 0 }, { 2, 2 }, { 2, 2 }, { 0, 0 }});</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span> </div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>* padLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_pad_layer.xhtml">PadLayer</a>>(padDescriptor, <span class="stringliteral">"pad"</span>);</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  padLayer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(paddedInfo);</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span> </div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> convolution2dDescriptor;</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</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>  std::vector<float> weightsVector(18);</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, weightsShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>), weightsVector);</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span> </div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  <a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">Convolution2dLayer</a>* conv2dLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">Convolution2dLayer</a>>(convolution2dDescriptor,<span class="stringliteral">"conv2d"</span>);</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  conv2dLayer-><a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml#a2664044e28e69309ea08ef385fe53903">m_Weight</a> = std::make_unique<armnn::ScopedCpuTensorHandle>(weights);</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  conv2dLayer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(outputInfo);</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_layer.xhtml">Layer</a>* output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span> </div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <span class="comment">// Connect up layers - input -> pad -> conv2d -> output</span></div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  input-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(padLayer->GetInputSlot(0));</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  padLayer->GetOutputSlot().Connect(conv2dLayer-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  conv2dLayer-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(output-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</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>  <span class="keyword">auto</span> checkSimpleConv2d = [ ](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer) -> <span class="keywordtype">bool</span></div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  {</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  <span class="keyword">const</span> <span class="keyword">auto</span> conv2dLayer = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">armnn::Convolution2dLayer</a>*<span class="keyword">></span>(layer);</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <span class="keyword">const</span> <span class="keyword">auto</span> conv2dLayerParams = conv2dLayer-><a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#a502c06a1b13e6d90a6cbf47c081f1444">GetParameters</a>();</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <span class="keywordflow">return</span> IsLayerOfType<armnn::Convolution2dLayer>(layer) &&</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  (layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a9a97cb6d32661a57fc33bd29b8e41ff4">GetNameStr</a>() == <span class="stringliteral">"conv2d"</span>) &&</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  (conv2dLayerParams.m_PadLeft == 0) &&</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  (conv2dLayerParams.m_PadRight == 0) &&</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  (conv2dLayerParams.m_PadTop == 0) &&</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  (conv2dLayerParams.m_PadBottom == 0) &&</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  (conv2dLayerParams.m_BiasEnabled == <span class="keyword">false</span>) &&</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  (conv2dLayerParams.m_StrideX == 1) &&</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  (conv2dLayerParams.m_StrideY == 1) &&</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  (conv2dLayerParams.m_DataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>);</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  };</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span> </div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(),</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  &IsLayerOfType<armnn::InputLayer>,</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  &IsLayerOfType<armnn::PadLayer>,</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  checkSimpleConv2d,</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  &IsLayerOfType<armnn::OutputLayer>));</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span> </div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <a class="code" href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a>(graph, <a class="code" href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">armnn::MakeOptimizations</a>(<a class="code" href="namespacearmnn_1_1optimizations.xhtml#add2180a15cdcf5a229de32bb956cb224">FoldPadIntoConvolution2d</a>()));</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span> </div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  <span class="keyword">auto</span> checkPadFoldedIntoConv2d = [ ](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer) -> <span class="keywordtype">bool</span></div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  {</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <span class="keyword">const</span> <span class="keyword">auto</span> conv2dLayer = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">armnn::Convolution2dLayer</a>*<span class="keyword">></span>(layer);</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <span class="keyword">const</span> <span class="keyword">auto</span> conv2dLayerParams = conv2dLayer-><a class="code" href="classarmnn_1_1_layer_with_parameters.xhtml#a502c06a1b13e6d90a6cbf47c081f1444">GetParameters</a>();</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  <span class="keywordflow">return</span> IsLayerOfType<armnn::Convolution2dLayer>(layer) &&</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  (layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a9a97cb6d32661a57fc33bd29b8e41ff4">GetNameStr</a>() == <span class="stringliteral">"folded-pad-into-conv2d"</span>) &&</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  (conv2dLayerParams.m_PadLeft == 2) &&</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  (conv2dLayerParams.m_PadRight == 2) &&</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  (conv2dLayerParams.m_PadTop == 2) &&</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  (conv2dLayerParams.m_PadBottom == 2) &&</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  (conv2dLayerParams.m_BiasEnabled == <span class="keyword">false</span>) &&</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  (conv2dLayerParams.m_StrideX == 1) &&</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  (conv2dLayerParams.m_StrideY == 1) &&</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  (conv2dLayerParams.m_DataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>);</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</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>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(),</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  &IsLayerOfType<armnn::InputLayer>,</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  checkPadFoldedIntoConv2d,</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  &IsLayerOfType<armnn::OutputLayer>));</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span> }</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> <span class="keyword">class </span><a class="code" href="classarmnn_1_1_mock_layer_support.xhtml">MockLayerSupport</a> : <span class="keyword">public</span> <a class="code" href="classarmnn_1_1_layer_support_base.xhtml">LayerSupportBase</a> {</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  <span class="keywordtype">bool</span> <a class="code" href="namespacearmnn.xhtml#a197a353aa963497d29a07796268ea5c1">IsInputSupported</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <span class="comment">/*input*/</span>,</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<std::string&></a> <span class="comment">/*reasonIfUnsupported = EmptyOptional()*/</span>)<span class="keyword"> const override</span></div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  }</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>  <span class="keywordtype">bool</span> <a class="code" href="namespacearmnn.xhtml#a701cecec7714cf8bc9dca804f473610d">IsOutputSupported</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <span class="comment">/*input*/</span>,</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<std::string&></a> <span class="comment">/*reasonIfUnsupported = EmptyOptional()*/</span>)<span class="keyword"> const override</span></div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</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> </div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  <span class="keywordtype">bool</span> <a class="code" href="namespacearmnn.xhtml#a58bfb9626d373249745d78b95543116e">IsActivationSupported</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <span class="comment">/*input0*/</span>,</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <span class="comment">/*output*/</span>,</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<std::string&></a> <span class="comment">/*reasonIfUnsupported = EmptyOptional()*/</span>)<span class="keyword"> const override</span></div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  }</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> </div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span> <span class="keyword">template</span><<span class="keyword">typename</span> NamePolicy></div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span> <span class="keyword">class </span><a class="code" href="classarmnn_1_1_mock_backend.xhtml">MockBackend</a> : <span class="keyword">public</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml">IBackendInternal</a></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> <span class="keyword">public</span>:</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  <a class="code" href="classarmnn_1_1_mock_backend.xhtml">MockBackend</a>() = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  ~<a class="code" href="classarmnn_1_1_mock_backend.xhtml">MockBackend</a>() = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span> </div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <span class="keyword">static</span> <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_backend_id.xhtml">BackendId</a>& GetIdStatic() { <span class="keywordflow">return</span> NamePolicy::GetIdStatic(); }</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_backend_id.xhtml">BackendId</a>& GetId()<span class="keyword"> const override </span>{ <span class="keywordflow">return</span> GetIdStatic(); }</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span> </div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a12bff6d51d63dac1375c89bc8415dc46">IBackendInternal::IMemoryManagerUniquePtr</a> CreateMemoryManager()<span class="keyword"> const override </span>{ <span class="keywordflow">return</span> <span class="keyword">nullptr</span>; };</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span> </div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a72ca1cf423bda4b0a9ffb789627126de">IBackendInternal::IWorkloadFactoryPtr</a> CreateWorkloadFactory(</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>&)<span class="keyword"> const override </span>{ <span class="keywordflow">return</span> <span class="keyword">nullptr</span>; }</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>  <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#ada6d56575c0fe53cf23c7ae4610c6367">IBackendInternal::IBackendContextPtr</a> CreateBackendContext(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a>&)<span class="keyword"> const override</span></div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</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> </div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#ad1794808004025d6e06c176507197b24">IBackendInternal::Optimizations</a> GetOptimizations()<span class="keyword"> const override </span>{ <span class="keywordflow">return</span> {}; }</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a11fa919c11fe46aad613b2e960fcfe90">IBackendInternal::ILayerSupportSharedPtr</a> GetLayerSupport()<span class="keyword"> const override</span></div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <span class="keywordflow">return</span> std::make_shared<MockLayerSupport>();</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  }</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>  <a class="code" href="classarmnn_1_1_optimization_views.xhtml">OptimizationViews</a> OptimizeSubgraphView(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_subgraph_view.xhtml">SubgraphView</a>&)<span class="keyword"> const override</span></div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  <span class="keywordflow">return</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> };</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span> </div><div class="line"><a name="l00682"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#a69de6f6ae1c2ba029453ce16bd4250a8"> 682</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(BackendHintTest)</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span> {</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  <span class="keyword">class </span>TestBackendAssignment : <span class="keyword">public</span> <a class="code" href="classarmnn_1_1_layer_visitor_base.xhtml">LayerVisitorBase</a><VisitorNoThrowPolicy></div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  {</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  <span class="keywordtype">void</span> VisitInputLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer,</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> <span class="keywordtype">id</span>,</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name = <span class="keyword">nullptr</span>)<span class="keyword"> override</span></div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(<span class="keywordtype">id</span>, name);</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  <span class="keyword">auto</span> inputLayer = PolymorphicDowncast<const InputLayer*>(layer);</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  BOOST_TEST((inputLayer->GetBackendId() == <span class="stringliteral">"MockBackend"</span>));</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  }</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span> </div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  <span class="keywordtype">void</span> VisitOutputLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer,</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> <span class="keywordtype">id</span>,</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name = <span class="keyword">nullptr</span>)<span class="keyword"> override</span></div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(<span class="keywordtype">id</span>, name);</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  <span class="keyword">auto</span> outputLayer = PolymorphicDowncast<const OutputLayer*>(layer);</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  BOOST_TEST((outputLayer->GetBackendId() == <span class="stringliteral">"MockBackend"</span>));</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> </div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  <span class="keywordtype">void</span> VisitActivationLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer,</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a>& activationDescriptor,</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name = <span class="keyword">nullptr</span>)<span class="keyword"> override</span></div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(activationDescriptor, name);</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  <span class="keyword">auto</span> activation = PolymorphicDowncast<const ActivationLayer*>(layer);</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  BOOST_TEST((activation->GetBackendId() == <span class="stringliteral">"CustomBackend"</span>));</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  }</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  };</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span> </div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  <span class="keyword">struct </span>CustomPolicy</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  {</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  <span class="keyword">static</span> <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_backend_id.xhtml">BackendId</a>& GetIdStatic()</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  {</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_backend_id.xhtml">BackendId</a> <span class="keywordtype">id</span>=<span class="stringliteral">"CustomBackend"</span>;</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  <span class="keywordflow">return</span> id;</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  }</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> </div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  <span class="keyword">struct </span>MockPolicy</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  {</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  <span class="keyword">static</span> <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_backend_id.xhtml">BackendId</a>& GetIdStatic()</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  {</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_backend_id.xhtml">BackendId</a> <span class="keywordtype">id</span>=<span class="stringliteral">"MockBackend"</span>;</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  <span class="keywordflow">return</span> id;</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>  };</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span> </div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  <span class="keyword">auto</span>& backendRegistry = <a class="code" href="namespacearmnn.xhtml#ac2807505b850738bc8a1991ce669dd47">BackendRegistryInstance</a>();</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span> </div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  backendRegistry.Register(<span class="stringliteral">"MockBackend"</span>, [](){</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  <span class="keywordflow">return</span> std::make_unique<MockBackend<MockPolicy>>();</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  });</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>  backendRegistry.Register(<span class="stringliteral">"CustomBackend"</span>, [](){</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  <span class="keywordflow">return</span> std::make_unique<MockBackend<CustomPolicy>>();</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> </div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  <span class="comment">// Define the network</span></div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  <span class="keyword">auto</span> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>();</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> desc;</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  desc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa32a843da6ea40ab3b17a3421ccdf671b">ActivationFunction::Linear</a>;</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span> </div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  std::unique_ptr<Graph> graph = std::make_unique<Graph>();</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  <span class="keyword">auto</span> input = graph->AddLayer<<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <span class="keyword">auto</span> act = graph->AddLayer<<a class="code" href="classarmnn_1_1_activation_layer.xhtml">ActivationLayer</a>>(desc, <span class="stringliteral">"activation"</span>);</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  <span class="keyword">auto</span> output = graph->AddLayer<<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span> </div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <a class="code" href="classarmnn_1_1_backend_id.xhtml">BackendId</a> customBackendId(<span class="stringliteral">"CustomBackend"</span>);</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  act-><a class="code" href="classarmnn_1_1_layer.xhtml#a43a46eafee5c08787ab17b4342730c20">BackendSelectionHint</a>(customBackendId);</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span> </div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  input->GetOutputSlot(0).Connect(act-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  act-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(output-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</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>  <a class="code" href="classarmnn_1_1_optimized_network_impl.xhtml">OptimizedNetworkImpl</a> optNet(std::move(graph));</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>  <span class="comment">// Get the optimized graph</span></div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& optGraph = optNet.<a class="code" href="classarmnn_1_1_optimized_network_impl.xhtml#a49800ad35ea869aa5569519760d3b339">GetGraph</a>();</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span> </div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  std::vector<BackendId> prefs{<span class="stringliteral">"MockBackend"</span>, <span class="stringliteral">"CustomBackend"</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>  <a class="code" href="namespacearmnn.xhtml#a1854d9cda81304325664363c1fd0fb27">BackendIdSet</a> availableBackends = {<span class="stringliteral">"CustomBackend"</span>, <span class="stringliteral">"MockBackend"</span>};</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  <a class="code" href="classarmnn_1_1_device_spec.xhtml">DeviceSpec</a> spec(availableBackends);</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span> </div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  <a class="code" href="structarmnn_1_1_backend_settings.xhtml">BackendSettings</a> backendSettings(prefs, spec);</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span> </div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  <span class="comment">// Assign an available backend to each layer</span></div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  <a class="code" href="classarmnn_1_1_graph.xhtml#acc25db0641c1c22faf95af3bb49080c9">Graph::Iterator</a> firstLayer = optGraph.<a class="code" href="classarmnn_1_1_graph.xhtml#a2387033802383edbdc95f9bbb12a707e">begin</a>();</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  <a class="code" href="classarmnn_1_1_graph.xhtml#acc25db0641c1c22faf95af3bb49080c9">Graph::Iterator</a> lastLayer = optGraph.<a class="code" href="classarmnn_1_1_graph.xhtml#ab45dae688fc5d8983727abffa4389003">end</a>();</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>  <a class="code" href="classarmnn_1_1_optimized_network_impl.xhtml">OptimizedNetworkImpl</a>* optNetObjPtr = &optNet;</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  <a class="code" href="structarmnn_1_1_optimization_result.xhtml">OptimizationResult</a> res = <a class="code" href="namespacearmnn.xhtml#a224df72b3d7a3bba8609bc167286e3f7">AssignBackends</a>(optNetObjPtr,</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  backendSettings,</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  firstLayer,</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  lastLayer,</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>());</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span> </div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  BOOST_TEST(res.<a class="code" href="structarmnn_1_1_optimization_result.xhtml#a955b65059e7f9429a5d6041136bc1487">IsOk</a>());</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>  TestBackendAssignment visitor;</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> it =firstLayer; it != lastLayer; ++it)</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  {</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  (*it)->Accept(visitor);</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> }</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span> </div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span> <span class="comment">// Tests that OptimizeForExclusiveConnections works, fusing when needed, using BatchNorm fusing as example</span></div><div class="line"><a name="l00792"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#ac44f534a66a9124fdebe5f6d566215be"> 792</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(OptimizeForExclusiveConnectionsFuseTest)</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span> {</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  <span class="comment">// Define layers information</span></div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> convolution2dDescriptor;</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  convolution2dDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">BatchNormalizationDescriptor</a> batchNormDescriptor;</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  batchNormDescriptor.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span> </div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputDimensionSizes[] = {1, 4, 4, 3}; <span class="comment">// NHWCin</span></div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsDimensionSizes[] = {1, 2, 2, 3}; <span class="comment">// CoutHWCin</span></div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputDimensionSizes[] = {1, 3, 3, 1}; <span class="comment">// NHWCout</span></div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannelSize[] = {outputDimensionSizes[3]}; <span class="comment">// Cout</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"> 807</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo(4, inputDimensionSizes, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(4, outputDimensionSizes, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span> </div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  std::vector<float> weightsVector = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12};</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(4, weightsDimensionSizes, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>), weightsVector);</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span> </div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  std::vector<float> betaVector = { 0.1f };</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  std::vector<float> gammaVector = { 0.5f };</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  std::vector<float> meanVector = { 0 };</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  std::vector<float> varianceVector = { 1 };</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> beta(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, outputChannelSize, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>), betaVector);</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> gamma(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, outputChannelSize, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>), gammaVector);</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> mean(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, outputChannelSize, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>), meanVector);</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> variance(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, outputChannelSize, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>), varianceVector);</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span> </div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  <span class="comment">// Define the network</span></div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  <span class="keyword">auto</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  <span class="keyword">auto</span> conv = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">Convolution2dLayer</a>>(convolution2dDescriptor, <span class="stringliteral">"convolution"</span>);</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  <span class="keyword">auto</span> batchNorm = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>>(batchNormDescriptor, <span class="stringliteral">"batchNorm"</span>);</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  <span class="keyword">auto</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span> </div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  <span class="comment">// Set layer information</span></div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  input-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  conv-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  batchNorm-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  conv->m_Weight = std::make_unique<ScopedCpuTensorHandle>(weights);</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  batchNorm->m_Beta = std::make_unique<ScopedCpuTensorHandle>(beta);</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  batchNorm->m_Gamma = std::make_unique<ScopedCpuTensorHandle>(gamma);</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  batchNorm->m_Mean = std::make_unique<ScopedCpuTensorHandle>(mean);</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  batchNorm->m_Variance = std::make_unique<ScopedCpuTensorHandle>(variance);</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  <span class="keywordflow">if</span> (convolution2dDescriptor.m_BiasEnabled)</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  {</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  std::vector<float> biasVector = {11};</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> bias(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, outputChannelSize, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>), biasVector);</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  conv->m_Bias = std::make_unique<ScopedCpuTensorHandle>(bias);</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  }</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span> </div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  <span class="comment">// Connect layers</span></div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  input-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(conv-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  conv-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(batchNorm-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>  batchNorm-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(output-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span> </div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  BOOST_CHECK(4 == graph.<a class="code" href="classarmnn_1_1_graph.xhtml#afdf8eb85585a798ad0e936bde884d87b">GetNumLayers</a>());</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(),</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  &IsLayerOfType<Convolution2dLayer>,</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  &IsLayerOfType<BatchNormalizationLayer>,</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  &IsLayerOfType<OutputLayer>));</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span> </div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  <span class="comment">// Optimize graph</span></div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  <a class="code" href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a>(graph, <a class="code" href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">MakeOptimizations</a>(<a class="code" href="namespacearmnn_1_1optimizations.xhtml#aa52c06792e18dc13030e82476f706f9e">FuseBatchNormIntoConvolution2DFloat32</a>()));</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span> </div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  <span class="keyword">auto</span> checkFusedConv2d = [](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer)-><span class="keywordtype">bool</span> </div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  {</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  <span class="keywordflow">return</span> IsLayerOfType<armnn::Convolution2dLayer>(layer) &&</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  (layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a9a97cb6d32661a57fc33bd29b8e41ff4">GetNameStr</a>() == <span class="stringliteral">"fused-batchNorm-into-convolution"</span>);</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  };</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>  BOOST_CHECK(3 == graph.<a class="code" href="classarmnn_1_1_graph.xhtml#afdf8eb85585a798ad0e936bde884d87b">GetNumLayers</a>());</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(),</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  checkFusedConv2d,</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  &IsLayerOfType<OutputLayer>));</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> </div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span> <span class="comment">// Tests that OptimizeForExclusiveConnections works, not fusing when not needed, using BatchNorm fusing as example</span></div><div class="line"><a name="l00876"></a><span class="lineno"><a class="line" href="_optimizer_tests_8cpp.xhtml#a73b9322c5ef957cedfd050053fd345c3"> 876</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(OptimizeForExclusiveConnectionsWithoutFuseTest)</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span> {</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  <span class="comment">// Define the network</span></div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> convolution2dDescriptor;</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">BatchNormalizationDescriptor</a> batchNormDescriptor;</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>  <span class="keyword">auto</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  <span class="keyword">auto</span> conv = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">Convolution2dLayer</a>>(convolution2dDescriptor, <span class="stringliteral">"convolution"</span>);</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  <span class="keyword">auto</span> batchNorm = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>>(batchNormDescriptor, <span class="stringliteral">"batchNorm"</span>);</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  <span class="keyword">auto</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  <span class="keyword">auto</span> output2 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(1, <span class="stringliteral">"output2"</span>);</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span> </div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  <span class="comment">// Connect layers</span></div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>  input-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(conv-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  conv-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(batchNorm-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  batchNorm-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(output-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  conv-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">Connect</a>(output2-><a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0));</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>  BOOST_CHECK(5 == graph.<a class="code" href="classarmnn_1_1_graph.xhtml#afdf8eb85585a798ad0e936bde884d87b">GetNumLayers</a>());</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(),</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  &IsLayerOfType<armnn::InputLayer>,</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  &IsLayerOfType<armnn::Convolution2dLayer>,</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  &IsLayerOfType<armnn::BatchNormalizationLayer>,</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  &IsLayerOfType<armnn::OutputLayer>,</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  &IsLayerOfType<armnn::OutputLayer>));</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  <span class="comment">// Optimize graph</span></div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  <a class="code" href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a>(graph, <a class="code" href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">armnn::MakeOptimizations</a>(<a class="code" href="namespacearmnn_1_1optimizations.xhtml#aa52c06792e18dc13030e82476f706f9e">FuseBatchNormIntoConvolution2DFloat32</a>()));</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>  BOOST_CHECK(5 == graph.<a class="code" href="classarmnn_1_1_graph.xhtml#afdf8eb85585a798ad0e936bde884d87b">GetNumLayers</a>());</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">cbegin</a>(),</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">cend</a>(),</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  &IsLayerOfType<armnn::InputLayer>,</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  &IsLayerOfType<armnn::Convolution2dLayer>,</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  &IsLayerOfType<armnn::BatchNormalizationLayer>,</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  &IsLayerOfType<armnn::OutputLayer>,</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  &IsLayerOfType<armnn::OutputLayer>));</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span> }</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</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_graph_xhtml_a2387033802383edbdc95f9bbb12a707e"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a2387033802383edbdc95f9bbb12a707e">armnn::Graph::begin</a></div><div class="ttdeci">Iterator begin()</div><div class="ttdoc">Returns iterator pointing to the beginning of the list. Lowercase for range-based for loops...</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00162">Graph.hpp:162</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_basic_parameters_xhtml_a0e0e17d5b494993407cb75d614455ddd"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#a0e0e17d5b494993407cb75d614455ddd">armnn::LstmBasicParameters::m_ForgetGateBias</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_ForgetGateBias</div><div class="ttdoc">A unique pointer to represent 1D weights tensor with dimensions [num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00069">LstmLayer.hpp:69</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00454">Descriptors.hpp:454</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_basic_parameters_xhtml_ae8d897b8d282f25a6eb784c4aaa98df6"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#ae8d897b8d282f25a6eb784c4aaa98df6">armnn::LstmBasicParameters::m_InputToOutputWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_InputToOutputWeights</div><div class="ttdoc">A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00061">LstmLayer.hpp:61</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_basic_parameters_xhtml_a6e8c3db3c5474f0760553ff93fbc39e6"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#a6e8c3db3c5474f0760553ff93fbc39e6">armnn::LstmBasicParameters::m_RecurrentToCellWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_RecurrentToCellWeights</div><div class="ttdoc">A unique pointer to represent 2D weights tensor with dimensions [output_size, num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00065">LstmLayer.hpp:65</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_support_base_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer_support_base.xhtml">armnn::LayerSupportBase</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_support_base_8hpp_source.xhtml#l00013">LayerSupportBase.hpp:13</a></div></div>
<div class="ttc" id="classarmnn_1_1_mock_backend_xhtml"><div class="ttname"><a href="classarmnn_1_1_mock_backend.xhtml">armnn::MockBackend</a></div><div class="ttdef"><b>Definition:</b> <a href="_mock_backend_8hpp_source.xhtml#l00143">MockBackend.hpp:143</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a6c9de81fc65b3c4924cab11907075a17"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">armnn::LstmDescriptor::m_ProjectionEnabled</a></div><div class="ttdeci">bool m_ProjectionEnabled</div><div class="ttdoc">Enable/disable the projection layer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00945">Descriptors.hpp:945</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Convolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00456">Descriptors.hpp:456</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a224df72b3d7a3bba8609bc167286e3f7"><div class="ttname"><a href="namespacearmnn.xhtml#a224df72b3d7a3bba8609bc167286e3f7">armnn::AssignBackends</a></div><div class="ttdeci">OptimizationResult AssignBackends(OptimizedNetworkImpl *optNetObjPtr, BackendSettings &backendSettings, Graph::Iterator &firstLayer, Graph::Iterator &lastLayer, Optional< std::vector< std::string > &> errMessages)</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00869">Network.cpp:869</a></div></div>
<div class="ttc" id="classarmnn_1_1_lstm_layer_xhtml_a8838b317568861294a9df608221f185e"><div class="ttname"><a href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">armnn::LstmLayer::m_BasicParameters</a></div><div class="ttdeci">LstmBasicParameters m_BasicParameters</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00081">LstmLayer.hpp:81</a></div></div>
<div class="ttc" id="classarmnn_1_1_batch_normalization_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_batch_normalization_layer.xhtml">armnn::BatchNormalizationLayer</a></div><div class="ttdoc">This layer represents a batch normalization operation. </div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_layer_8hpp_source.xhtml#l00015">BatchNormalizationLayer.hpp:15</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_backend_internal_xhtml_a72ca1cf423bda4b0a9ffb789627126de"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml#a72ca1cf423bda4b0a9ffb789627126de">armnn::IBackendInternal::IWorkloadFactoryPtr</a></div><div class="ttdeci">std::unique_ptr< IWorkloadFactory > IWorkloadFactoryPtr</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.xhtml#l00080">IBackendInternal.hpp:80</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="structarmnn_1_1_pooling2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Pooling2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00371">Descriptors.hpp:371</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_abf625e50a5eaeafce5b39580dc95a9d3"><div class="ttname"><a href="namespacearmnn.xhtml#abf625e50a5eaeafce5b39580dc95a9d3">armnn::InsertConvertFp32ToFp16LayersAfter</a></div><div class="ttdeci">std::vector< ConvertFp32ToFp16Layer * > InsertConvertFp32ToFp16LayersAfter(Graph &graph, Layer &layer)</div><div class="ttdef"><b>Definition:</b> <a href="_network_utils_8cpp_source.xhtml#l00201">NetworkUtils.cpp:201</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::DepthwiseConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00506">Descriptors.hpp:506</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_backend_internal_xhtml_ad1794808004025d6e06c176507197b24"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml#ad1794808004025d6e06c176507197b24">armnn::IBackendInternal::Optimizations</a></div><div class="ttdeci">std::vector< OptimizationPtr > Optimizations</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.xhtml#l00086">IBackendInternal.hpp:86</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_with_parameters_xhtml_a502c06a1b13e6d90a6cbf47c081f1444"><div class="ttname"><a href="classarmnn_1_1_layer_with_parameters.xhtml#a502c06a1b13e6d90a6cbf47c081f1444">armnn::LayerWithParameters::GetParameters</a></div><div class="ttdeci">const Parameters & GetParameters() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_with_parameters_8hpp_source.xhtml#l00018">LayerWithParameters.hpp:18</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00050">Types.hpp:50</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad31c56533e4f9f9f51719599fbfcf7bb"><div class="ttname"><a href="namespacearmnn.xhtml#ad31c56533e4f9f9f51719599fbfcf7bb">armnn::InsertConvertFp16ToFp32LayersBefore</a></div><div class="ttdeci">std::vector< ConvertFp16ToFp32Layer * > InsertConvertFp16ToFp32LayersBefore(Graph &graph, Layer &layer, bool expectCorrectInputType)</div><div class="ttdef"><b>Definition:</b> <a href="_network_utils_8cpp_source.xhtml#l00129">NetworkUtils.cpp:129</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_opt_cifg_parameters_xhtml_a4d731c5e73638c7cf7e63f65e9f8b550"><div class="ttname"><a href="structarmnn_1_1_lstm_opt_cifg_parameters.xhtml#a4d731c5e73638c7cf7e63f65e9f8b550">armnn::LstmOptCifgParameters::m_InputToInputWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_InputToInputWeights</div><div class="ttdoc">A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00029">LstmLayer.hpp:29</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Pooling2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00365">Descriptors.hpp:365</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_aa7427025a851113a492de0b68b23d22a"><div class="ttname"><a href="namespacearmnn.xhtml#aa7427025a851113a492de0b68b23d22a">armnn::MakeOptimizations</a></div><div class="ttdeci">Optimizer::Optimizations MakeOptimizations(Args &&... args)</div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_8hpp_source.xhtml#l00043">Optimizer.hpp:43</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a86e88bef0df4df96df752b4b8955a3af"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">armnn::LstmDescriptor::m_ClippingThresProj</a></div><div class="ttdeci">float m_ClippingThresProj</div><div class="ttdoc">Clipping threshold value for the projection. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00939">Descriptors.hpp:939</a></div></div>
<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional</a></div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00270">Optional.hpp:270</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_backend_internal_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml">armnn::IBackendInternal</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.xhtml#l00068">IBackendInternal.hpp:68</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3"><div class="ttname"><a href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</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="classarmnn_1_1_layer_xhtml_a43a46eafee5c08787ab17b4342730c20"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a43a46eafee5c08787ab17b4342730c20">armnn::Layer::BackendSelectionHint</a></div><div class="ttdeci">void BackendSelectionHint(Optional< BackendId > backend) final</div><div class="ttdoc">Provide a hint for the optimizer as to which backend to prefer for this layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00330">Layer.hpp:330</a></div></div>
<div class="ttc" id="_floating_point_converter_8hpp_xhtml"><div class="ttname"><a href="_floating_point_converter_8hpp.xhtml">FloatingPointConverter.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_mock_layer_support_xhtml"><div class="ttname"><a href="classarmnn_1_1_mock_layer_support.xhtml">armnn::MockLayerSupport</a></div><div class="ttdef"><b>Definition:</b> <a href="_mock_backend_8hpp_source.xhtml#l00171">MockBackend.hpp:171</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::DepthwiseConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00508">Descriptors.hpp:508</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="classarmnn_1_1_device_spec_xhtml"><div class="ttname"><a href="classarmnn_1_1_device_spec.xhtml">armnn::DeviceSpec</a></div><div class="ttdef"><b>Definition:</b> <a href="_device_spec_8hpp_source.xhtml#l00014">DeviceSpec.hpp:14</a></div></div>
<div class="ttc" id="classarmnn_1_1_depthwise_convolution2d_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml">armnn::DepthwiseConvolution2dLayer</a></div><div class="ttdoc">This layer represents a depthwise convolution 2d operation. </div><div class="ttdef"><b>Definition:</b> <a href="_depthwise_convolution2d_layer_8hpp_source.xhtml#l00015">DepthwiseConvolution2dLayer.hpp:15</a></div></div>
<div class="ttc" id="namespacearmnn_1_1optimizations_xhtml"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml">armnn::optimizations</a></div><div class="ttdef"><b>Definition:</b> <a href="_add_broadcast_reshape_layer_8hpp_source.xhtml#l00014">AddBroadcastReshapeLayer.hpp:14</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_a7563c5b899e7d0ada08fd0fdb202f205"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">armnn::Graph::AddLayer</a></div><div class="ttdeci">LayerT * AddLayer(Args &&... args)</div><div class="ttdoc">Adds a new layer, of type LayerType, to the graph constructed with the arguments passed. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00402">Graph.hpp:402</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a6d8fb685cc1ff224f25aa127fcf62c86"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">armnn::Pooling2dDescriptor::m_PoolWidth</a></div><div class="ttdeci">uint32_t m_PoolWidth</div><div class="ttdoc">Pooling width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00373">Descriptors.hpp:373</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_a98b1109a9006f8cc7d4566146a3bd737"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a98b1109a9006f8cc7d4566146a3bd737">armnn::Graph::cbegin</a></div><div class="ttdeci">ConstIterator cbegin() const</div><div class="ttdoc">Returns const iterator pointing to the beginning of the list. Lowercase for range-based for loops...</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00172">Graph.hpp:172</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a></div><div class="ttdoc">A Convolution2dDescriptor for the Convolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00408">Descriptors.hpp:408</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div>
<div class="ttc" id="_optimizer_tests_8cpp_xhtml_aefb2c7f14f687a9432490a1bdee05458"><div class="ttname"><a href="_optimizer_tests_8cpp.xhtml#aefb2c7f14f687a9432490a1bdee05458">CreateResizeBilinearGraph</a></div><div class="ttdeci">void CreateResizeBilinearGraph(Graph &graph, const unsigned int *inputShape, const unsigned int *outputShape, DataLayout dataLayout=DataLayout::NCHW)</div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_tests_8cpp_source.xhtml#l00406">OptimizerTests.cpp:406</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_basic_parameters_xhtml_a3d5f129421bbe6479a66d4ed1356bf68"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#a3d5f129421bbe6479a66d4ed1356bf68">armnn::LstmBasicParameters::m_RecurrentToForgetWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_RecurrentToForgetWeights</div><div class="ttdoc">A unique pointer to represent 2D weights tensor with dimensions [output_size, num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00063">LstmLayer.hpp:63</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_adcfb97035799ea4c043f9ef370714815"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#adcfb97035799ea4c043f9ef370714815">armnn::OutputSlot::Connect</a></div><div class="ttdeci">int Connect(InputSlot &destination)</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00083">Layer.cpp:83</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a869254cb56968986a78a79e1d6d4a86b"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">armnn::ResizeDescriptor::m_Method</a></div><div class="ttdeci">ResizeMethod m_Method</div><div class="ttdoc">The Interpolation method to use (Bilinear, NearestNeighbor). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00821">Descriptors.hpp:821</a></div></div>
<div class="ttc" id="classarmnn_1_1_optimizer_xhtml_a1f48ba622b76ea04d15c9b62f642bf08"><div class="ttname"><a href="classarmnn_1_1_optimizer.xhtml#a1f48ba622b76ea04d15c9b62f642bf08">armnn::Optimizer::Pass</a></div><div class="ttdeci">static void Pass(Graph &graph, const Optimizations &optimizations)</div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_8cpp_source.xhtml#l00016">Optimizer.cpp:16</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6"><div class="ttname"><a href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a></div><div class="ttdoc">The padding fields don&#39;t count and are ignored. </div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a8c29d6ea9b4186d69aad5961c910939c"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">armnn::Pooling2dDescriptor::m_PaddingMethod</a></div><div class="ttdeci">PaddingMethod m_PaddingMethod</div><div class="ttdoc">The padding method to be used. (Exclude, IgnoreValue). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00383">Descriptors.hpp:383</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::BatchNormalizationDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00641">Descriptors.hpp:641</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_basic_parameters_xhtml_aacb55e0992b6781a7bd3225ab6e6bb2f"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#aacb55e0992b6781a7bd3225ab6e6bb2f">armnn::LstmBasicParameters::m_OutputGateBias</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_OutputGateBias</div><div class="ttdoc">A unique pointer to represent 1D weights tensor with dimensions [num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00073">LstmLayer.hpp:73</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4addf4f83b056acd5549949fc0350e9aad"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4addf4f83b056acd5549949fc0350e9aad">armnn::LayerType::ConvertFp32ToFp16</a></div></div>
<div class="ttc" id="namespacearmnn_1_1optimizations_xhtml_aa52c06792e18dc13030e82476f706f9e"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml#aa52c06792e18dc13030e82476f706f9e">armnn::optimizations::FuseBatchNormIntoConvolution2DFloat32</a></div><div class="ttdeci">OptimizeForExclusiveConnection< Convolution2dLayer, BatchNormalizationLayer, FuseBatchNorm< Convolution2dLayer, armnn::DataType::Float32 > > FuseBatchNormIntoConvolution2DFloat32</div><div class="ttdef"><b>Definition:</b> <a href="_fuse_batch_norm_8hpp_source.xhtml#l00187">FuseBatchNorm.hpp:187</a></div></div>
<div class="ttc" id="classarmnn_1_1_activation_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_activation_layer.xhtml">armnn::ActivationLayer</a></div><div class="ttdoc">This layer represents an activation operation with the specified activation function. </div><div class="ttdef"><b>Definition:</b> <a href="_activation_layer_8hpp_source.xhtml#l00012">ActivationLayer.hpp:12</a></div></div>
<div class="ttc" id="classarmnn_1_1_optimization_views_xhtml"><div class="ttname"><a href="classarmnn_1_1_optimization_views.xhtml">armnn::OptimizationViews</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_optimization_views_8hpp_source.xhtml#l00013">OptimizationViews.hpp:13</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="structarmnn_1_1_pooling2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Pooling2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00369">Descriptors.hpp:369</a></div></div>
<div class="ttc" id="_backend_settings_8hpp_xhtml"><div class="ttname"><a href="_backend_settings_8hpp.xhtml">BackendSettings.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_optimized_network_impl_xhtml_a49800ad35ea869aa5569519760d3b339"><div class="ttname"><a href="classarmnn_1_1_optimized_network_impl.xhtml#a49800ad35ea869aa5569519760d3b339">armnn::OptimizedNetworkImpl::GetGraph</a></div><div class="ttdeci">Graph & GetGraph()</div><div class="ttdef"><b>Definition:</b> <a href="_optimized_network_impl_8hpp_source.xhtml#l00021">OptimizedNetworkImpl.hpp:21</a></div></div>
<div class="ttc" id="classarmnn_1_1_detection_post_process_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_detection_post_process_layer.xhtml">armnn::DetectionPostProcessLayer</a></div><div class="ttdoc">This layer represents a detection postprocess operator. </div><div class="ttdef"><b>Definition:</b> <a href="_detection_post_process_layer_8hpp_source.xhtml#l00016">DetectionPostProcessLayer.hpp:16</a></div></div>
<div class="ttc" id="_test_utils_8hpp_xhtml"><div class="ttname"><a href="_test_utils_8hpp.xhtml">TestUtils.hpp</a></div></div>
<div class="ttc" id="_optimizer_tests_8cpp_xhtml_a4756218150e4ca0da09d0ecc390a7a17"><div class="ttname"><a href="_optimizer_tests_8cpp.xhtml#a4756218150e4ca0da09d0ecc390a7a17">CreatePooling2dGraph</a></div><div class="ttdeci">void CreatePooling2dGraph(Graph &graph, const unsigned int *inputShape, const unsigned int *outputShape, DataLayout dataLayout=DataLayout::NCHW)</div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_tests_8cpp_source.xhtml#l00358">OptimizerTests.cpp:358</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_backend_internal_xhtml_a12bff6d51d63dac1375c89bc8415dc46"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml#a12bff6d51d63dac1375c89bc8415dc46">armnn::IBackendInternal::IMemoryManagerUniquePtr</a></div><div class="ttdeci">std::unique_ptr< IMemoryManager > IMemoryManagerUniquePtr</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.xhtml#l00091">IBackendInternal.hpp:91</a></div></div>
<div class="ttc" id="classarmnn_1_1_pad_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_pad_layer.xhtml">armnn::PadLayer</a></div><div class="ttdoc">This layer represents a pad operation. </div><div class="ttdef"><b>Definition:</b> <a href="_pad_layer_8hpp_source.xhtml#l00014">PadLayer.hpp:14</a></div></div>
<div class="ttc" id="classarmnn_1_1_lstm_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_lstm_layer.xhtml">armnn::LstmLayer</a></div><div class="ttdoc">This layer represents a LSTM operation. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00077">LstmLayer.hpp:77</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &&...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div>
<div class="ttc" id="namespacearmnn_1_1optimizations_xhtml_add2180a15cdcf5a229de32bb956cb224"><div class="ttname"><a href="namespacearmnn_1_1optimizations.xhtml#add2180a15cdcf5a229de32bb956cb224">armnn::optimizations::FoldPadIntoConvolution2d</a></div><div class="ttdeci">OptimizeForConnection< PadLayer, Convolution2dLayer, FoldPadIntoConvolution2dImpl > FoldPadIntoConvolution2d</div><div class="ttdef"><b>Definition:</b> <a href="_fold_pad_into_convolution2d_8hpp_source.xhtml#l00088">FoldPadIntoConvolution2d.hpp:88</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_acc25db0641c1c22faf95af3bb49080c9"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#acc25db0641c1c22faf95af3bb49080c9">armnn::Graph::Iterator</a></div><div class="ttdeci">LayerList::const_iterator Iterator</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00050">Graph.hpp:50</a></div></div>
<div class="ttc" id="_backend_registry_8hpp_xhtml"><div class="ttname"><a href="_backend_registry_8hpp.xhtml">BackendRegistry.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Pooling2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00377">Descriptors.hpp:377</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_basic_parameters_xhtml_a51255889cbc063130a3d691c1781c5d3"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#a51255889cbc063130a3d691c1781c5d3">armnn::LstmBasicParameters::m_CellBias</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_CellBias</div><div class="ttdoc">A unique pointer to represent 1D weights tensor with dimensions [num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00071">LstmLayer.hpp:71</a></div></div>
<div class="ttc" id="_layer_support_base_8hpp_xhtml"><div class="ttname"><a href="_layer_support_base_8hpp.xhtml">LayerSupportBase.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ab8cf8f9fb6792e654c2d8d8382f6f01b"><div class="ttname"><a href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a></div><div class="ttdeci">int LayerBindingId</div><div class="ttdoc">Type of identifiers for bindable layers (inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00210">Types.hpp:210</a></div></div>
<div class="ttc" id="_optimizer_8hpp_xhtml"><div class="ttname"><a href="_optimizer_8hpp.xhtml">Optimizer.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml">armnn::ResizeDescriptor</a></div><div class="ttdoc">A ResizeDescriptor for the ResizeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00794">Descriptors.hpp:794</a></div></div>
<div class="ttc" id="src_2backends_2backends_common_2_i_backend_internal_8hpp_xhtml"><div class="ttname"><a href="src_2backends_2backends_common_2_i_backend_internal_8hpp.xhtml">IBackendInternal.hpp</a></div></div>
<div class="ttc" id="_polymorphic_downcast_8hpp_xhtml"><div class="ttname"><a href="_polymorphic_downcast_8hpp.xhtml">PolymorphicDowncast.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a197a353aa963497d29a07796268ea5c1"><div class="ttname"><a href="namespacearmnn.xhtml#a197a353aa963497d29a07796268ea5c1">armnn::IsInputSupported</a></div><div class="ttdeci">bool IsInputSupported(const BackendId &backend, const TensorInfo &input, char *reasonIfUnsupported=nullptr, size_t reasonIfUnsupportedMaxLength=1024)</div><div class="ttdoc">Deprecated in favor of IBackend and ILayerSupport interfaces. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_support_8cpp_source.xhtml#l00346">LayerSupport.cpp:346</a></div></div>
<div class="ttc" id="classarmnn_1_1_subgraph_view_xhtml"><div class="ttname"><a href="classarmnn_1_1_subgraph_view.xhtml">armnn::SubgraphView</a></div><div class="ttdoc">The SubgraphView class represents a subgraph of a Graph. </div><div class="ttdef"><b>Definition:</b> <a href="_subgraph_view_8hpp_source.xhtml#l00023">SubgraphView.hpp:23</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a5699e8606c37d18c03910b242cd1b010"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">armnn::Pooling2dDescriptor::m_PoolHeight</a></div><div class="ttdeci">uint32_t m_PoolHeight</div><div class="ttdoc">Pooling height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00375">Descriptors.hpp:375</a></div></div>
<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_ae72089bcab60ac175557f4241b16a014"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae72089bcab60ac175557f4241b16a014">armnn::DetectionPostProcessDescriptor::m_MaxDetections</a></div><div class="ttdeci">uint32_t m_MaxDetections</div><div class="ttdoc">Maximum numbers of detections. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00543">Descriptors.hpp:543</a></div></div>
<div class="ttc" id="structarmnn_1_1_pad_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a></div><div class="ttdoc">A PadDescriptor for the PadLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00975">Descriptors.hpp:975</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_opt_peephole_parameters_xhtml_a5d0ebbbb11b727a67877df40b59a628c"><div class="ttname"><a href="structarmnn_1_1_lstm_opt_peephole_parameters.xhtml#a5d0ebbbb11b727a67877df40b59a628c">armnn::LstmOptPeepholeParameters::m_CellToForgetWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_CellToForgetWeights</div><div class="ttdoc">A unique pointer to represent 1D weights tensor with dimensions [num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00049">LstmLayer.hpp:49</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_acf8b8e23bf647836592982f97088d375"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">armnn::Layer::GetInputSlot</a></div><div class="ttdeci">const InputSlot & GetInputSlot(unsigned int index) const override</div><div class="ttdoc">Get a const input slot handle by slot index. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00316">Layer.hpp:316</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00446">Descriptors.hpp:446</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a912a4b4d73726c282e3f79aa2c390d6c"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a912a4b4d73726c282e3f79aa2c390d6c">armnn::LayerType::ConvertFp16ToFp32</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::DepthwiseConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00498">Descriptors.hpp:498</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_output_layer.xhtml">armnn::OutputLayer</a></div><div class="ttdoc">A layer user-provided data can be bound to (e.g. inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_output_layer_8hpp_source.xhtml#l00013">OutputLayer.hpp:13</a></div></div>
<div class="ttc" id="classarmnn_1_1_gather_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_gather_layer.xhtml">armnn::GatherLayer</a></div><div class="ttdoc">This layer represents a Gather operator. </div><div class="ttdef"><b>Definition:</b> <a href="_gather_layer_8hpp_source.xhtml#l00014">GatherLayer.hpp:14</a></div></div>
<div class="ttc" id="classarmnn_1_1_detection_post_process_layer_xhtml_a6844fecab0edaf324de5a57fee8b65f1"><div class="ttname"><a href="classarmnn_1_1_detection_post_process_layer.xhtml#a6844fecab0edaf324de5a57fee8b65f1">armnn::DetectionPostProcessLayer::m_Anchors</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_Anchors</div><div class="ttdoc">A unique pointer to store Anchor values. </div><div class="ttdef"><b>Definition:</b> <a href="_detection_post_process_layer_8hpp_source.xhtml#l00020">DetectionPostProcessLayer.hpp:20</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a></div><div class="ttdoc">An LstmDescriptor for the LstmLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00911">Descriptors.hpp:911</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Pooling2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00367">Descriptors.hpp:367</a></div></div>
<div class="ttc" id="_optimizer_tests_8cpp_xhtml_aa4e793c84e5dfea800d4dba921651e5b"><div class="ttname"><a href="_optimizer_tests_8cpp.xhtml#aa4e793c84e5dfea800d4dba921651e5b">CreateGatherGraph</a></div><div class="ttdeci">void CreateGatherGraph(Graph &graph, const armnn::TensorInfo &paramsInfo, const armnn::TensorInfo &indicesInfo, const armnn::TensorInfo &outputInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_tests_8cpp_source.xhtml#l00449">OptimizerTests.cpp:449</a></div></div>
<div class="ttc" id="_graph_8hpp_xhtml"><div class="ttname"><a href="_graph_8hpp.xhtml">Graph.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_backend_internal_xhtml_a693b40e6b94e958836aeb0410ca186bd"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a></div><div class="ttdeci">std::shared_ptr< IMemoryManager > IMemoryManagerSharedPtr</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.xhtml#l00092">IBackendInternal.hpp:92</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a701cecec7714cf8bc9dca804f473610d"><div class="ttname"><a href="namespacearmnn.xhtml#a701cecec7714cf8bc9dca804f473610d">armnn::IsOutputSupported</a></div><div class="ttdeci">bool IsOutputSupported(const BackendId &backend, const TensorInfo &output, char *reasonIfUnsupported=nullptr, size_t reasonIfUnsupportedMaxLength=1024)</div><div class="ttdoc">Deprecated in favor of IBackend and ILayerSupport interfaces. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_support_8cpp_source.xhtml#l00471">LayerSupport.cpp:471</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00194">Tensor.hpp:194</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div>
<div class="ttc" id="structarmnn_1_1_optimization_result_xhtml_a955b65059e7f9429a5d6041136bc1487"><div class="ttname"><a href="structarmnn_1_1_optimization_result.xhtml#a955b65059e7f9429a5d6041136bc1487">armnn::OptimizationResult::IsOk</a></div><div class="ttdeci">bool IsOk() const</div><div class="ttdef"><b>Definition:</b> <a href="_network_8hpp_source.xhtml#l00304">Network.hpp:304</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_opt_peephole_parameters_xhtml_a658f4245732f95c9fe756a934d370ca8"><div class="ttname"><a href="structarmnn_1_1_lstm_opt_peephole_parameters.xhtml#a658f4245732f95c9fe756a934d370ca8">armnn::LstmOptPeepholeParameters::m_CellToInputWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_CellToInputWeights</div><div class="ttdoc">A unique pointer to represent 1D weights tensor with dimensions [num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00047">LstmLayer.hpp:47</a></div></div>
<div class="ttc" id="classarmnn_1_1_convolution2d_layer_xhtml_a2664044e28e69309ea08ef385fe53903"><div class="ttname"><a href="classarmnn_1_1_convolution2d_layer.xhtml#a2664044e28e69309ea08ef385fe53903">armnn::Convolution2dLayer::m_Weight</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_Weight</div><div class="ttdoc">A unique pointer to store Weight values. </div><div class="ttdef"><b>Definition:</b> <a href="_convolution2d_layer_8hpp_source.xhtml#l00020">Convolution2dLayer.hpp:20</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="classarmnn_1_1_layer_xhtml_a9a97cb6d32661a57fc33bd29b8e41ff4"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a9a97cb6d32661a57fc33bd29b8e41ff4">armnn::Layer::GetNameStr</a></div><div class="ttdeci">const std::string & GetNameStr() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00220">Layer.hpp:220</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="structarmnn_1_1_resize_descriptor_xhtml_adcf5037208faac36c0788239a073f75c"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">armnn::ResizeDescriptor::m_TargetWidth</a></div><div class="ttdeci">uint32_t m_TargetWidth</div><div class="ttdoc">Target width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00816">Descriptors.hpp:816</a></div></div>
<div class="ttc" id="structarmnn_1_1_gather_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_gather_descriptor.xhtml">armnn::GatherDescriptor</a></div><div class="ttdoc">A GatherDescriptor for the GatherLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00742">Descriptors.hpp:742</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a2837b4396f20c956952d1a7286cab5f8"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">armnn::LstmDescriptor::m_PeepholeEnabled</a></div><div class="ttdeci">bool m_PeepholeEnabled</div><div class="ttdoc">Enable/disable peephole. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00943">Descriptors.hpp:943</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a></div></div>
<div class="ttc" id="classarmnn_1_1_mem_copy_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_mem_copy_layer.xhtml">armnn::MemCopyLayer</a></div><div class="ttdoc">This layer represents a memory copy operation. </div><div class="ttdef"><b>Definition:</b> <a href="_mem_copy_layer_8hpp_source.xhtml#l00013">MemCopyLayer.hpp:13</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml_a5698be69cbd5dfe6c28fcd9867e8cbed"><div class="ttname"><a href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a></div><div class="ttdeci">#define ARMNN_ASSERT(COND)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00014">Assert.hpp:14</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="namespacearmnn_xhtml_a58bfb9626d373249745d78b95543116e"><div class="ttname"><a href="namespacearmnn.xhtml#a58bfb9626d373249745d78b95543116e">armnn::IsActivationSupported</a></div><div class="ttdeci">bool IsActivationSupported(const BackendId &backend, const TensorInfo &input, const TensorInfo &output, const ActivationDescriptor &descriptor, char *reasonIfUnsupported=nullptr, size_t reasonIfUnsupportedMaxLength=1024)</div><div class="ttdoc">Deprecated in favor of IBackend and ILayerSupport interfaces. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_support_8cpp_source.xhtml#l00069">LayerSupport.cpp:69</a></div></div>
<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a></div><div class="ttdoc">An ActivationDescriptor for the ActivationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00025">Descriptors.hpp:25</a></div></div>
<div class="ttc" id="structarmnn_1_1_optimization_result_xhtml"><div class="ttname"><a href="structarmnn_1_1_optimization_result.xhtml">armnn::OptimizationResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_network_8hpp_source.xhtml#l00290">Network.hpp:290</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f">armnn::LayerType::Addition</a></div></div>
<div class="ttc" id="classarmnn_1_1_floor_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_floor_layer.xhtml">armnn::FloorLayer</a></div><div class="ttdoc">This layer represents a floor operation. </div><div class="ttdef"><b>Definition:</b> <a href="_floor_layer_8hpp_source.xhtml#l00013">FloorLayer.hpp:13</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_opt_cifg_parameters_xhtml_a6e8971757790a032e5936da7847ba14b"><div class="ttname"><a href="structarmnn_1_1_lstm_opt_cifg_parameters.xhtml#a6e8971757790a032e5936da7847ba14b">armnn::LstmOptCifgParameters::m_RecurrentToInputWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_RecurrentToInputWeights</div><div class="ttdoc">A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00031">LstmLayer.hpp:31</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a46c3fa15c46fb0d1dcdc24d0ea5cb5cd"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">armnn::ResizeDescriptor::m_TargetHeight</a></div><div class="ttdeci">uint32_t m_TargetHeight</div><div class="ttdoc">Target height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00818">Descriptors.hpp:818</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_ae1b07ed928036004bd257169e5aeeef4"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">armnn::LstmDescriptor::m_ActivationFunc</a></div><div class="ttdeci">uint32_t m_ActivationFunc</div><div class="ttdoc">The activation function to use. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00935">Descriptors.hpp:935</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_visitor_base_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer_visitor_base.xhtml">armnn::LayerVisitorBase</a></div><div class="ttdoc">Visitor base class with empty implementations. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_visitor_base_8hpp_source.xhtml#l00025">LayerVisitorBase.hpp:25</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00448">Descriptors.hpp:448</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="classarmnn_1_1_optimized_network_impl_xhtml"><div class="ttname"><a href="classarmnn_1_1_optimized_network_impl.xhtml">armnn::OptimizedNetworkImpl</a></div><div class="ttdef"><b>Definition:</b> <a href="_optimized_network_impl_8hpp_source.xhtml#l00009">OptimizedNetworkImpl.hpp:9</a></div></div>
<div class="ttc" id="classarmnn_1_1_pooling2d_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_pooling2d_layer.xhtml">armnn::Pooling2dLayer</a></div><div class="ttdoc">This layer represents a pooling 2d operation. </div><div class="ttdef"><b>Definition:</b> <a href="_pooling2d_layer_8hpp_source.xhtml#l00013">Pooling2dLayer.hpp:13</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a531a3907ec13d3772370da88030191a5"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">armnn::LstmDescriptor::m_ClippingThresCell</a></div><div class="ttdeci">float m_ClippingThresCell</div><div class="ttdoc">Clipping threshold value for the cell state. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00937">Descriptors.hpp:937</a></div></div>
<div class="ttc" id="_i_network_8hpp_xhtml"><div class="ttname"><a href="_i_network_8hpp.xhtml">INetwork.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Pooling2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00385">Descriptors.hpp:385</a></div></div>
<div class="ttc" id="classarmnn_1_1_addition_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_addition_layer.xhtml">armnn::AdditionLayer</a></div><div class="ttdoc">This layer represents an addition operation. </div><div class="ttdef"><b>Definition:</b> <a href="_addition_layer_8hpp_source.xhtml#l00013">AdditionLayer.hpp:13</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_basic_parameters_xhtml_a91dda74af4085ae43913746ad817795a"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#a91dda74af4085ae43913746ad817795a">armnn::LstmBasicParameters::m_RecurrentToOutputWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_RecurrentToOutputWeights</div><div class="ttdoc">A unique pointer to represent 2D weights tensor with dimensions [output_size, num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00067">LstmLayer.hpp:67</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="classarmnn_1_1_lstm_layer_xhtml_a4efa0f4d46817ab94e36c8507c26f276"><div class="ttname"><a href="classarmnn_1_1_lstm_layer.xhtml#a4efa0f4d46817ab94e36c8507c26f276">armnn::LstmLayer::m_PeepholeParameters</a></div><div class="ttdeci">LstmOptPeepholeParameters m_PeepholeParameters</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00084">LstmLayer.hpp:84</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_handler_xhtml_a97db12c41024f5545ef5cc4153e5443b"><div class="ttname"><a href="classarmnn_1_1_output_handler.xhtml#a97db12c41024f5545ef5cc4153e5443b">armnn::OutputHandler::SetTensorInfo</a></div><div class="ttdeci">void SetTensorInfo(const TensorInfo &tensorInfo)</div><div class="ttdoc">Sets the TensorInfo used by this output handler. </div><div class="ttdef"><b>Definition:</b> <a href="_output_handler_8cpp_source.xhtml#l00015">OutputHandler.cpp:15</a></div></div>
<div class="ttc" id="_cpu_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="_cpu_tensor_handle_8hpp.xhtml">CpuTensorHandle.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_backend_internal_xhtml_a11fa919c11fe46aad613b2e960fcfe90"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml#a11fa919c11fe46aad613b2e960fcfe90">armnn::IBackendInternal::ILayerSupportSharedPtr</a></div><div class="ttdeci">std::shared_ptr< ILayerSupport > ILayerSupportSharedPtr</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.xhtml#l00087">IBackendInternal.hpp:87</a></div></div>
<div class="ttc" id="classarmnn_1_1_lstm_layer_xhtml_a3d3e6d0c3e6e570d9f831489c3bd14ce"><div class="ttname"><a href="classarmnn_1_1_lstm_layer.xhtml#a3d3e6d0c3e6e570d9f831489c3bd14ce">armnn::LstmLayer::m_ProjectionParameters</a></div><div class="ttdeci">LstmOptProjectionParameters m_ProjectionParameters</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00083">LstmLayer.hpp:83</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_opt_projection_parameters_xhtml_aa9f2880e4e2a1eb731f61c1e0941c6a7"><div class="ttname"><a href="structarmnn_1_1_lstm_opt_projection_parameters.xhtml#aa9f2880e4e2a1eb731f61c1e0941c6a7">armnn::LstmOptProjectionParameters::m_ProjectionBias</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_ProjectionBias</div><div class="ttdoc">A unique pointer to represent 1D weights tensor with dimensions [output_size]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00041">LstmLayer.hpp:41</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="structarmnn_1_1_lstm_descriptor_xhtml_ad474e5c51a0b194ef32e812b86c0cbdb"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">armnn::LstmDescriptor::m_CifgEnabled</a></div><div class="ttdeci">bool m_CifgEnabled</div><div class="ttdoc">Enable/disable cifg (coupled input & forget gate). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00941">Descriptors.hpp:941</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_basic_parameters_xhtml_a14ab2bc78421c417c4f97a65b0bd78f9"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#a14ab2bc78421c417c4f97a65b0bd78f9">armnn::LstmBasicParameters::m_InputToCellWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_InputToCellWeights</div><div class="ttdoc">A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00059">LstmLayer.hpp:59</a></div></div>
<div class="ttc" id="structarmnn_1_1_empty_optional_xhtml"><div class="ttname"><a href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a></div><div class="ttdoc">EmptyOptional is used to initialize the Optional class in case we want to have default value for an O...</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00032">Optional.hpp:32</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a0031997bf43bd2747656c31e4977793a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">armnn::Pooling2dDescriptor::m_PoolType</a></div><div class="ttdeci">PoolingAlgorithm m_PoolType</div><div class="ttdoc">The pooling algorithm to use (Max. Average, L2). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00363">Descriptors.hpp:363</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::DepthwiseConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00500">Descriptors.hpp:500</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_a2ceda8d369e861997d558fac74d79c33"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a2ceda8d369e861997d558fac74d79c33">armnn::Graph::InferTensorInfos</a></div><div class="ttdeci">void InferTensorInfos()</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8cpp_source.xhtml#l00529">Graph.cpp:529</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="classarmnn_1_1_layer_xhtml_af2c0edc7ea62a8baaec4d3d9b2b09256"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#af2c0edc7ea62a8baaec4d3d9b2b09256">armnn::Layer::GetOutputHandler</a></div><div class="ttdeci">const OutputHandler & GetOutputHandler(unsigned int i=0) const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00225">Layer.hpp:225</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f"><div class="ttname"><a href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f">armnn::ResizeMethod::Bilinear</a></div></div>
<div class="ttc" id="classarmnn_1_1_input_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_input_layer.xhtml">armnn::InputLayer</a></div><div class="ttdoc">A layer user-provided data can be bound to (e.g. inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_input_layer_8hpp_source.xhtml#l00013">InputLayer.hpp:13</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_ab45dae688fc5d8983727abffa4389003"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#ab45dae688fc5d8983727abffa4389003">armnn::Graph::end</a></div><div class="ttdeci">Iterator end()</div><div class="ttdoc">Returns iterator pointing to the end of the list. Lowercase for range-based for loops. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00164">Graph.hpp:164</a></div></div>
<div class="ttc" id="_network_8hpp_xhtml"><div class="ttname"><a href="_network_8hpp.xhtml">Network.hpp</a></div></div>
<div class="ttc" id="_test_utils_8hpp_xhtml_a0eedb278f57355b47fa983450d4e378c"><div class="ttname"><a href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a></div><div class="ttdeci">bool CheckSequence(const armnn::Graph::ConstIterator first, const armnn::Graph::ConstIterator last)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8hpp_source.xhtml#l00021">TestUtils.hpp:21</a></div></div>
<div class="ttc" id="_optimizer_tests_8cpp_xhtml_a5065b32dd0aa2c08ef75e953ebedbc16"><div class="ttname"><a href="_optimizer_tests_8cpp.xhtml#a5065b32dd0aa2c08ef75e953ebedbc16">CreateConvolution2dGraph</a></div><div class="ttdeci">void CreateConvolution2dGraph(Graph &graph, const unsigned int *inputShape, const unsigned int *weightsShape, const unsigned int *outputShape, DataLayout dataLayout=DataLayout::NCHW)</div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_tests_8cpp_source.xhtml#l00258">OptimizerTests.cpp:258</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_a7e5c5771d741dd5473989047a9314728"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">armnn::OutputSlot::SetTensorInfo</a></div><div class="ttdeci">void SetTensorInfo(const TensorInfo &tensorInfo) override</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00058">Layer.cpp:58</a></div></div>
<div class="ttc" id="_neon_end_to_end_tests_8cpp_xhtml_a64c1dd1b6dd60be9f4a16db9c8f427a5"><div class="ttname"><a href="_neon_end_to_end_tests_8cpp.xhtml#a64c1dd1b6dd60be9f4a16db9c8f427a5">scoresInfo</a></div><div class="ttdeci">armnn::TensorInfo scoresInfo({ 1, 6, 3 }, armnn::DataType::Float32)</div></div>
<div class="ttc" id="structarmnn_1_1_lstm_opt_peephole_parameters_xhtml_a310e133b0b51b93a74b83008893792e9"><div class="ttname"><a href="structarmnn_1_1_lstm_opt_peephole_parameters.xhtml#a310e133b0b51b93a74b83008893792e9">armnn::LstmOptPeepholeParameters::m_CellToOutputWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_CellToOutputWeights</div><div class="ttdoc">A unique pointer to represent 1D weights tensor with dimensions [num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00051">LstmLayer.hpp:51</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#aea909c7327109228ef618d459015def3">armnn::Layer::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00283">Layer.cpp:283</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_a0e36688a43c35668d8db5257274c68fe"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">armnn::Layer::GetOutputSlot</a></div><div class="ttdeci">const OutputSlot & GetOutputSlot(unsigned int index=0) const override</div><div class="ttdoc">Get the const output slot handle by slot index. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00318">Layer.hpp:318</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_opt_projection_parameters_xhtml_a3ec2885c48ce888516e27c8b75a8cb83"><div class="ttname"><a href="structarmnn_1_1_lstm_opt_projection_parameters.xhtml#a3ec2885c48ce888516e27c8b75a8cb83">armnn::LstmOptProjectionParameters::m_ProjectionWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_ProjectionWeights</div><div class="ttdoc">A unique pointer to represent 2D weights tensor with dimensions [output_size, num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00039">LstmLayer.hpp:39</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
<div class="ttc" id="classarmnn_1_1_lstm_layer_xhtml_a0e940dfa428f4eb429f8bc0d138b20af"><div class="ttname"><a href="classarmnn_1_1_lstm_layer.xhtml#a0e940dfa428f4eb429f8bc0d138b20af">armnn::LstmLayer::m_CifgParameters</a></div><div class="ttdeci">LstmOptCifgParameters m_CifgParameters</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00082">LstmLayer.hpp:82</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_a02fd29b6dc3e21fbe4484362d85893bc"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a02fd29b6dc3e21fbe4484362d85893bc">armnn::Graph::cend</a></div><div class="ttdeci">ConstIterator cend() const</div><div class="ttdoc">Returns const iterator pointing to the end of the list. Lowercase for range-based for loops...</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00174">Graph.hpp:174</a></div></div>
<div class="ttc" id="classarmnn_1_1_convolution2d_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_convolution2d_layer.xhtml">armnn::Convolution2dLayer</a></div><div class="ttdoc">This layer represents a convolution 2d operation. </div><div class="ttdef"><b>Definition:</b> <a href="_convolution2d_layer_8hpp_source.xhtml#l00015">Convolution2dLayer.hpp:15</a></div></div>
<div class="ttc" id="_test_utils_8cpp_xhtml_a0b295acb179f15eb3fb862b32008f782"><div class="ttname"><a href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a></div><div class="ttdeci">void Connect(armnn::IConnectableLayer *from, armnn::IConnectableLayer *to, const armnn::TensorInfo &tensorInfo, unsigned int fromIndex, unsigned int toIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8cpp_source.xhtml#l00012">TestUtils.cpp:12</a></div></div>
<div class="ttc" id="_layer_visitor_base_8hpp_xhtml"><div class="ttname"><a href="_layer_visitor_base_8hpp.xhtml">LayerVisitorBase.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div>
<div class="ttc" id="classarmnn_1_1_depthwise_convolution2d_layer_xhtml_a2664044e28e69309ea08ef385fe53903"><div class="ttname"><a href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml#a2664044e28e69309ea08ef385fe53903">armnn::DepthwiseConvolution2dLayer::m_Weight</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_Weight</div><div class="ttdoc">A unique pointer to store Weight values. </div><div class="ttdef"><b>Definition:</b> <a href="_depthwise_convolution2d_layer_8hpp_source.xhtml#l00019">DepthwiseConvolution2dLayer.hpp:19</a></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="structarmnn_1_1_backend_settings_xhtml"><div class="ttname"><a href="structarmnn_1_1_backend_settings.xhtml">armnn::BackendSettings</a></div><div class="ttdef"><b>Definition:</b> <a href="_backend_settings_8hpp_source.xhtml#l00018">BackendSettings.hpp:18</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml_afdf8eb85585a798ad0e936bde884d87b"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#afdf8eb85585a798ad0e936bde884d87b">armnn::Graph::GetNumLayers</a></div><div class="ttdeci">size_t GetNumLayers() const</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00191">Graph.hpp:191</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::ResizeDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00823">Descriptors.hpp:823</a></div></div>
<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml">armnn::DetectionPostProcessDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00511">Descriptors.hpp:511</a></div></div>
<div class="ttc" id="_optimizer_tests_8cpp_xhtml_acd97facea671e23ec3e8b33c6c2ea321"><div class="ttname"><a href="_optimizer_tests_8cpp.xhtml#acd97facea671e23ec3e8b33c6c2ea321">CreateDepthwiseConvolution2dGraph</a></div><div class="ttdeci">void CreateDepthwiseConvolution2dGraph(Graph &graph, const unsigned int *inputShape, const unsigned int *weightsShape, const unsigned int *outputShape, DataLayout dataLayout=DataLayout::NCHW)</div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_tests_8cpp_source.xhtml#l00308">OptimizerTests.cpp:308</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa32a843da6ea40ab3b17a3421ccdf671b"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa32a843da6ea40ab3b17a3421ccdf671b">armnn::ActivationFunction::Linear</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_ada2ad7d1caeeb4ef6195c8925fad6a65"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#ada2ad7d1caeeb4ef6195c8925fad6a65">armnn::OutputSlot::GetTensorInfo</a></div><div class="ttdeci">const TensorInfo & GetTensorInfo() const override</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00063">Layer.cpp:63</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="structarmnn_1_1_lstm_basic_parameters_xhtml_a5a0d8af26a6aad1e5be521ea7dc550eb"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#a5a0d8af26a6aad1e5be521ea7dc550eb">armnn::LstmBasicParameters::m_InputToForgetWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_InputToForgetWeights</div><div class="ttdoc">A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00057">LstmLayer.hpp:57</a></div></div>
<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml_af10fa7883e3579950f477bee92a64844"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">armnn::ActivationDescriptor::m_Function</a></div><div class="ttdeci">ActivationFunction m_Function</div><div class="ttdoc">The activation function to use (Sigmoid, TanH, Linear, ReLu, BoundedReLu, SoftReLu, LeakyReLu, Abs, Sqrt, Square, Elu). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00048">Descriptors.hpp:48</a></div></div>
<div class="ttc" id="structarmnn_1_1_lstm_opt_cifg_parameters_xhtml_a9945bc99f8a7400c0724117e29cb3abb"><div class="ttname"><a href="structarmnn_1_1_lstm_opt_cifg_parameters.xhtml#a9945bc99f8a7400c0724117e29cb3abb">armnn::LstmOptCifgParameters::m_InputGateBias</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_InputGateBias</div><div class="ttdoc">A unique pointer to represent 1D weights tensor with dimensions [num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00033">LstmLayer.hpp:33</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Pooling2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00379">Descriptors.hpp:379</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a></div><div class="ttdoc">A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00460">Descriptors.hpp:460</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>
<div class="ttc" id="classarmnn_1_1_optimizer_xhtml"><div class="ttname"><a href="classarmnn_1_1_optimizer.xhtml">armnn::Optimizer</a></div><div class="ttdef"><b>Definition:</b> <a href="_optimizer_8hpp_source.xhtml#l00014">Optimizer.hpp:14</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml">armnn::BatchNormalizationDescriptor</a></div><div class="ttdoc">A BatchNormalizationDescriptor for the BatchNormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00626">Descriptors.hpp:626</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div>
<div class="ttc" id="classarmnn_1_1_resize_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_resize_layer.xhtml">armnn::ResizeLayer</a></div><div class="ttdoc">This layer represents a resize operation. </div><div class="ttdef"><b>Definition:</b> <a href="_resize_layer_8hpp_source.xhtml#l00013">ResizeLayer.hpp:13</a></div></div>
<div class="ttc" id="classarmnn_1_1_backend_id_xhtml"><div class="ttname"><a href="classarmnn_1_1_backend_id.xhtml">armnn::BackendId</a></div><div class="ttdef"><b>Definition:</b> <a href="_backend_id_8hpp_source.xhtml#l00075">BackendId.hpp:75</a></div></div>
<div class="ttc" id="_neon_end_to_end_tests_8cpp_xhtml_ac0981848e4ae57729f14f72bd4caa9f8"><div class="ttname"><a href="_neon_end_to_end_tests_8cpp.xhtml#ac0981848e4ae57729f14f72bd4caa9f8">anchors</a></div><div class="ttdeci">std::vector< float > anchors({ 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 10.5f, 1.0f, 1.0f, 0.5f, 10.5f, 1.0f, 1.0f, 0.5f, 100.5f, 1.0f, 1.0f })</div></div>
<div class="ttc" id="classarmnn_1_1_i_backend_internal_xhtml_ada6d56575c0fe53cf23c7ae4610c6367"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml#ada6d56575c0fe53cf23c7ae4610c6367">armnn::IBackendInternal::IBackendContextPtr</a></div><div class="ttdeci">std::unique_ptr< IBackendContext > IBackendContextPtr</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.xhtml#l00081">IBackendInternal.hpp:81</a></div></div>
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