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<div class="title">FuseBatchNorm.hpp</div> </div>
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<a href="_fuse_batch_norm_8hpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div>
<div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2020,2022 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">#pragma once</span></div>
<div class="line"><a name="l00007"></a><span class="lineno"> 7</span>  </div>
<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include "<a class="code" href="_optimization_8hpp.html">Optimization.hpp</a>"</span></div>
<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_data_layout_indexed_8hpp.html">armnnUtils/DataLayoutIndexed.hpp</a>></span></div>
<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_resolve_type_8hpp.html">ResolveType.hpp</a>></span></div>
<div class="line"><a name="l00011"></a><span class="lineno"> 11</span>  </div>
<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.html">armnn</a></div>
<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> {</div>
<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="keyword">namespace </span>optimizations</div>
<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> {</div>
<div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  </div>
<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="keyword">template</span><<span class="keyword">typename</span> ConvLayer, <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnType,</div>
<div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  <span class="keyword">typename</span> T = <a class="code" href="namespacearmnn.html#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnType></a>></div>
<div class="line"><a name="l00019"></a><span class="lineno"><a class="line" href="classarmnn_1_1optimizations_1_1_fuse_batch_norm.html"> 19</a></span> <span class="keyword">class </span><a class="code" href="classarmnn_1_1optimizations_1_1_fuse_batch_norm.html">FuseBatchNorm</a></div>
<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> {</div>
<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="keyword">public</span>:<span class="comment"></span></div>
<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> /// Run for every exclusive connection between any base Convolution layer and a child BatchNorm layer for not</span></div>
<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> /// quantized layers.</span></div>
<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="comment"> /// The child will be removed, the base will be removed if it's left unconnected. A new Convolution layer will</span></div>
<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="comment"> /// be added, its weights and bias will be calculated using the weights and bias of the base Convolution layer</span></div>
<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="comment"> /// combined with the parameters of the child BatchNorm layer.</span></div>
<div class="line"><a name="l00027"></a><span class="lineno"><a class="line" href="classarmnn_1_1optimizations_1_1_fuse_batch_norm.html#a5a8476ffc04ce7460bb09ad50d1d23de"> 27</a></span> <span class="comment"></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1optimizations_1_1_fuse_batch_norm.html#a5a8476ffc04ce7460bb09ad50d1d23de">Run</a>(<a class="code" href="classarmnn_1_1_graph.html">Graph</a>& graph, <a class="code" href="classarmnn_1_1_input_slot.html">InputSlot</a>& connection)<span class="keyword"> const</span></div>
<div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="keyword"> </span>{</div>
<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <a class="code" href="classarmnn_1_1_layer.html">Layer</a>& base = connection.<a class="code" href="classarmnn_1_1_input_slot.html#a9effd325a6d512a3f8ff4bd207d53255">GetConnectedOutputSlot</a>()-><a class="code" href="classarmnn_1_1_output_slot.html#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</a>();</div>
<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <a class="code" href="classarmnn_1_1_layer.html">Layer</a>& child = connection.<a class="code" href="classarmnn_1_1_input_slot.html#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</a>();</div>
<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  </div>
<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keywordtype">bool</span> depthwise = (base.<a class="code" href="classarmnn_1_1_layer.html#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4af97adbfc88b7012a0243215b1076e7e7">LayerType::DepthwiseConvolution2d</a>);</div>
<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  </div>
<div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <a class="code" href="_assert_8hpp.html#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(base.<a class="code" href="classarmnn_1_1_layer.html#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a">LayerType::Convolution2d</a> || depthwise);</div>
<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <a class="code" href="_assert_8hpp.html#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(child.<a class="code" href="classarmnn_1_1_layer.html#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4ae4743c3ec15d1d84169b17264634692e">LayerType::BatchNormalization</a>);</div>
<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  </div>
<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordflow">if</span> (base.<a class="code" href="classarmnn_1_1_layer.html#aea909c7327109228ef618d459015def3">GetDataType</a>() == ArmnnType && child.<a class="code" href="classarmnn_1_1_layer.html#aea909c7327109228ef618d459015def3">GetDataType</a>() == ArmnnType)</div>
<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  {</div>
<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <a class="code" href="classarmnn_1_1_output_slot.html">OutputSlot</a>* parentOut = base.<a class="code" href="classarmnn_1_1_layer.html#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0).<a class="code" href="classarmnn_1_1_input_slot.html#a9effd325a6d512a3f8ff4bd207d53255">GetConnectedOutputSlot</a>();</div>
<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="keyword">auto</span> convLayer = PolymorphicDowncast<ConvLayer*>(&base);</div>
<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keyword">auto</span> batchNormLayer = PolymorphicDowncast<BatchNormalizationLayer*>(&child);</div>
<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  </div>
<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="comment">// Read convolution and batch norm parameters</span></div>
<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.html">BatchNormalizationDescriptor</a> batchNormDescriptor = batchNormLayer->GetParameters();</div>
<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keyword">auto</span> epsilon = batchNormDescriptor.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.html#a11c821c7524251004a72ed13c510853c">m_Eps</a>;</div>
<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <a class="code" href="namespacearmnn.html#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(epsilon);</div>
<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  </div>
<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <a class="code" href="classarmnn_1_1_const_tensor.html">ConstTensor</a> betaTensor(batchNormLayer->m_Beta->GetTensorInfo(), batchNormLayer->m_Beta->Map(<span class="keyword">true</span>));</div>
<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <a class="code" href="classarmnn_1_1_const_tensor.html">ConstTensor</a> gammaTensor(batchNormLayer->m_Gamma->GetTensorInfo(), batchNormLayer->m_Gamma->Map(<span class="keyword">true</span>));</div>
<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <a class="code" href="classarmnn_1_1_const_tensor.html">ConstTensor</a> meanTensor(batchNormLayer->m_Mean->GetTensorInfo(), batchNormLayer->m_Mean->Map(<span class="keyword">true</span>));</div>
<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <a class="code" href="classarmnn_1_1_const_tensor.html">ConstTensor</a> varTensor(batchNormLayer->m_Variance->GetTensorInfo(), batchNormLayer->m_Variance->Map(<span class="keyword">true</span>));</div>
<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  </div>
<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keyword">auto</span> convDescriptor = convLayer->GetParameters();</div>
<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <a class="code" href="classarmnn_1_1_const_tensor.html">ConstTensor</a> weightsTensor;</div>
<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <a class="code" href="_assert_8hpp.html#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(convLayer->GetInputSlots()[1].GetConnection() != <span class="keyword">nullptr</span>,</div>
<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="stringliteral">"FuseBatchNorm: Weight data should not be null."</span>);</div>
<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  </div>
<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <a class="code" href="classarmnn_1_1_constant_layer.html">ConstantLayer</a>* weightLayer = PolymorphicDowncast<ConstantLayer*>(</div>
<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  &base.<a class="code" href="classarmnn_1_1_layer.html#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(1).<a class="code" href="classarmnn_1_1_input_slot.html#a9effd325a6d512a3f8ff4bd207d53255">GetConnectedOutputSlot</a>()-><a class="code" href="classarmnn_1_1_output_slot.html#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</a>());</div>
<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  </div>
<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  weightsTensor = <a class="code" href="classarmnn_1_1_const_tensor.html">ConstTensor</a>(weightLayer-><a class="code" href="classarmnn_1_1_constant_layer.html#ad0c4b8ee0efd8f9336571cbeab8a53fe">m_LayerOutput</a>->GetTensorInfo(),</div>
<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  weightLayer-><a class="code" href="classarmnn_1_1_constant_layer.html#ad0c4b8ee0efd8f9336571cbeab8a53fe">m_LayerOutput</a>->Map(<span class="keyword">true</span>));</div>
<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  </div>
<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.html">armnnUtils::DataLayoutIndexed</a> dataLayout(convDescriptor.m_DataLayout);</div>
<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keyword">auto</span> weightsShape = weightsTensor.<a class="code" href="classarmnn_1_1_base_tensor.html#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div>
<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = parentOut-><a class="code" href="classarmnn_1_1_output_slot.html#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[dataLayout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.html#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>()];</div>
<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depthMultiplier = depthwise ? weightsShape[3] / inputChannels : 1;</div>
<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = depthwise ? weightsShape[3] : weightsShape[0];</div>
<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsHeight = depthwise ? weightsShape[1] :</div>
<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  weightsShape[dataLayout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.html#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>()];</div>
<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsWidth = depthwise ? weightsShape[2] :</div>
<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  weightsShape[dataLayout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.html#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>()];</div>
<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  </div>
<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* weightsBuffer = <span class="keyword">static_cast<</span><span class="keyword">const </span>T*<span class="keyword">></span>(weightsTensor.<a class="code" href="classarmnn_1_1_base_tensor.html#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>());</div>
<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* betaBuffer = <span class="keyword">static_cast<</span><span class="keyword">const </span>T*<span class="keyword">></span>(betaTensor.<a class="code" href="classarmnn_1_1_base_tensor.html#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>());</div>
<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* gammaBuffer = <span class="keyword">static_cast<</span><span class="keyword">const </span>T*<span class="keyword">></span>(gammaTensor.<a class="code" href="classarmnn_1_1_base_tensor.html#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>());</div>
<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* meanBuffer = <span class="keyword">static_cast<</span><span class="keyword">const </span>T*<span class="keyword">></span>(meanTensor.<a class="code" href="classarmnn_1_1_base_tensor.html#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>());</div>
<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* varBuffer = <span class="keyword">static_cast<</span><span class="keyword">const </span>T*<span class="keyword">></span>(varTensor.<a class="code" href="classarmnn_1_1_base_tensor.html#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>());</div>
<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  </div>
<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  std::vector<T> weightsVector (weightsBuffer, weightsBuffer + weightsTensor.<a class="code" href="classarmnn_1_1_base_tensor.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div>
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  std::vector<T> betaVector (betaBuffer, betaBuffer + betaTensor.<a class="code" href="classarmnn_1_1_base_tensor.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div>
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  std::vector<T> gammaVector (gammaBuffer, gammaBuffer + gammaTensor.<a class="code" href="classarmnn_1_1_base_tensor.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div>
<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  std::vector<T> meanVector (meanBuffer, meanBuffer + meanTensor.<a class="code" href="classarmnn_1_1_base_tensor.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div>
<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  std::vector<T> varianceVector(varBuffer, varBuffer + varTensor.<a class="code" href="classarmnn_1_1_base_tensor.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div>
<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  </div>
<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="comment">// fusedWeights = ( gamma * weights ) / ( std - epsilon);</span></div>
<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  std::vector<T> fusedWeightsVector(weightsVector.size());</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">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> cInput = 0; cInput < inputChannels; ++cInput)</div>
<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  {</div>
<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> cOut = 0; cOut < outputChannels; ++cOut)</div>
<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  {</div>
<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  T mult = gammaVector[cOut] / <span class="keyword">static_cast<</span>T<span class="keyword">></span>(sqrtf(varianceVector[cOut] + epsilon));</div>
<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  </div>
<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> h = 0; h < weightsHeight; ++h)</div>
<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  {</div>
<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> w = 0; w < weightsWidth; ++w)</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsIdx = 0;</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> (depthwise)</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>  cInput = cOut / depthMultiplier;</div>
<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  weightsIdx = w * outputChannels + cOut +</div>
<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  h * weightsWidth * outputChannels;</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (convDescriptor.m_DataLayout == <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>)</div>
<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  {</div>
<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  weightsIdx = cOut * weightsHeight * weightsWidth * inputChannels +</div>
<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  h * weightsWidth * inputChannels +</div>
<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  w * inputChannels +</div>
<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  cInput;</div>
<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  }</div>
<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  {</div>
<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  weightsIdx = cOut * weightsWidth * weightsHeight * inputChannels +</div>
<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  cInput * weightsWidth * weightsHeight +</div>
<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  h * weightsWidth +</div>
<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  w;</div>
<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  }</div>
<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  fusedWeightsVector[weightsIdx] = mult * weightsVector[weightsIdx];</div>
<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  }</div>
<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  }</div>
<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  }</div>
<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  }</div>
<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <a class="code" href="classarmnn_1_1_const_tensor.html">ConstTensor</a> fusedWeightsTensor(weightsTensor.<a class="code" href="classarmnn_1_1_base_tensor.html#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>(), fusedWeightsVector);</div>
<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  </div>
<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="comment">// fusedBias = (gamma * (bias - mean)) / (variance - epsilon) + beta;</span></div>
<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  std::vector<T> fusedBiasVector(outputChannels);</div>
<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordtype">bool</span> biasWasEnabledBeforeOpt = convDescriptor.m_BiasEnabled;</div>
<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordflow">if</span> (biasWasEnabledBeforeOpt)</div>
<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  {</div>
<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <a class="code" href="classarmnn_1_1_const_tensor.html">ConstTensor</a> biasTensor;</div>
<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <a class="code" href="_assert_8hpp.html#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(convLayer->GetInputSlots()[2].GetConnection() != <span class="keyword">nullptr</span>,</div>
<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="stringliteral">"FuseBatchNorm: Bias data should not be null if bias is enabled."</span>);</div>
<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  </div>
<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <a class="code" href="classarmnn_1_1_constant_layer.html">ConstantLayer</a>* biasLayer = PolymorphicDowncast<ConstantLayer*>(</div>
<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  &base.<a class="code" href="classarmnn_1_1_layer.html#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(2).<a class="code" href="classarmnn_1_1_input_slot.html#a9effd325a6d512a3f8ff4bd207d53255">GetConnectedOutputSlot</a>()-><a class="code" href="classarmnn_1_1_output_slot.html#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</a>());</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>  biasTensor = <a class="code" href="classarmnn_1_1_const_tensor.html">ConstTensor</a>(biasLayer-><a class="code" href="classarmnn_1_1_constant_layer.html#ad0c4b8ee0efd8f9336571cbeab8a53fe">m_LayerOutput</a>->GetTensorInfo(),</div>
<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  biasLayer-><a class="code" href="classarmnn_1_1_constant_layer.html#ad0c4b8ee0efd8f9336571cbeab8a53fe">m_LayerOutput</a>->Map(<span class="keyword">true</span>));</div>
<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  </div>
<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* biasBuffer = <span class="keyword">static_cast<</span><span class="keyword">const </span>T*<span class="keyword">></span>(biasTensor.<a class="code" href="classarmnn_1_1_base_tensor.html#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>());</div>
<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  std::vector<T> biasVector(biasBuffer, biasBuffer + biasTensor.<a class="code" href="classarmnn_1_1_base_tensor.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div>
<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  </div>
<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> cOut = 0; cOut < outputChannels; ++cOut)</div>
<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  {</div>
<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  fusedBiasVector[cOut] = ((gammaVector[cOut] * (biasVector[cOut] - meanVector[cOut])) /</div>
<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  sqrtf(varianceVector[cOut] + epsilon)) + betaVector[cOut];</div>
<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  }</div>
<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  }</div>
<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  {</div>
<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  convDescriptor.m_BiasEnabled = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  std::vector<T> biasVector(outputChannels, T(0));</div>
<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  </div>
<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> cOut = 0; cOut < outputChannels; ++cOut)</div>
<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  {</div>
<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  fusedBiasVector[cOut] = ((gammaVector[cOut] * (biasVector[cOut] - meanVector[cOut])) /</div>
<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  sqrtf(varianceVector[cOut] + epsilon)) + betaVector[cOut];</div>
<div class="line"><a name="l00161"></a><span class="lineno"> 161</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>  <a class="code" href="classarmnn_1_1_const_tensor.html">ConstTensor</a> fusedBiasTensor(<a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>({outputChannels}, ArmnnType, 0.0f, 0, <span class="keyword">true</span>), fusedBiasVector);</div>
<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  </div>
<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="comment">// Insert the new convolution layer that has batch norm parameters fused into</span></div>
<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keyword">const</span> std::string name = std::string(<span class="stringliteral">"fused-"</span>) + child.<a class="code" href="classarmnn_1_1_layer.html#a7ddf0cf6f620d59c10e63495ace795d0">GetName</a>() + std::string(<span class="stringliteral">"-into-"</span>) + base.<a class="code" href="classarmnn_1_1_layer.html#a7ddf0cf6f620d59c10e63495ace795d0">GetName</a>();</div>
<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="keyword">auto</span>& newConv2dLayer = *graph.<a class="code" href="classarmnn_1_1_graph.html#a3ff30c6669fdc69de1f5be1f89bacc3f">InsertNewLayer</a><ConvLayer>(base.<a class="code" href="classarmnn_1_1_layer.html#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0),</div>
<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  convDescriptor,</div>
<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  name.c_str());</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>  <span class="comment">// Connect weights and bias from old to new Conv2d layer</span></div>
<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="comment">// This optimization will always have 3 input slots on the Conv2d base layer</span></div>
<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keywordflow">if</span> (newConv2dLayer.GetNumInputSlots() > 1)</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>  <span class="comment">// Remove old connection and connect to new layer2d</span></div>
<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  weightLayer-><a class="code" href="classarmnn_1_1_layer.html#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.html#ac72a192dfcfa19e6ce826f99b415a11d">Disconnect</a>(base.<a class="code" href="classarmnn_1_1_layer.html#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(1));</div>
<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  weightLayer-><a class="code" href="classarmnn_1_1_layer.html#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.html#adcfb97035799ea4c043f9ef370714815">Connect</a>(newConv2dLayer.GetInputSlot(1));</div>
<div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  weightLayer-><a class="code" href="classarmnn_1_1_constant_layer.html#ad0c4b8ee0efd8f9336571cbeab8a53fe">m_LayerOutput</a> = std::make_unique<ScopedTensorHandle>(fusedWeightsTensor);</div>
<div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  </div>
<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="comment">// Move bias const layers as normal if it was enabled before the optimisation</span></div>
<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <a class="code" href="classarmnn_1_1_constant_layer.html">ConstantLayer</a>* biasLayer;</div>
<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keywordflow">if</span> (biasWasEnabledBeforeOpt)</div>
<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  {</div>
<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  biasLayer = PolymorphicDowncast<ConstantLayer*>(</div>
<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  &base.<a class="code" href="classarmnn_1_1_layer.html#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(2).<a class="code" href="classarmnn_1_1_input_slot.html#a9effd325a6d512a3f8ff4bd207d53255">GetConnectedOutputSlot</a>()-><a class="code" href="classarmnn_1_1_output_slot.html#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</a>());</div>
<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="comment">// Remove old connection and connect to new layer2d</span></div>
<div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  biasLayer-><a class="code" href="classarmnn_1_1_layer.html#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.html#ac72a192dfcfa19e6ce826f99b415a11d">Disconnect</a>(base.<a class="code" href="classarmnn_1_1_layer.html#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(2));</div>
<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  biasLayer-><a class="code" href="classarmnn_1_1_layer.html#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.html#adcfb97035799ea4c043f9ef370714815">Connect</a>(newConv2dLayer.GetInputSlot(2));</div>
<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  </div>
<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  }</div>
<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="comment">// Otherwise create a new bias layer and add to the new convolution2d</span></div>
<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  {</div>
<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="comment">// Add in bias constant layer</span></div>
<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  biasLayer = graph.<a class="code" href="classarmnn_1_1_graph.html#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_constant_layer.html">ConstantLayer</a>>(<span class="stringliteral">"Bias"</span>);</div>
<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  biasLayer-><a class="code" href="classarmnn_1_1_layer.html#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.html#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(fusedBiasTensor.<a class="code" href="classarmnn_1_1_base_tensor.html#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>());</div>
<div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  biasLayer-><a class="code" href="classarmnn_1_1_layer.html#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.html#adcfb97035799ea4c043f9ef370714815">Connect</a>(newConv2dLayer.GetInputSlot(2));</div>
<div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  }</div>
<div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  biasLayer-><a class="code" href="classarmnn_1_1_constant_layer.html#ad0c4b8ee0efd8f9336571cbeab8a53fe">m_LayerOutput</a> = std::make_unique<ScopedTensorHandle>(<a class="code" href="classarmnn_1_1_const_tensor.html">ConstTensor</a>(fusedBiasTensor));</div>
<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  }</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>  </div>
<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="comment">// Reconnects with original parent.</span></div>
<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  newConv2dLayer.GetOutputSlot().MoveAllConnections(*parentOut);</div>
<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="comment">// Parent is now the new convolution2d layer.</span></div>
<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  parentOut = &newConv2dLayer.GetOutputSlot();</div>
<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  </div>
<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="comment">// Moves connections in child output to parent layer.</span></div>
<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="comment">// Child layer will be removed as it's left unconnected.</span></div>
<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="comment">// Base layer will be removed if left unconnected.</span></div>
<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  child.<a class="code" href="classarmnn_1_1_layer.html#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>().<a class="code" href="classarmnn_1_1_output_slot.html#a19d30f83e90f2612e6aec510715f790d">MoveAllConnections</a>(*parentOut);</div>
<div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  }</div>
<div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  }</div>
<div class="line"><a name="l00214"></a><span class="lineno"> 214</span> <span class="keyword">protected</span>:</div>
<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <a class="code" href="classarmnn_1_1optimizations_1_1_fuse_batch_norm.html#abe49327783cb8bdc12c085c987db14db">FuseBatchNorm</a>() = <span class="keywordflow">default</span>;</div>
<div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <a class="code" href="classarmnn_1_1optimizations_1_1_fuse_batch_norm.html#a0ff9a790927b898d90261a8ea0e479e6">~FuseBatchNorm</a>() = <span class="keywordflow">default</span>;</div>
<div class="line"><a name="l00217"></a><span class="lineno"> 217</span> };</div>
<div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  </div>
<div class="line"><a name="l00219"></a><span class="lineno"> 219</span> <span class="keyword">using</span> <a class="code" href="namespacearmnn_1_1optimizations.html#aa52c06792e18dc13030e82476f706f9e">FuseBatchNormIntoConvolution2DFloat32</a> =</div>
<div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <a class="code" href="classarmnn_1_1_optimize_for_exclusive_connection.html">OptimizeForExclusiveConnection</a><<a class="code" href="classarmnn_1_1_convolution2d_layer.html">Convolution2dLayer</a>,</div>
<div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <a class="code" href="classarmnn_1_1_batch_normalization_layer.html">BatchNormalizationLayer</a>,</div>
<div class="line"><a name="l00222"></a><span class="lineno"><a class="line" href="namespacearmnn_1_1optimizations.html#aa52c06792e18dc13030e82476f706f9e"> 222</a></span>  <a class="code" href="classarmnn_1_1optimizations_1_1_fuse_batch_norm.html">FuseBatchNorm<Convolution2dLayer, armnn::DataType::Float32></a>>;</div>
<div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  </div>
<div class="line"><a name="l00224"></a><span class="lineno"> 224</span> <span class="keyword">using</span> <a class="code" href="classarmnn_1_1_optimize_for_exclusive_connection.html">FuseBatchNormIntoConvolution2DFloat16</a> =</div>
<div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <a class="code" href="classarmnn_1_1_optimize_for_exclusive_connection.html">OptimizeForExclusiveConnection</a><<a class="code" href="classarmnn_1_1_convolution2d_layer.html">Convolution2dLayer</a>,</div>
<div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <a class="code" href="classarmnn_1_1_batch_normalization_layer.html">BatchNormalizationLayer</a>,</div>
<div class="line"><a name="l00227"></a><span class="lineno"><a class="line" href="namespacearmnn_1_1optimizations.html#a8a81178ddcebb93ec0c35b6e6284273c"> 227</a></span>  <a class="code" href="classarmnn_1_1optimizations_1_1_fuse_batch_norm.html">FuseBatchNorm<Convolution2dLayer, armnn::DataType::Float16></a>>;</div>
<div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  </div>
<div class="line"><a name="l00229"></a><span class="lineno"> 229</span> <span class="keyword">using</span> <a class="code" href="classarmnn_1_1_optimize_for_exclusive_connection.html">FuseBatchNormIntoDepthwiseConvolution2DFloat32</a> =</div>
<div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <a class="code" href="classarmnn_1_1_optimize_for_exclusive_connection.html">OptimizeForExclusiveConnection</a><<a class="code" href="classarmnn_1_1_depthwise_convolution2d_layer.html">DepthwiseConvolution2dLayer</a>,</div>
<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <a class="code" href="classarmnn_1_1_batch_normalization_layer.html">BatchNormalizationLayer</a>,</div>
<div class="line"><a name="l00232"></a><span class="lineno"><a class="line" href="namespacearmnn_1_1optimizations.html#a56e54a818166a2f4b2c1a7f76a3629ff"> 232</a></span>  <a class="code" href="classarmnn_1_1optimizations_1_1_fuse_batch_norm.html">FuseBatchNorm<DepthwiseConvolution2dLayer, armnn::DataType::Float32></a>>;</div>
<div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  </div>
<div class="line"><a name="l00234"></a><span class="lineno"> 234</span> <span class="keyword">using</span> <a class="code" href="classarmnn_1_1_optimize_for_exclusive_connection.html">FuseBatchNormIntoDepthwiseConvolution2DFloat16</a> =</div>
<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <a class="code" href="classarmnn_1_1_optimize_for_exclusive_connection.html">OptimizeForExclusiveConnection</a><<a class="code" href="classarmnn_1_1_depthwise_convolution2d_layer.html">DepthwiseConvolution2dLayer</a>,</div>
<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <a class="code" href="classarmnn_1_1_batch_normalization_layer.html">BatchNormalizationLayer</a>,</div>
<div class="line"><a name="l00237"></a><span class="lineno"><a class="line" href="namespacearmnn_1_1optimizations.html#ab40bb51feca46649eb9d00522bfe51f6"> 237</a></span>  <a class="code" href="classarmnn_1_1optimizations_1_1_fuse_batch_norm.html">FuseBatchNorm<DepthwiseConvolution2dLayer, armnn::DataType::Float16></a>>;</div>
<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  </div>
<div class="line"><a name="l00239"></a><span class="lineno"> 239</span> } <span class="comment">// namespace optimizations</span></div>
<div class="line"><a name="l00240"></a><span class="lineno"> 240</span> } <span class="comment">// namespace armnn</span></div>
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</div><!-- doc-content -->
<div class="ttc" id="a_assert_8hpp_html_a5698be69cbd5dfe6c28fcd9867e8cbed"><div class="ttname"><a href="_assert_8hpp.html#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a></div><div class="ttdeci">#define ARMNN_ASSERT(COND)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.html#l00014">Assert.hpp:14</a></div></div>
<div class="ttc" id="astructarmnn_1_1_batch_normalization_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.html">armnn::BatchNormalizationDescriptor</a></div><div class="ttdoc">A BatchNormalizationDescriptor for the BatchNormalizationLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00828">Descriptors.hpp:828</a></div></div>
<div class="ttc" id="anamespacearmnn_1_1optimizations_html_aa52c06792e18dc13030e82476f706f9e"><div class="ttname"><a href="namespacearmnn_1_1optimizations.html#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.html#l00222">FuseBatchNorm.hpp:222</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_base_tensor_html_aa81f67ac64f0c249e26499600c45d996"><div class="ttname"><a href="classarmnn_1_1_base_tensor.html#aa81f67ac64f0c249e26499600c45d996">armnn::BaseTensor::GetMemoryArea</a></div><div class="ttdeci">MemoryType GetMemoryArea() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00307">Tensor.hpp:307</a></div></div>
<div class="ttc" id="anamespacearmnn_html_a56943a0946e5f15e5e58054b8e7a04a4ae4743c3ec15d1d84169b17264634692e"><div class="ttname"><a href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4ae4743c3ec15d1d84169b17264634692e">armnn::LayerType::BatchNormalization</a></div><div class="ttdeci">@ BatchNormalization</div></div>
<div class="ttc" id="aclassarmnn_1_1optimizations_1_1_fuse_batch_norm_html_abe49327783cb8bdc12c085c987db14db"><div class="ttname"><a href="classarmnn_1_1optimizations_1_1_fuse_batch_norm.html#abe49327783cb8bdc12c085c987db14db">armnn::optimizations::FuseBatchNorm::FuseBatchNorm</a></div><div class="ttdeci">FuseBatchNorm()=default</div></div>
<div class="ttc" id="aclassarmnn_1_1_input_slot_html_a7ddaf04177053a536f0e7be83a642bc6"><div class="ttname"><a href="classarmnn_1_1_input_slot.html#a7ddaf04177053a536f0e7be83a642bc6">armnn::InputSlot::GetOwningLayer</a></div><div class="ttdeci">Layer & GetOwningLayer() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.html#l00053">Layer.hpp:53</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_output_slot_html_ada2ad7d1caeeb4ef6195c8925fad6a65"><div class="ttname"><a href="classarmnn_1_1_output_slot.html#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.html#l00092">Layer.cpp:92</a></div></div>
<div class="ttc" id="anamespacearmnn_html_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div><div class="ttdeci">@ NHWC</div></div>
<div class="ttc" id="aclassarmnn_1_1_output_slot_html"><div class="ttname"><a href="classarmnn_1_1_output_slot.html">armnn::OutputSlot</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.html#l00100">Layer.hpp:100</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_depthwise_convolution2d_layer_html"><div class="ttname"><a href="classarmnn_1_1_depthwise_convolution2d_layer.html">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.html#l00015">DepthwiseConvolution2dLayer.hpp:15</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_output_slot_html_a7e5c5771d741dd5473989047a9314728"><div class="ttname"><a href="classarmnn_1_1_output_slot.html#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.html#l00087">Layer.cpp:87</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_tensor_info_html"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="aclassarmnn_utils_1_1_data_layout_indexed_html"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.html">armnnUtils::DataLayoutIndexed</a></div><div class="ttdoc">Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout.</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.html#l00017">DataLayoutIndexed.hpp:17</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_layer_html_a0e36688a43c35668d8db5257274c68fe"><div class="ttname"><a href="classarmnn_1_1_layer.html#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.html#l00339">Layer.hpp:339</a></div></div>
<div class="ttc" id="a_resolve_type_8hpp_html"><div class="ttname"><a href="_resolve_type_8hpp.html">ResolveType.hpp</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_base_tensor_html_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_base_tensor.html#a8846406ac37fbd2204f0be16ee05d5b7">armnn::BaseTensor::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00305">Tensor.hpp:305</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_output_slot_html_adcfb97035799ea4c043f9ef370714815"><div class="ttname"><a href="classarmnn_1_1_output_slot.html#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.html#l00112">Layer.cpp:112</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_batch_normalization_layer_html"><div class="ttname"><a href="classarmnn_1_1_batch_normalization_layer.html">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.html#l00015">BatchNormalizationLayer.hpp:15</a></div></div>
<div class="ttc" id="a_assert_8hpp_html_a91c4dfde57907d7698c7531785690a7f"><div class="ttname"><a href="_assert_8hpp.html#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a></div><div class="ttdeci">#define ARMNN_ASSERT_MSG(COND, MSG)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.html#l00015">Assert.hpp:15</a></div></div>
<div class="ttc" id="a_optimization_8hpp_html"><div class="ttname"><a href="_optimization_8hpp.html">Optimization.hpp</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_layer_html_acf8b8e23bf647836592982f97088d375"><div class="ttname"><a href="classarmnn_1_1_layer.html#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.html#l00337">Layer.hpp:337</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_layer_html_a7ddf0cf6f620d59c10e63495ace795d0"><div class="ttname"><a href="classarmnn_1_1_layer.html#a7ddf0cf6f620d59c10e63495ace795d0">armnn::Layer::GetName</a></div><div class="ttdeci">const char * GetName() const override</div><div class="ttdoc">Returns the name of the layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.html#l00332">Layer.hpp:332</a></div></div>
<div class="ttc" id="aclassarmnn_utils_1_1_data_layout_indexed_html_a61c00316c443adc233c24e85c6c5b740"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.html#a61c00316c443adc233c24e85c6c5b740">armnnUtils::DataLayoutIndexed::GetHeightIndex</a></div><div class="ttdeci">unsigned int GetHeightIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.html#l00024">DataLayoutIndexed.hpp:24</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_convolution2d_layer_html"><div class="ttname"><a href="classarmnn_1_1_convolution2d_layer.html">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.html#l00015">Convolution2dLayer.hpp:15</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_layer_html"><div class="ttname"><a href="classarmnn_1_1_layer.html">armnn::Layer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.html#l00230">Layer.hpp:230</a></div></div>
<div class="ttc" id="aclassarmnn_1_1optimizations_1_1_fuse_batch_norm_html_a5a8476ffc04ce7460bb09ad50d1d23de"><div class="ttname"><a href="classarmnn_1_1optimizations_1_1_fuse_batch_norm.html#a5a8476ffc04ce7460bb09ad50d1d23de">armnn::optimizations::FuseBatchNorm::Run</a></div><div class="ttdeci">void Run(Graph &graph, InputSlot &connection) const</div><div class="ttdoc">Run for every exclusive connection between any base Convolution layer and a child BatchNorm layer for...</div><div class="ttdef"><b>Definition:</b> <a href="_fuse_batch_norm_8hpp_source.html#l00027">FuseBatchNorm.hpp:27</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_output_slot_html_a7ddaf04177053a536f0e7be83a642bc6"><div class="ttname"><a href="classarmnn_1_1_output_slot.html#a7ddaf04177053a536f0e7be83a642bc6">armnn::OutputSlot::GetOwningLayer</a></div><div class="ttdeci">Layer & GetOwningLayer() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.html#l00132">Layer.hpp:132</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_output_slot_html_ac72a192dfcfa19e6ce826f99b415a11d"><div class="ttname"><a href="classarmnn_1_1_output_slot.html#ac72a192dfcfa19e6ce826f99b415a11d">armnn::OutputSlot::Disconnect</a></div><div class="ttdeci">void Disconnect(InputSlot &slot)</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.html#l00120">Layer.cpp:120</a></div></div>
<div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00048">Types.hpp:48</a></div></div>
<div class="ttc" id="anamespacearmnn_html_a0743ed5e860c316a20b68ca96301b411"><div class="ttname"><a href="namespacearmnn.html#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a></div><div class="ttdeci">typename ResolveTypeImpl< DT >::Type ResolveType</div><div class="ttdef"><b>Definition:</b> <a href="_resolve_type_8hpp_source.html#l00079">ResolveType.hpp:79</a></div></div>
<div class="ttc" id="aclassarmnn_1_1optimizations_1_1_fuse_batch_norm_html_a0ff9a790927b898d90261a8ea0e479e6"><div class="ttname"><a href="classarmnn_1_1optimizations_1_1_fuse_batch_norm.html#a0ff9a790927b898d90261a8ea0e479e6">armnn::optimizations::FuseBatchNorm::~FuseBatchNorm</a></div><div class="ttdeci">~FuseBatchNorm()=default</div></div>
<div class="ttc" id="aclassarmnn_utils_1_1_data_layout_indexed_html_a414e6f95548e6f7a01d5028b55ad3941"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.html#a414e6f95548e6f7a01d5028b55ad3941">armnnUtils::DataLayoutIndexed::GetWidthIndex</a></div><div class="ttdeci">unsigned int GetWidthIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.html#l00025">DataLayoutIndexed.hpp:25</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_base_tensor_html_a8aeddebdcf02e1832b22203c08a6b678"><div class="ttname"><a href="classarmnn_1_1_base_tensor.html#a8aeddebdcf02e1832b22203c08a6b678">armnn::BaseTensor::GetInfo</a></div><div class="ttdeci">const TensorInfo & GetInfo() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00297">Tensor.hpp:297</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_output_slot_html_a19d30f83e90f2612e6aec510715f790d"><div class="ttname"><a href="classarmnn_1_1_output_slot.html#a19d30f83e90f2612e6aec510715f790d">armnn::OutputSlot::MoveAllConnections</a></div><div class="ttdeci">void MoveAllConnections(OutputSlot &destination)</div><div class="ttdoc">Moves all connections to another OutputSlot.</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.html#l00145">Layer.cpp:145</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_input_slot_html"><div class="ttname"><a href="classarmnn_1_1_input_slot.html">armnn::InputSlot</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.html#l00042">Layer.hpp:42</a></div></div>
<div class="ttc" id="anamespacearmnn_html_a56943a0946e5f15e5e58054b8e7a04a4af97adbfc88b7012a0243215b1076e7e7"><div class="ttname"><a href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4af97adbfc88b7012a0243215b1076e7e7">armnn::LayerType::DepthwiseConvolution2d</a></div><div class="ttdeci">@ DepthwiseConvolution2d</div></div>
<div class="ttc" id="aclassarmnn_1_1_layer_html_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_layer.html#aea909c7327109228ef618d459015def3">armnn::Layer::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.html#l00326">Layer.cpp:326</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_layer_html_ad8e15c530c929ab823d89ae9fd2d3f11"><div class="ttname"><a href="classarmnn_1_1_layer.html#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.html#l00286">Layer.hpp:286</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00193">Tensor.hpp:193</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_constant_layer_html_ad0c4b8ee0efd8f9336571cbeab8a53fe"><div class="ttname"><a href="classarmnn_1_1_constant_layer.html#ad0c4b8ee0efd8f9336571cbeab8a53fe">armnn::ConstantLayer::m_LayerOutput</a></div><div class="ttdeci">std::shared_ptr< ConstTensorHandle > m_LayerOutput</div><div class="ttdef"><b>Definition:</b> <a href="_constant_layer_8hpp_source.html#l00046">ConstantLayer.hpp:46</a></div></div>
<div class="ttc" id="anamespacearmnn_html_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.html#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &&...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.html#l00014">IgnoreUnused.hpp:14</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_graph_html_a7563c5b899e7d0ada08fd0fdb202f205"><div class="ttname"><a href="classarmnn_1_1_graph.html#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.html#l00456">Graph.hpp:456</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_input_slot_html_a9effd325a6d512a3f8ff4bd207d53255"><div class="ttname"><a href="classarmnn_1_1_input_slot.html#a9effd325a6d512a3f8ff4bd207d53255">armnn::InputSlot::GetConnectedOutputSlot</a></div><div class="ttdeci">const OutputSlot * GetConnectedOutputSlot() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.html#l00056">Layer.hpp:56</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_constant_layer_html"><div class="ttname"><a href="classarmnn_1_1_constant_layer.html">armnn::ConstantLayer</a></div><div class="ttdoc">A layer that the constant data can be bound to.</div><div class="ttdef"><b>Definition:</b> <a href="_constant_layer_8hpp_source.html#l00015">ConstantLayer.hpp:15</a></div></div>
<div class="ttc" id="anamespacearmnn_html"><div class="ttname"><a href="namespacearmnn.html">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors.</div><div class="ttdef"><b>Definition:</b> <a href="01__00__quick__start_8dox_source.html#l00006">01_00_quick_start.dox:6</a></div></div>
<div class="ttc" id="aclassarmnn_utils_1_1_data_layout_indexed_html_a861b2621ee46e4b63379988b360b8cd9"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.html#a861b2621ee46e4b63379988b360b8cd9">armnnUtils::DataLayoutIndexed::GetChannelsIndex</a></div><div class="ttdeci">unsigned int GetChannelsIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.html#l00023">DataLayoutIndexed.hpp:23</a></div></div>
<div class="ttc" id="aclassarmnn_1_1optimizations_1_1_fuse_batch_norm_html"><div class="ttname"><a href="classarmnn_1_1optimizations_1_1_fuse_batch_norm.html">armnn::optimizations::FuseBatchNorm</a></div><div class="ttdef"><b>Definition:</b> <a href="_fuse_batch_norm_8hpp_source.html#l00019">FuseBatchNorm.hpp:19</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_const_tensor_html"><div class="ttname"><a href="classarmnn_1_1_const_tensor.html">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00329">Tensor.hpp:329</a></div></div>
<div class="ttc" id="anamespacearmnn_html_a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a"><div class="ttname"><a href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a">armnn::LayerType::Convolution2d</a></div><div class="ttdeci">@ Convolution2d</div></div>
<div class="ttc" id="astructarmnn_1_1_batch_normalization_descriptor_html_a11c821c7524251004a72ed13c510853c"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.html#a11c821c7524251004a72ed13c510853c">armnn::BatchNormalizationDescriptor::m_Eps</a></div><div class="ttdeci">float m_Eps</div><div class="ttdoc">Value to add to the variance. Used to avoid dividing by zero.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00841">Descriptors.hpp:841</a></div></div>
<div class="ttc" id="a_data_layout_indexed_8hpp_html"><div class="ttname"><a href="_data_layout_indexed_8hpp.html">DataLayoutIndexed.hpp</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_graph_html"><div class="ttname"><a href="classarmnn_1_1_graph.html">armnn::Graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.html#l00030">Graph.hpp:30</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_graph_html_a3ff30c6669fdc69de1f5be1f89bacc3f"><div class="ttname"><a href="classarmnn_1_1_graph.html#a3ff30c6669fdc69de1f5be1f89bacc3f">armnn::Graph::InsertNewLayer</a></div><div class="ttdeci">LayerT * InsertNewLayer(InputSlot &insertBefore, Args &&... args)</div><div class="ttdoc">Inserts a new layer between the output slot currently connected to insertBefore and insertBefore itse...</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.html#l00471">Graph.hpp:471</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_optimize_for_exclusive_connection_html"><div class="ttname"><a href="classarmnn_1_1_optimize_for_exclusive_connection.html">armnn::OptimizeForExclusiveConnection</a></div><div class="ttdef"><b>Definition:</b> <a href="_optimization_8hpp_source.html#l00173">Optimization.hpp:173</a></div></div>
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