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<div class="title">NeonBackendOptimizationUtils.hpp</div> </div>
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<a href="_neon_backend_optimization_utils_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 © 2023-2024 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="_arm_compute_subgraph_utils_8hpp.html">aclCommon/ArmComputeSubgraphUtils.hpp</a>></span></div>
<div class="line"><a name="l00009"></a><span class="lineno"> 9</span>  </div>
<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.html">armnn</a></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>  </div>
<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment">// Changes shapes of the form [1, 1, ..., W] to [ W ]</span></div>
<div class="line"><a name="l00014"></a><span class="lineno"><a class="line" href="namespacearmnn.html#aec9e2fd5ad76777cb83005a5ed7fe3de"> 14</a></span> <span class="keyword">inline</span> <span class="keywordtype">bool</span> <a class="code" href="namespacearmnn.html#aec9e2fd5ad76777cb83005a5ed7fe3de">CollapseLeadingUnitDimensions</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>& in, <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>& out)</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>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDimensions = in.<a class="code" href="classarmnn_1_1_tensor_info.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div>
<div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < (numDimensions-1); ++i)</div>
<div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  {</div>
<div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  <span class="keywordflow">if</span> (in.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i] != 1)</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="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a name="l00022"></a><span class="lineno"> 22</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>  </div>
<div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> w = in.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[numDimensions-1];</div>
<div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  out = in;</div>
<div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  out.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({w});</div>
<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  </div>
<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keywordflow">return</span> <span class="keyword">true</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>  </div>
<div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="comment">//</span></div>
<div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="comment">// Build slot and tensor info lists for Add/Mul/Add replacement</span></div>
<div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="comment">//</span></div>
<div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="keyword">template</span><<span class="keyword">typename</span> SlotListType></div>
<div class="line"><a name="l00036"></a><span class="lineno"><a class="line" href="namespacearmnn.html#aade64a959eb941bf19339e14d711a4b0"> 36</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.html#aade64a959eb941bf19339e14d711a4b0">BuildAddMulAddSlotLists</a>(<span class="keywordtype">bool</span> handleReLu,</div>
<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordtype">bool</span> multipleOutputs,</div>
<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  std::vector<SlotListType>& inputLayersSlotLists,</div>
<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  std::vector<SlotListType>& outputLayersSlotLists)</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>  <span class="comment">// Build input slot list</span></div>
<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  inputLayersSlotLists.push_back({0, 1}); <span class="comment">// Add</span></div>
<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  inputLayersSlotLists.push_back({1}); <span class="comment">// Mul</span></div>
<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  inputLayersSlotLists.push_back({1}); <span class="comment">// Add</span></div>
<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keywordflow">if</span> (handleReLu)</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>  inputLayersSlotLists.push_back({}); <span class="comment">// Relu</span></div>
<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  }</div>
<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  </div>
<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="comment">// Build output slot list</span></div>
<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keywordflow">if</span> (multipleOutputs)</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>  outputLayersSlotLists.push_back({0}); <span class="comment">// Add</span></div>
<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  }</div>
<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  {</div>
<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  outputLayersSlotLists.push_back({}); <span class="comment">// Add</span></div>
<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  }</div>
<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  outputLayersSlotLists.push_back({}); <span class="comment">// Mul</span></div>
<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">if</span> (handleReLu)</div>
<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  {</div>
<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  outputLayersSlotLists.push_back({}); <span class="comment">// Add</span></div>
<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  outputLayersSlotLists.push_back({0}); <span class="comment">// Relu</span></div>
<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  }</div>
<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  {</div>
<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  outputLayersSlotLists.push_back({0}); <span class="comment">// Add</span></div>
<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  }</div>
<div class="line"><a name="l00069"></a><span class="lineno"> 69</span> }</div>
<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  </div>
<div class="line"><a name="l00071"></a><span class="lineno"><a class="line" href="namespacearmnn.html#a107fb425e884b38b3b8568334ae1ee3c"> 71</a></span> <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.html#a107fb425e884b38b3b8568334ae1ee3c">GetFusedName</a>(<a class="code" href="classarmnn_1_1_layer.html">Layer</a> *layerList[4], std::string& fusedName)</div>
<div class="line"><a name="l00072"></a><span class="lineno"> 72</span> {</div>
<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="comment">// Build the fused name string</span></div>
<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  fusedName = <span class="stringliteral">"fused"</span>;</div>
<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> layerIdx = 0; layerIdx< 4; ++layerIdx)</div>
<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  {</div>
<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keywordflow">if</span> (! layerList[layerIdx])</div>
<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  {</div>
<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  }</div>
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  fusedName += <span class="stringliteral">"-"</span>;</div>
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  fusedName += layerList[layerIdx]-><a class="code" href="classarmnn_1_1_layer.html#a9a97cb6d32661a57fc33bd29b8e41ff4">GetNameStr</a>();</div>
<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  }</div>
<div class="line"><a name="l00084"></a><span class="lineno"> 84</span> }</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="keyword">template</span><<span class="keyword">typename</span> Type></div>
<div class="line"><a name="l00087"></a><span class="lineno"><a class="line" href="namespacearmnn.html#aad03b7a578035532aa75c183bc708846"> 87</a></span> <span class="keywordtype">bool</span> <a class="code" href="namespacearmnn.html#aad03b7a578035532aa75c183bc708846">BuildAddMulAddTensorInfoLists</a>(Type* layerList[4],</div>
<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>& numInputs,</div>
<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>& numOutputs,</div>
<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  std::vector<TensorInfo>& inputInfos,</div>
<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  std::vector<TensorInfo>& outputInfos,</div>
<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_activation_descriptor.html">ActivationDescriptor</a>*& activationDescriptor,</div>
<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordtype">bool</span>& fuseReLu)</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>  <a class="code" href="_exceptions_8hpp.html#a31e4268013316d114e8927e4c0035ec5">ARMNN_THROW_INVALIDARG_IF_FALSE</a>(layerList[0]);</div>
<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <a class="code" href="_exceptions_8hpp.html#a31e4268013316d114e8927e4c0035ec5">ARMNN_THROW_INVALIDARG_IF_FALSE</a>(layerList[1]);</div>
<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <a class="code" href="_exceptions_8hpp.html#a31e4268013316d114e8927e4c0035ec5">ARMNN_THROW_INVALIDARG_IF_FALSE</a>(layerList[2]);</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>  <a class="code" href="_exceptions_8hpp.html#a31e4268013316d114e8927e4c0035ec5">ARMNN_THROW_INVALIDARG_IF_FALSE</a>(<a class="code" href="namespacearmnn.html#aefc7bff008047b9f763c2cf82c1a0fcb">IsSequenceLayerType</a>(*layerList[0], <a class="code" href="namespacearmnn.html#ac4f8557279754ed7b3f749d55b0e3047aec211f7c20af43e742bf2570c3cb84f9">BinaryOperation::Add</a>));</div>
<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <a class="code" href="_exceptions_8hpp.html#a31e4268013316d114e8927e4c0035ec5">ARMNN_THROW_INVALIDARG_IF_FALSE</a>(<a class="code" href="namespacearmnn.html#aefc7bff008047b9f763c2cf82c1a0fcb">IsSequenceLayerType</a>(*layerList[1], <a class="code" href="namespacearmnn.html#ac4f8557279754ed7b3f749d55b0e3047a62b6d55816cf737bfc6f42e60df1a3f2">BinaryOperation::Mul</a>));</div>
<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <a class="code" href="_exceptions_8hpp.html#a31e4268013316d114e8927e4c0035ec5">ARMNN_THROW_INVALIDARG_IF_FALSE</a>(<a class="code" href="namespacearmnn.html#aefc7bff008047b9f763c2cf82c1a0fcb">IsSequenceLayerType</a>(*layerList[2], <a class="code" href="namespacearmnn.html#ac4f8557279754ed7b3f749d55b0e3047aec211f7c20af43e742bf2570c3cb84f9">BinaryOperation::Add</a>));</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>  <span class="keyword">auto</span> is1D = [](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> expanded)</div>
<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  {</div>
<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> collapsed;</div>
<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keywordflow">if</span> (<a class="code" href="namespacearmnn.html#aec9e2fd5ad76777cb83005a5ed7fe3de">CollapseLeadingUnitDimensions</a>(expanded, collapsed))</div>
<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  {</div>
<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="keywordflow">return</span> (collapsed.<a class="code" href="classarmnn_1_1_tensor_info.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() == 1);</div>
<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  }</div>
<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  {</div>
<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keywordflow">return</span> (expanded.GetNumDimensions() == 1);</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>  </div>
<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="comment">// One of the 2 inputs for MUL and the Second ADD must be 1D</span></div>
<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="comment">// ref: clframework/src/cpu/kernels/CpuAddMulAddKernel.cpp</span></div>
<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keyword">auto</span>& mulLayer = *(PolymorphicDowncast<ElementwiseBinaryLayer*>(layerList[1]));</div>
<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keyword">auto</span>& add2Layer = *(PolymorphicDowncast<ElementwiseBinaryLayer*>(layerList[2]));</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>  <a class="code" href="classarmnn_1_1_layer.html">Layer</a>& mulInput0 = mulLayer.<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>()-><a class="code" href="classarmnn_1_1_output_slot.html#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</a>();</div>
<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <a class="code" href="classarmnn_1_1_layer.html">Layer</a>& mulInput1 = mulLayer.<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="l00123"></a><span class="lineno"> 123</span>  <a class="code" href="classarmnn_1_1_layer.html">Layer</a>& add2Input0 = add2Layer.<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>()-><a class="code" href="classarmnn_1_1_output_slot.html#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</a>();</div>
<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <a class="code" href="classarmnn_1_1_layer.html">Layer</a>& add2Input1 = add2Layer.<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="l00125"></a><span class="lineno"> 125</span>  <span class="keywordflow">if</span> (!is1D(mulInput0.<a class="code" href="classarmnn_1_1_layer.html#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.html#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>()) && !is1D(mulInput1.<a class="code" href="classarmnn_1_1_layer.html#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.html#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>()))</div>
<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  {</div>
<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  }</div>
<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="keywordflow">if</span> (!is1D(add2Input0.<a class="code" href="classarmnn_1_1_layer.html#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.html#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>()) && !is1D(add2Input1.<a class="code" href="classarmnn_1_1_layer.html#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.html#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</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>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</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>  </div>
<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  fuseReLu = (layerList[3] != <span class="keyword">nullptr</span>);</div>
<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keywordflow">if</span> (fuseReLu)</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>  activationDescriptor = &PolymorphicDowncast<ActivationLayer *>(layerList[3])->GetParameters();</div>
<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <a class="code" href="_exceptions_8hpp.html#a31e4268013316d114e8927e4c0035ec5">ARMNN_THROW_INVALIDARG_IF_FALSE</a>((activationDescriptor-><a class="code" href="structarmnn_1_1_activation_descriptor.html#af10fa7883e3579950f477bee92a64844">m_Function</a> == <a class="code" href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">ActivationFunction::ReLu</a>) ||</div>
<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  (activationDescriptor-><a class="code" href="structarmnn_1_1_activation_descriptor.html#af10fa7883e3579950f477bee92a64844">m_Function</a> == <a class="code" href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">ActivationFunction::BoundedReLu</a>));</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>  numInputs = 0;</div>
<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  numOutputs = 0;</div>
<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  </div>
<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="comment">// Ensure that there are 6 input slots in the add/mul/add layers</span></div>
<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="comment">// we are going to replace</span></div>
<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> layerIdx = 0;</div>
<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSlotCount = 0;</div>
<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="keywordflow">for</span> (layerIdx = 0; layerIdx < 3; ++layerIdx)</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>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> slotIdx = 0; slotIdx < layerList[layerIdx]->GetNumInputSlots(); ++slotIdx)</div>
<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  {</div>
<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <a class="code" href="classarmnn_1_1_input_slot.html">InputSlot</a>* inputSlot = &layerList[layerIdx]->GetInputSlot(slotIdx);</div>
<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <a class="code" href="classarmnn_1_1_output_slot.html">OutputSlot</a>* outputSlot = inputSlot-><a class="code" href="classarmnn_1_1_input_slot.html#a9effd325a6d512a3f8ff4bd207d53255">GetConnectedOutputSlot</a>();</div>
<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keywordflow">if</span> (outputSlot)</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">if</span> (layerIdx == 0)</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>  <span class="comment">// Always count the input connections of the first add</span></div>
<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  inputInfos.push_back(inputSlot-><a class="code" href="classarmnn_1_1_input_slot.html#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>());</div>
<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  numInputs++;</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="keywordflow">else</span></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">// For subsequent layers, we skip connections to the previous layers in the counting</span></div>
<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keywordflow">if</span> (&outputSlot-><a class="code" href="classarmnn_1_1_output_slot.html#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</a>() != layerList[layerIdx-1])</div>
<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  {</div>
<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> inputSlotInfo = inputSlot-><a class="code" href="classarmnn_1_1_input_slot.html#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>();</div>
<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keywordflow">if</span> (numInputs == 2 || numInputs == 3)</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">// Workaround the broadcast optimization to collapse shapes such as</span></div>
<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="comment">// [1, 1, 1, 2] to [2] as required by backend</span></div>
<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keywordflow">if</span> (<a class="code" href="namespacearmnn.html#aec9e2fd5ad76777cb83005a5ed7fe3de">CollapseLeadingUnitDimensions</a>(inputSlot-><a class="code" href="classarmnn_1_1_input_slot.html#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>(), inputSlotInfo))</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_output_slot.html">OutputSlot</a>* previousLayerSlot = inputSlot-><a class="code" href="classarmnn_1_1_input_slot.html#a9effd325a6d512a3f8ff4bd207d53255">GetConnectedOutputSlot</a>();</div>
<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keywordflow">if</span> (previousLayerSlot)</div>
<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  {</div>
<div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordflow">if</span> (previousLayerSlot-><a class="code" href="classarmnn_1_1_output_slot.html#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</a>().<a class="code" href="classarmnn_1_1_layer.html#ad8e15c530c929ab823d89ae9fd2d3f11">GetType</a>() == <a class="code" href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255">LayerType::Constant</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>  <span class="comment">// First update the TensorInfo in the constant owning layer</span></div>
<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  previousLayerSlot-><a class="code" href="classarmnn_1_1_output_slot.html#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(inputSlotInfo);</div>
<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="comment">// Then update the TensorInfo in the workload for the owning layer</span></div>
<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <a class="code" href="classarmnn_1_1_constant_layer.html">ConstantLayer</a>* layer = PolymorphicDowncast<ConstantLayer*>(</div>
<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  &previousLayerSlot-><a class="code" href="classarmnn_1_1_output_slot.html#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</a>());</div>
<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  layer-><a class="code" href="classarmnn_1_1_constant_layer.html#ad0c4b8ee0efd8f9336571cbeab8a53fe">m_LayerOutput</a></div>
<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  = std::make_unique<ScopedTensorHandle>(</div>
<div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <a class="code" href="classarmnn_1_1_const_tensor.html">ConstTensor</a>(inputSlotInfo,</div>
<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  layer-><a class="code" href="classarmnn_1_1_constant_layer.html#ad0c4b8ee0efd8f9336571cbeab8a53fe">m_LayerOutput</a>.get()->GetConstTensor<<span class="keywordtype">void</span>>()));</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>  }</div>
<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  }</div>
<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  inputInfos.push_back(inputSlotInfo);</div>
<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  numInputs++;</div>
<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  }</div>
<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  }</div>
<div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  inputSlotCount++;</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>  }</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>  <span class="comment">// Check the input counts</span></div>
<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keywordtype">bool</span> validInputCount = (inputSlotCount == 6) && (inputInfos.size() == 4);</div>
<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keywordflow">if</span> (! validInputCount)</div>
<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  {</div>
<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</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>  </div>
<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> maxIdx = (fuseReLu) ? 4 : 3;</div>
<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keywordflow">for</span> (layerIdx = 0; layerIdx < maxIdx; ++layerIdx)</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="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> slotIdx = 0; slotIdx < layerList[layerIdx]->GetNumOutputSlots(); ++slotIdx)</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>  <a class="code" href="classarmnn_1_1_output_slot.html">OutputSlot</a>* outputSlot = &layerList[layerIdx]->GetOutputSlot(slotIdx);</div>
<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  </div>
<div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> connectionIdx = 0; connectionIdx < outputSlot-><a class="code" href="classarmnn_1_1_output_slot.html#a25b0119c02aece1d341b99953d169c0f">GetNumConnections</a>(); ++connectionIdx)</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>  <a class="code" href="classarmnn_1_1_input_slot.html">InputSlot</a>* inputSlot = outputSlot-><a class="code" href="classarmnn_1_1_output_slot.html#a048e8c1536cf6b8871b093a73a4a3a85">GetConnection</a>(connectionIdx);</div>
<div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keywordflow">if</span> (layerIdx < (maxIdx-1))</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>  <span class="keywordflow">if</span> (&inputSlot-><a class="code" href="classarmnn_1_1_input_slot.html#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</a>() != layerList[layerIdx+1])</div>
<div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  {</div>
<div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  outputInfos.push_back(outputSlot-><a class="code" href="classarmnn_1_1_output_slot.html#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>());</div>
<div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  numOutputs++;</div>
<div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  }</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (layerList[layerIdx] != <span class="keyword">nullptr</span>)</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>  outputInfos.push_back(outputSlot-><a class="code" href="classarmnn_1_1_output_slot.html#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>());</div>
<div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  numOutputs++;</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>  }</div>
<div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  }</div>
<div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  }</div>
<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  </div>
<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="comment">// Check the output count</span></div>
<div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="keywordtype">bool</span> validOutputCount = (outputInfos.size() > 0);</div>
<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keywordflow">if</span> (! validOutputCount)</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>  <span class="keywordflow">return</span> <span class="keyword">false</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>  </div>
<div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
<div class="line"><a name="l00244"></a><span class="lineno"> 244</span> }</div>
<div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  </div>
<div class="line"><a name="l00246"></a><span class="lineno"> 246</span> }</div>
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</div><!-- doc-content -->
<div class="ttc" id="anamespacearmnn_html_ac4f8557279754ed7b3f749d55b0e3047a62b6d55816cf737bfc6f42e60df1a3f2"><div class="ttname"><a href="namespacearmnn.html#ac4f8557279754ed7b3f749d55b0e3047a62b6d55816cf737bfc6f42e60df1a3f2">armnn::BinaryOperation::Mul</a></div><div class="ttdeci">@ Mul</div></div>
<div class="ttc" id="astructarmnn_1_1_activation_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.html">armnn::ActivationDescriptor</a></div><div class="ttdoc">An ActivationDescriptor for the ActivationLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00036">Descriptors.hpp:36</a></div></div>
<div class="ttc" id="anamespacearmnn_html_ac4f8557279754ed7b3f749d55b0e3047aec211f7c20af43e742bf2570c3cb84f9"><div class="ttname"><a href="namespacearmnn.html#ac4f8557279754ed7b3f749d55b0e3047aec211f7c20af43e742bf2570c3cb84f9">armnn::BinaryOperation::Add</a></div><div class="ttdeci">@ Add</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#l00100">Layer.cpp:100</a></div></div>
<div class="ttc" id="anamespacearmnn_html_aec9e2fd5ad76777cb83005a5ed7fe3de"><div class="ttname"><a href="namespacearmnn.html#aec9e2fd5ad76777cb83005a5ed7fe3de">armnn::CollapseLeadingUnitDimensions</a></div><div class="ttdeci">bool CollapseLeadingUnitDimensions(const TensorInfo &in, TensorInfo &out)</div><div class="ttdef"><b>Definition:</b> <a href="_neon_backend_optimization_utils_8hpp_source.html#l00014">NeonBackendOptimizationUtils.hpp:14</a></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_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#l00095">Layer.cpp:95</a></div></div>
<div class="ttc" id="anamespacearmnn_html_aade64a959eb941bf19339e14d711a4b0"><div class="ttname"><a href="namespacearmnn.html#aade64a959eb941bf19339e14d711a4b0">armnn::BuildAddMulAddSlotLists</a></div><div class="ttdeci">void BuildAddMulAddSlotLists(bool handleReLu, bool multipleOutputs, std::vector< SlotListType > &inputLayersSlotLists, std::vector< SlotListType > &outputLayersSlotLists)</div><div class="ttdef"><b>Definition:</b> <a href="_neon_backend_optimization_utils_8hpp_source.html#l00036">NeonBackendOptimizationUtils.hpp:36</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="anamespacearmnn_html_aefc7bff008047b9f763c2cf82c1a0fcb"><div class="ttname"><a href="namespacearmnn.html#aefc7bff008047b9f763c2cf82c1a0fcb">armnn::IsSequenceLayerType</a></div><div class="ttdeci">bool IsSequenceLayerType(Layer &layer, LayerType type)</div><div class="ttdef"><b>Definition:</b> <a href="_subgraph_utils_8hpp_source.html#l00362">SubgraphUtils.hpp:362</a></div></div>
<div class="ttc" id="anamespacearmnn_html_a107fb425e884b38b3b8568334ae1ee3c"><div class="ttname"><a href="namespacearmnn.html#a107fb425e884b38b3b8568334ae1ee3c">armnn::GetFusedName</a></div><div class="ttdeci">void GetFusedName(Layer *layerList[4], std::string &fusedName)</div><div class="ttdef"><b>Definition:</b> <a href="_neon_backend_optimization_utils_8hpp_source.html#l00071">NeonBackendOptimizationUtils.hpp:71</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorInfo::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00197">Tensor.hpp:197</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="anamespacearmnn_html_a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d"><div class="ttname"><a href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">armnn::ActivationFunction::BoundedReLu</a></div><div class="ttdeci">@ BoundedReLu</div><div class="ttdoc">min(a, max(b, input)) ReLu1 & ReLu6.</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"><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_1_input_slot_html_ada2ad7d1caeeb4ef6195c8925fad6a65"><div class="ttname"><a href="classarmnn_1_1_input_slot.html#ada2ad7d1caeeb4ef6195c8925fad6a65">armnn::InputSlot::GetTensorInfo</a></div><div class="ttdeci">const TensorInfo & GetTensorInfo() const override</div><div class="ttdoc">Gets the TensorInfo for this InputSlot.</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.html#l00614">Layer.cpp:614</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="a_exceptions_8hpp_html_a31e4268013316d114e8927e4c0035ec5"><div class="ttname"><a href="_exceptions_8hpp.html#a31e4268013316d114e8927e4c0035ec5">ARMNN_THROW_INVALIDARG_IF_FALSE</a></div><div class="ttdeci">#define ARMNN_THROW_INVALIDARG_IF_FALSE(_cond)</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.html#l00212">Exceptions.hpp:212</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_output_slot_html_a25b0119c02aece1d341b99953d169c0f"><div class="ttname"><a href="classarmnn_1_1_output_slot.html#a25b0119c02aece1d341b99953d169c0f">armnn::OutputSlot::GetNumConnections</a></div><div class="ttdeci">unsigned int GetNumConnections() const override</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.html#l00158">Layer.hpp:158</a></div></div>
<div class="ttc" id="astructarmnn_1_1_activation_descriptor_html_af10fa7883e3579950f477bee92a64844"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.html#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,...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00059">Descriptors.hpp:59</a></div></div>
<div class="ttc" id="anamespacearmnn_html_aad03b7a578035532aa75c183bc708846"><div class="ttname"><a href="namespacearmnn.html#aad03b7a578035532aa75c183bc708846">armnn::BuildAddMulAddTensorInfoLists</a></div><div class="ttdeci">bool BuildAddMulAddTensorInfoLists(Type *layerList[4], unsigned int &numInputs, unsigned int &numOutputs, std::vector< TensorInfo > &inputInfos, std::vector< TensorInfo > &outputInfos, const ActivationDescriptor *&activationDescriptor, bool &fuseReLu)</div><div class="ttdef"><b>Definition:</b> <a href="_neon_backend_optimization_utils_8hpp_source.html#l00087">NeonBackendOptimizationUtils.hpp:87</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_layer_html_a9a97cb6d32661a57fc33bd29b8e41ff4"><div class="ttname"><a href="classarmnn_1_1_layer.html#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.html#l00240">Layer.hpp:240</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="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_a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559"><div class="ttname"><a href="namespacearmnn.html#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">armnn::ActivationFunction::ReLu</a></div><div class="ttdeci">@ ReLu</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="aclassarmnn_1_1_tensor_info_html_abe8889e8150beef5fd204b2d87b49298"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">armnn::TensorInfo::SetShape</a></div><div class="ttdeci">void SetShape(const TensorShape &newShape)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00195">Tensor.hpp:195</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_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="aclassarmnn_1_1_output_slot_html_a048e8c1536cf6b8871b093a73a4a3a85"><div class="ttname"><a href="classarmnn_1_1_output_slot.html#a048e8c1536cf6b8871b093a73a4a3a85">armnn::OutputSlot::GetConnection</a></div><div class="ttdeci">const InputSlot * GetConnection(unsigned int index) const override</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.html#l00083">Layer.cpp:83</a></div></div>
<div class="ttc" id="a_arm_compute_subgraph_utils_8hpp_html"><div class="ttname"><a href="_arm_compute_subgraph_utils_8hpp.html">ArmComputeSubgraphUtils.hpp</a></div></div>
<div class="ttc" id="anamespacearmnn_html_a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255"><div class="ttname"><a href="namespacearmnn.html#a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255">armnn::LayerType::Constant</a></div><div class="ttdeci">@ Constant</div></div>
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