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<div class="title">QuantizerStrategy.cpp</div> </div>
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<a href="_quantizer_strategy_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_quantizer_strategy_8hpp.xhtml">QuantizerStrategy.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "<a class="code" href="_polymorphic_downcast_8hpp.xhtml">armnn/utility/PolymorphicDowncast.hpp</a>"</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> {</div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> </div><div class="line"><a name="l00012"></a><span class="lineno"><a class="line" href="classarmnn_1_1_quantizer_strategy.xhtml#a7f9b9f65046b39e047e674ab5cd7a18f"> 12</a></span> <a class="code" href="classarmnn_1_1_quantizer_strategy.xhtml#a7f9b9f65046b39e047e674ab5cd7a18f">QuantizerStrategy::QuantizerStrategy</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_range_tracker.xhtml">RangeTracker</a>& rangeTracker,</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_i_quantization_scheme.xhtml">IQuantizationScheme</a>* quantizationScheme,</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>  <span class="keywordtype">bool</span> preserveType)</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>  : m_Ranges(rangeTracker)</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  , m_QuantizedNetwork(<a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>::Create())</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  , m_QuantizationScheme(quantizationScheme)</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  , m_PreserveType(preserveType)</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> {</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> }</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="keywordtype">void</span> QuantizerStrategy::SetQuantizedInputConnections(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* srcLayer,</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* quantizedLayer)</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>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(srcLayer);</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < srcLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>(); i++)</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  {</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_input_slot.xhtml">IInputSlot</a>& srcInputSlot = srcLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(i);</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_input_slot.xhtml">InputSlot</a>* inputSlot = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_input_slot.xhtml">InputSlot</a>*<span class="keyword">></span>(&srcInputSlot);</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(inputSlot);</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_output_slot.xhtml">OutputSlot</a>* outputSlot = inputSlot-><a class="code" href="classarmnn_1_1_input_slot.xhtml#a9effd325a6d512a3f8ff4bd207d53255">GetConnectedOutputSlot</a>();</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(outputSlot);</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> slotIdx = outputSlot-><a class="code" href="classarmnn_1_1_output_slot.xhtml#a27545b353e48a739981d345b64bb9dc9">CalculateIndexOnOwner</a>();</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>& layerToFind = outputSlot-><a class="code" href="classarmnn_1_1_output_slot.xhtml#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</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="keyword">auto</span> found = m_OriginalToQuantizedGuidMap.find(layerToFind.<a class="code" href="classarmnn_1_1_layer.xhtml#a8dc12f0ee5b232d397bd18ced1a72a64">GetGuid</a>());</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordflow">if</span> (found == m_OriginalToQuantizedGuidMap.end())</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  {</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="comment">// Error in graph traversal order</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(<span class="keyword">false</span>, <span class="stringliteral">"Error in graph traversal"</span>);</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  }</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> </div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="comment">// Connect the slots in the quantized model</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* prevQuantizedLayer = m_QuantizedGuidToLayerMap[found->second];</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <a class="code" href="classarmnn_1_1_i_input_slot.xhtml">IInputSlot</a>& newInputSlot = quantizedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(i);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& newOutputSlot = prevQuantizedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(slotIdx);</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  newOutputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(newInputSlot);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>(outputSlot-><a class="code" href="classarmnn_1_1_output_slot.xhtml#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>());</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="comment">// Only try to set quantization params on tensors that can be quantized</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordflow">if</span> (inputSlot-><a class="code" href="classarmnn_1_1_input_slot.xhtml#a9effd325a6d512a3f8ff4bd207d53255">GetConnectedOutputSlot</a>()-><a class="code" href="classarmnn_1_1_output_slot.xhtml#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a> &&</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  inputSlot-><a class="code" href="classarmnn_1_1_input_slot.xhtml#a9effd325a6d512a3f8ff4bd207d53255">GetConnectedOutputSlot</a>()-><a class="code" href="classarmnn_1_1_output_slot.xhtml#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a> &&</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  inputSlot-><a class="code" href="classarmnn_1_1_input_slot.xhtml#a9effd325a6d512a3f8ff4bd207d53255">GetConnectedOutputSlot</a>()-><a class="code" href="classarmnn_1_1_output_slot.xhtml#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">DataType::Signed64</a>)</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>  <span class="comment">// Fetch the min/max ranges that were computed earlier</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keyword">auto</span> range = m_Ranges.<a class="code" href="classarmnn_1_1_range_tracker.xhtml#a507bae23f59e94b4161886ebe663cdf4">GetRange</a>(layerToFind.<a class="code" href="classarmnn_1_1_layer.xhtml#a8dc12f0ee5b232d397bd18ced1a72a64">GetGuid</a>(), slotIdx);</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qParams = m_QuantizationScheme-><a class="code" href="structarmnn_1_1_i_quantization_scheme.xhtml#a6a5561395e9693f02258b49dfcc009b4">ComputeScheme</a>(range.first, range.second);</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>.SetDataType(m_QuantizationScheme-><a class="code" href="structarmnn_1_1_i_quantization_scheme.xhtml#ad23181f9f8fcc85758f62c49fc7ca23f">GetDataType</a>());</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>.SetQuantizationOffset(qParams.second);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>.SetQuantizationScale(qParams.first);</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>  newOutputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>);</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  }</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> }</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> </div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> QuantizerStrategy::CreateQuantizedBias(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* srcLayer,</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>& weights,</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>& biases,</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  std::vector<int32_t>& backing)</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>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(srcLayer);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_input_slot.xhtml">IInputSlot</a>& srcInputSlot = srcLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keyword">auto</span> inputSlot = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="classarmnn_1_1_input_slot.xhtml">InputSlot</a>*<span class="keyword">></span>(&srcInputSlot);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(inputSlot);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_output_slot.xhtml">OutputSlot</a>* outputSlot = inputSlot->GetConnectedOutputSlot();</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>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(outputSlot);</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> slotIdx = outputSlot-><a class="code" href="classarmnn_1_1_output_slot.xhtml#a27545b353e48a739981d345b64bb9dc9">CalculateIndexOnOwner</a>();</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>& layerToFind = outputSlot-><a class="code" href="classarmnn_1_1_output_slot.xhtml#a7ddaf04177053a536f0e7be83a642bc6">GetOwningLayer</a>();</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> </div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keyword">auto</span> found = m_OriginalToQuantizedGuidMap.find(layerToFind.<a class="code" href="classarmnn_1_1_layer.xhtml#a8dc12f0ee5b232d397bd18ced1a72a64">GetGuid</a>());</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordflow">if</span> (found == m_OriginalToQuantizedGuidMap.end())</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">// Error in graph traversal order</span></div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(<span class="keyword">false</span>, <span class="stringliteral">"Error in graph traversal"</span>);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keywordflow">return</span> biases.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>();</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  }</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="comment">// Fetch the min/max ranges that were computed earlier</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keyword">auto</span> range = m_Ranges.<a class="code" href="classarmnn_1_1_range_tracker.xhtml#a507bae23f59e94b4161886ebe663cdf4">GetRange</a>(layerToFind.<a class="code" href="classarmnn_1_1_layer.xhtml#a8dc12f0ee5b232d397bd18ced1a72a64">GetGuid</a>(), slotIdx);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <a class="code" href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">OffsetScalePair</a> qParams = m_QuantizationScheme-><a class="code" href="structarmnn_1_1_i_quantization_scheme.xhtml#a6a5561395e9693f02258b49dfcc009b4">ComputeScheme</a>(range.first, range.second);</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="comment">// Get the quantization scale based on input and weight scale</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordtype">float</span> scale = qParams.first * weights.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>();</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="comment">// Set up quantized bias tensor info and allocate space</span></div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> qInfo(biases.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>().GetInfo().GetShape(), <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>, scale, 0);</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  backing.resize(biases.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>().GetInfo().GetNumElements());</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> </div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="comment">// Convert values to int32</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < backing.size(); ++i)</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>  <span class="keywordtype">float</span> fp32Value = <span class="keyword">static_cast<</span><span class="keyword">const </span><span class="keywordtype">float</span>*<span class="keyword">></span>(biases.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>().GetMemoryArea())[i];</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  backing[i] = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><int32_t>(fp32Value * ( 1 / scale ));</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> </div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(qInfo, backing);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> }</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> </div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> <span class="keywordtype">void</span> QuantizerStrategy::RecordLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* srcLayer, <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* quantizedLayer)</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>  m_OriginalToQuantizedGuidMap.insert(std::make_pair(srcLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afb5e65c770f6cee222db8af7581541a6">GetGuid</a>(), quantizedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afb5e65c770f6cee222db8af7581541a6">GetGuid</a>()));</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  m_QuantizedGuidToLayerMap.insert(std::make_pair(quantizedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afb5e65c770f6cee222db8af7581541a6">GetGuid</a>(), quantizedLayer));</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> }</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"><a class="line" href="classarmnn_1_1_quantizer_strategy.xhtml#ab79e40d3d0c6f4c9e24534f251306add"> 118</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_quantizer_strategy.xhtml#ab79e40d3d0c6f4c9e24534f251306add">QuantizerStrategy::ExecuteStrategy</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *layer,</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor> &constants,</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keyword">const</span> <span class="keywordtype">char</span> *name,</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</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>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(<span class="keywordtype">id</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_i_connectable_layer.xhtml">IConnectableLayer</a>* newLayer;</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="keywordflow">switch</span> (layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">GetType</a>())</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  {</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f">armnn::LayerType::Addition</a> :</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  {</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  newLayer = m_QuantizedNetwork->AddAdditionLayer(name);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  }</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">armnn::LayerType::Activation</a> :</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  {</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a>& activationDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  newLayer = m_QuantizedNetwork->AddActivationLayer(activationDescriptor, name);</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  }</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2139684546b147c106b329f41547640c">armnn::LayerType::ArgMinMax</a> :</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>  <a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml">ArgMinMaxDescriptor</a> argMinMaxDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml">ArgMinMaxDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  newLayer = m_QuantizedNetwork->AddArgMinMaxLayer(argMinMaxDescriptor, name);</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  }</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae4743c3ec15d1d84169b17264634692e">armnn::LayerType::BatchNormalization</a> :</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  {</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> </div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">BatchNormalizationDescriptor</a> batchNormalizationDescriptor =</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">BatchNormalizationDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  std::vector<uint8_t> meanBacking;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> qMean = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[0], meanBacking);</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  std::vector<uint8_t> varianceBacking;</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> qVariance = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[1], varianceBacking);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  std::vector<uint8_t> betaBacking;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> qBeta = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[2], betaBacking);</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> </div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  std::vector<uint8_t> gammaBacking;</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> qGamma = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[3], gammaBacking);</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span> </div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  newLayer = m_QuantizedNetwork->AddBatchNormalizationLayer(batchNormalizationDescriptor,</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  qMean,</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  qVariance,</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  qBeta,</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  qGamma,</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  name);</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  }</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6ee06c6045d0c5b6565a247955ef0fc2">armnn::LayerType::BatchToSpaceNd</a> :</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  {</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">BatchToSpaceNdDescriptor</a> batchToSpaceNdDescriptor =</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">BatchToSpaceNdDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> </div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  newLayer = m_QuantizedNetwork->AddBatchToSpaceNdLayer(batchToSpaceNdDescriptor, name);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordflow">break</span>;</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="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af6c0e3a1c3cfabd32ae8d3ae741fcf0a">armnn::LayerType::Comparison</a> :</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  {</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml">ComparisonDescriptor</a> comparisonDescriptor =<span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml">ComparisonDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  newLayer = m_QuantizedNetwork->AddComparisonLayer(comparisonDescriptor, name);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  }</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae20f0f2826a6549809f050b86274567f">armnn::LayerType::Concat</a> :</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  {</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> originsDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  newLayer = m_QuantizedNetwork->AddConcatLayer(originsDescriptor, name);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keywordflow">break</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255">armnn::LayerType::Constant</a> :</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>  std::vector<uint8_t> inputBacking;</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> qInput = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[0], inputBacking);</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>  newLayer = m_QuantizedNetwork->AddConstantLayer(qInput, name);</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keywordflow">break</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a">armnn::LayerType::Convolution2d</a> :</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="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a> biases = constants.size() == 1 ?</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a>{} :</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a>(constants[1]);</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>  std::vector<uint8_t> weightsBacking;</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> qWeights = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[0], weightsBacking);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalQBiases;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  std::vector<int32_t> biasesBacking;</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> </div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keywordflow">if</span> (biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>())</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>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> qBiases = CreateQuantizedBias(layer, qWeights, biases, biasesBacking);</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  optionalQBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(qBiases);</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>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> convolution2dDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a>&<span class="keyword">></span>(descriptor);</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>  newLayer = m_QuantizedNetwork->AddConvolution2dLayer(convolution2dDescriptor,</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  qWeights,</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  optionalQBiases,</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  name);</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keywordflow">break</span>;</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="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a731729ad1b2c0eb9399b62c770b3482d">armnn::LayerType::DepthToSpace</a> :</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>  <a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml">DepthToSpaceDescriptor</a> depthToSpaceDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml">DepthToSpaceDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> </div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  newLayer = m_QuantizedNetwork->AddDepthToSpaceLayer(depthToSpaceDescriptor, name);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  }</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af97adbfc88b7012a0243215b1076e7e7">armnn::LayerType::DepthwiseConvolution2d</a> :</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>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> depthwiseConvolution2dDescriptor =</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a>&<span class="keyword">></span>(descriptor);</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="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a> biases = constants.size() == 1 ?</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a>{} :</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a>(constants[1]);</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>  std::vector<uint8_t> weightsBacking;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> qWeights = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[0], weightsBacking);</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalQBiases;</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  std::vector<int32_t> biasesBacking;</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>  <span class="keywordflow">if</span> (biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>())</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  {</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> qBiases = CreateQuantizedBias(layer, qWeights, biases, biasesBacking);</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  optionalQBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(qBiases);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  }</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> </div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  newLayer = m_QuantizedNetwork->AddDepthwiseConvolution2dLayer(</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  depthwiseConvolution2dDescriptor,</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  qWeights,</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  optionalQBiases,</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  name);</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  }</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4abcd30d7ea97ad20c2cddc0f47e6b70c7">armnn::LayerType::ElementwiseUnary</a> :</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  {</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <a class="code" href="structarmnn_1_1_elementwise_unary_descriptor.xhtml">ElementwiseUnaryDescriptor</a> elementwiseUnaryDescriptor =</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_elementwise_unary_descriptor.xhtml">ElementwiseUnaryDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> </div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  newLayer = m_QuantizedNetwork->AddElementwiseUnaryLayer(elementwiseUnaryDescriptor, name);</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  }</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4adb3e3f51c9107e26c9bccf9a188ce2ed">armnn::LayerType::Fill</a> :</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  {</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <a class="code" href="structarmnn_1_1_fill_descriptor.xhtml">FillDescriptor</a> fillDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_fill_descriptor.xhtml">FillDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> </div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  newLayer = m_QuantizedNetwork->AddFillLayer(fillDescriptor, name);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  }</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acab78faff25393e9defd1911cb58133e">armnn::LayerType::FullyConnected</a> :</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  {</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> fullyConnectedDescriptor =</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span> </div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a> biases = constants.size() == 1 ?</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a>{} :</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a>(constants[1]);</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> </div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  std::vector<uint8_t> weightsBacking;</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> qWeights = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[0], weightsBacking);</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalQBiases;</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  std::vector<int32_t> biasesBacking;</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span> </div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="keywordflow">if</span> (biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>())</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  {</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> qBiases = CreateQuantizedBias(layer, qWeights, biases, biasesBacking);</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  optionalQBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(qBiases);</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  }</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span> </div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  newLayer = m_QuantizedNetwork->AddFullyConnectedLayer(fullyConnectedDescriptor,</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  qWeights,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  optionalQBiases,</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  name);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  }</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a> :</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  {</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dataType = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer = m_QuantizedNetwork->AddInputLayer(<span class="keywordtype">id</span>, name);</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> </div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keywordflow">if</span> (m_PreserveType && (dataType == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a> || dataType == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>))</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  {</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* quantizeLayer = m_QuantizedNetwork->AddQuantizeLayer();</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(quantizeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>());</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  RecordLayer(layer, quantizeLayer);</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  }</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  {</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  RecordLayer(layer, inputLayer);</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  }</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  }</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a21baa4498161d195f5bb2e3627344ba4">armnn::LayerType::InstanceNormalization</a> :</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  {</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <a class="code" href="structarmnn_1_1_instance_normalization_descriptor.xhtml">InstanceNormalizationDescriptor</a> instanceNormalizationDescriptor =</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_instance_normalization_descriptor.xhtml">InstanceNormalizationDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span> </div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  newLayer =</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  m_QuantizedNetwork->AddInstanceNormalizationLayer(instanceNormalizationDescriptor, name);</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  }</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ac21dbda57d88c21ec9857f5d1522c488">armnn::LayerType::LogSoftmax</a> :</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  {</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">LogSoftmaxDescriptor</a> logSoftmaxDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">LogSoftmaxDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> </div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  newLayer = m_QuantizedNetwork->AddLogSoftmaxLayer(logSoftmaxDescriptor, name);</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  }</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0a3d6c9ac08ada31c184094bbc67afe00d">armnn::LayerType::Mean</a> :</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  {</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <a class="code" href="structarmnn_1_1_mean_descriptor.xhtml">MeanDescriptor</a> meanDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_mean_descriptor.xhtml">MeanDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> </div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  newLayer = m_QuantizedNetwork->AddMeanLayer(meanDescriptor, name);</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  }</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a27d1a1f7b7c2180e5b20ce9e3d00e2dd">armnn::LayerType::Multiplication</a> :</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  {</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  newLayer = m_QuantizedNetwork->AddMultiplicationLayer(name);</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  }</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aac61f2e17250a818dee4d12b112aa88f">armnn::LayerType::Normalization</a> :</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  {</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml">NormalizationDescriptor</a> normalizationDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml">NormalizationDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span> </div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  newLayer = m_QuantizedNetwork->AddNormalizationLayer(normalizationDescriptor, name);</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  }</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a> :</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  {</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">GetConnection</a>()-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>& dataType = info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  newLayer = m_QuantizedNetwork->AddOutputLayer(<span class="keywordtype">id</span>, name);</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span> </div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="keywordflow">if</span> (m_PreserveType && (dataType == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a> || dataType == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>))</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  {</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* dequantizeLayer = m_QuantizedNetwork->AddDequantizeLayer();</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  RecordLayer(layer, dequantizeLayer);</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  SetQuantizedInputConnections(layer, dequantizeLayer);</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  dequantizeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(newLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  dequantizeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  }</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  {</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  }</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  }</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ade43468adaf6acb2c38ebc0c1176f82f">armnn::LayerType::Pad</a> :</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  {</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span> </div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  newLayer = m_QuantizedNetwork->AddPadLayer(padDescriptor, name);</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  }</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">armnn::LayerType::Permute</a> :</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  {</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <a class="code" href="structarmnn_1_1_permute_descriptor.xhtml">PermuteDescriptor</a> permuteDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_permute_descriptor.xhtml">PermuteDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> </div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  newLayer = m_QuantizedNetwork->AddPermuteLayer(permuteDescriptor, name);</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  }</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad662867a41bfb30b9f75dda2b5849001">armnn::LayerType::Pooling2d</a> :</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  {</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pooling2dDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> </div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  newLayer = m_QuantizedNetwork->AddPooling2dLayer(pooling2dDescriptor, name);</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  }</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a0c5967f09e0669c840ebb1ed0da85e32">armnn::LayerType::Prelu</a> :</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  {</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  newLayer = m_QuantizedNetwork->AddPreluLayer(name);</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  }</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa7c59ccedc6a3bd90c17f3b990afefad">armnn::LayerType::Reshape</a> :</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  {</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">ReshapeDescriptor</a> reshapeDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">ReshapeDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span> </div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  newLayer = m_QuantizedNetwork->AddReshapeLayer(reshapeDescriptor, name);</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  }</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a9d723d04c40bfd81835c0766a698cf63">armnn::LayerType::Resize</a> :</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  {</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span> </div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <a class="code" href="structarmnn_1_1_resize_bilinear_descriptor.xhtml">ResizeBilinearDescriptor</a> resizeBilinearDescriptor =</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_resize_bilinear_descriptor.xhtml">ResizeBilinearDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span> </div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">ResizeDescriptor</a> resizeDescriptor;</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  resizeDescriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">m_Method</a> = <a class="code" href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f">ResizeMethod::Bilinear</a>;</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  resizeDescriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">m_TargetWidth</a> = resizeBilinearDescriptor.<a class="code" href="structarmnn_1_1_resize_bilinear_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">m_TargetWidth</a>;</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  resizeDescriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">m_TargetHeight</a> = resizeBilinearDescriptor.<a class="code" href="structarmnn_1_1_resize_bilinear_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">m_TargetHeight</a>;</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  resizeDescriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = resizeBilinearDescriptor.<a class="code" href="structarmnn_1_1_resize_bilinear_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>;</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span> </div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  newLayer = m_QuantizedNetwork->AddResizeLayer(resizeDescriptor, name);</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  }</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad140d37ad98c12ccd8e1c432f548bcdb">armnn::LayerType::Slice</a> :</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  {</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <a class="code" href="structarmnn_1_1_slice_descriptor.xhtml">SliceDescriptor</a> sliceDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_slice_descriptor.xhtml">SliceDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span> </div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  newLayer = m_QuantizedNetwork->AddSliceLayer(sliceDescriptor, name);</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  }</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a31d953b9d49a6b4378f45097047976d0">armnn::LayerType::Softmax</a> :</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  {</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">SoftmaxDescriptor</a> softmaxDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">SoftmaxDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span> </div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  newLayer = m_QuantizedNetwork->AddSoftmaxLayer(softmaxDescriptor, name);</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  }</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a337c392144dca0d18290c6b4711a2279">armnn::LayerType::SpaceToBatchNd</a> :</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  {</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <a class="code" href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml">SpaceToBatchNdDescriptor</a> spaceToBatchNdDescriptor =</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml">SpaceToBatchNdDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span> </div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  newLayer = m_QuantizedNetwork->AddSpaceToBatchNdLayer(spaceToBatchNdDescriptor, name);</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  }</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a5e7ff12da912dc79e7e547281823fa4a">armnn::LayerType::SpaceToDepth</a> :</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  {</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml">SpaceToDepthDescriptor</a> spaceToDepthDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml">SpaceToDepthDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  newLayer = m_QuantizedNetwork->AddSpaceToDepthLayer(spaceToDepthDescriptor, name);</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  }</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a41cb9b797ebc6f6f6314e3ded935f4cf">armnn::LayerType::Splitter</a> :</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  {</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">SplitterDescriptor</a> splitterDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_views_descriptor.xhtml">SplitterDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  newLayer = m_QuantizedNetwork->AddSplitterLayer(splitterDescriptor, name);</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  }</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2187e1021a911b3807cc1bea2eb1a9ca">armnn::LayerType::Stack</a> :</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  {</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <a class="code" href="structarmnn_1_1_stack_descriptor.xhtml">StackDescriptor</a> stackDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_stack_descriptor.xhtml">StackDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span> </div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  newLayer = m_QuantizedNetwork->AddStackLayer(stackDescriptor, name);</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  }</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa31904f2b3479b5a00137fd985974b4d">armnn::LayerType::StridedSlice</a> :</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  {</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml">StridedSliceDescriptor</a> stridedSliceDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml">StridedSliceDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span> </div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  newLayer = m_QuantizedNetwork->AddStridedSliceLayer(stridedSliceDescriptor, name);</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  }</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6eb8b8b560161603402c0238b3a7d8b0">armnn::LayerType::Subtraction</a> :</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  {</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  newLayer = m_QuantizedNetwork->AddSubtractionLayer( name);</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  }</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a71b23d26c0f5d20416d6c77754f9806a">armnn::LayerType::TransposeConvolution2d</a> :</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  {</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span> </div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a> biases = constants.size() == 1 ?</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a>{} :</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a>(constants[1]);</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <span class="comment">// quantize weights</span></div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  std::vector<uint8_t> weightsBacking;</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> qWeights = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[0], weightsBacking);</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span> </div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="comment">// quantize biases</span></div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  std::vector<int32_t> biasesBacking;</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalQBiases;</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="keywordflow">if</span> (biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>())</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  {</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> qBiases = CreateQuantizedBias(layer, qWeights, biases, biasesBacking);</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  optionalQBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(qBiases);</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  }</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span> </div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  <a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">TransposeConvolution2dDescriptor</a> transposeConvolution2dDescriptor =</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">TransposeConvolution2dDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span> </div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  newLayer = m_QuantizedNetwork->AddTransposeConvolution2dLayer(transposeConvolution2dDescriptor,</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  qWeights,</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  optionalQBiases,</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  name);</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  }</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aaf70b1ac863830a4e1ce6268c8399f54">armnn::LayerType::Transpose</a> :</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  {</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  <a class="code" href="structarmnn_1_1_transpose_descriptor.xhtml">TransposeDescriptor</a> transposeDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="structarmnn_1_1_transpose_descriptor.xhtml">TransposeDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span> </div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  newLayer = m_QuantizedNetwork->AddTransposeLayer(transposeDescriptor, name);</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  }</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  {</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_unimplemented_exception.xhtml">UnimplementedException</a>(<span class="stringliteral">"Unimplemented layer encountered"</span>);</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  }</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  }</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  RecordLayer(layer, newLayer);</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  SetQuantizedInputConnections(layer, newLayer);</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span> }</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span> </div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> }</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span> </div><div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a71b23d26c0f5d20416d6c77754f9806a"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a71b23d26c0f5d20416d6c77754f9806a">armnn::LayerType::TransposeConvolution2d</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4abcd30d7ea97ad20c2cddc0f47e6b70c7"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4abcd30d7ea97ad20c2cddc0f47e6b70c7">armnn::LayerType::ElementwiseUnary</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a9b8e5a95f8c061bbbcdb036915dcb61a"><div class="ttname"><a href="namespacearmnn.xhtml#a9b8e5a95f8c061bbbcdb036915dcb61a">armnn::OffsetScalePair</a></div><div class="ttdeci">std::pair< float, int > OffsetScalePair</div><div class="ttdef"><b>Definition:</b> <a href="_network_quantization_scheme_8hpp_source.xhtml#l00016">NetworkQuantizationScheme.hpp:16</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ae20f0f2826a6549809f050b86274567f"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae20f0f2826a6549809f050b86274567f">armnn::LayerType::Concat</a></div></div>
<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml">armnn::ViewsDescriptor</a></div><div class="ttdoc">A ViewsDescriptor for the SplitterLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00206">Descriptors.hpp:206</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00062">INetwork.hpp:62</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a9c2cba04b6d7ace4fc2a2436b82a5a63"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">armnn::IConnectableLayer::GetNumInputSlots</a></div><div class="ttdeci">virtual unsigned int GetNumInputSlots() const =0</div><div class="ttdoc">Returns the number of connectable input slots. </div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4af6c0e3a1c3cfabd32ae8d3ae741fcf0a"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af6c0e3a1c3cfabd32ae8d3ae741fcf0a">armnn::LayerType::Comparison</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">armnn::TransposeConvolution2dDescriptor</a></div><div class="ttdoc">A TransposeConvolution2dDescriptor for the TransposeConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01213">Descriptors.hpp:1213</a></div></div>
<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional</a></div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00270">Optional.hpp:270</a></div></div>
<div class="ttc" id="structarmnn_1_1_reshape_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_reshape_descriptor.xhtml">armnn::ReshapeDescriptor</a></div><div class="ttdoc">A ReshapeDescriptor for the ReshapeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00832">Descriptors.hpp:832</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a6ee06c6045d0c5b6565a247955ef0fc2"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6ee06c6045d0c5b6565a247955ef0fc2">armnn::LayerType::BatchToSpaceNd</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="structarmnn_1_1_comparison_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_comparison_descriptor.xhtml">armnn::ComparisonDescriptor</a></div><div class="ttdoc">A ComparisonDescriptor for the ComparisonLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00078">Descriptors.hpp:78</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_bilinear_descriptor_xhtml_adcf5037208faac36c0788239a073f75c"><div class="ttname"><a href="structarmnn_1_1_resize_bilinear_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">armnn::ResizeBilinearDescriptor::m_TargetWidth</a></div><div class="ttdeci">uint32_t m_TargetWidth</div><div class="ttdoc">Target width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00782">Descriptors.hpp:782</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_bilinear_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_resize_bilinear_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::ResizeBilinearDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00786">Descriptors.hpp:786</a></div></div>
<div class="ttc" id="classarmnn_1_1_range_tracker_xhtml"><div class="ttname"><a href="classarmnn_1_1_range_tracker.xhtml">armnn::RangeTracker</a></div><div class="ttdef"><b>Definition:</b> <a href="_range_tracker_8hpp_source.xhtml#l00017">RangeTracker.hpp:17</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a></div><div class="ttdoc">A Convolution2dDescriptor for the Convolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00408">Descriptors.hpp:408</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a2187e1021a911b3807cc1bea2eb1a9ca"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2187e1021a911b3807cc1bea2eb1a9ca">armnn::LayerType::Stack</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_a7ddaf04177053a536f0e7be83a642bc6"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#a7ddaf04177053a536f0e7be83a642bc6">armnn::OutputSlot::GetOwningLayer</a></div><div class="ttdeci">Layer & GetOwningLayer() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00115">Layer.hpp:115</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aa31904f2b3479b5a00137fd985974b4d"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa31904f2b3479b5a00137fd985974b4d">armnn::LayerType::StridedSlice</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">armnn::LayerType::Activation</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a869254cb56968986a78a79e1d6d4a86b"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">armnn::ResizeDescriptor::m_Method</a></div><div class="ttdeci">ResizeMethod m_Method</div><div class="ttdoc">The Interpolation method to use (Bilinear, NearestNeighbor). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00821">Descriptors.hpp:821</a></div></div>
<div class="ttc" id="classarmnn_1_1_quantizer_strategy_xhtml_ab79e40d3d0c6f4c9e24534f251306add"><div class="ttname"><a href="classarmnn_1_1_quantizer_strategy.xhtml#ab79e40d3d0c6f4c9e24534f251306add">armnn::QuantizerStrategy::ExecuteStrategy</a></div><div class="ttdeci">void ExecuteStrategy(const armnn::IConnectableLayer *layer, const BaseDescriptor &descriptor, const std::vector< armnn::ConstTensor > &constants, const char *name, const armnn::LayerBindingId id) override</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_strategy_8cpp_source.xhtml#l00118">QuantizerStrategy.cpp:118</a></div></div>
<div class="ttc" id="classarmnn_1_1_unimplemented_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_unimplemented_exception.xhtml">armnn::UnimplementedException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00098">Exceptions.hpp:98</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_network_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml">armnn::INetwork</a></div><div class="ttdoc">Main network class which provides the interface for building up a neural network. ...</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00178">INetwork.hpp:178</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aac61f2e17250a818dee4d12b112aa88f"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aac61f2e17250a818dee4d12b112aa88f">armnn::LayerType::Normalization</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a337c392144dca0d18290c6b4711a2279"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a337c392144dca0d18290c6b4711a2279">armnn::LayerType::SpaceToBatchNd</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4acab78faff25393e9defd1911cb58133e"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acab78faff25393e9defd1911cb58133e">armnn::LayerType::FullyConnected</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::DataType::Signed64</a></div></div>
<div class="ttc" id="classarmnn_1_1_input_slot_xhtml"><div class="ttname"><a href="classarmnn_1_1_input_slot.xhtml">armnn::InputSlot</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00041">Layer.hpp:41</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__software__tools_8dox_source.xhtml#l00006">01_00_software_tools.dox:6</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &&...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a27d1a1f7b7c2180e5b20ce9e3d00e2dd"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a27d1a1f7b7c2180e5b20ce9e3d00e2dd">armnn::LayerType::Multiplication</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a21baa4498161d195f5bb2e3627344ba4"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a21baa4498161d195f5bb2e3627344ba4">armnn::LayerType::InstanceNormalization</a></div></div>
<div class="ttc" id="structarmnn_1_1_space_to_depth_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_space_to_depth_descriptor.xhtml">armnn::SpaceToDepthDescriptor</a></div><div class="ttdoc">A SpaceToDepthDescriptor for the SpaceToDepthLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00884">Descriptors.hpp:884</a></div></div>
<div class="ttc" id="classarmnn_1_1_optional_reference_switch_xhtml_a77c7d528ac063d870b8c8426ec81c1c3"><div class="ttname"><a href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">armnn::OptionalReferenceSwitch< std::is_reference< T >::value, T >::value</a></div><div class="ttdeci">const T & value() const</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00146">Optional.hpp:146</a></div></div>
<div class="ttc" id="structarmnn_1_1_i_quantization_scheme_xhtml_a6a5561395e9693f02258b49dfcc009b4"><div class="ttname"><a href="structarmnn_1_1_i_quantization_scheme.xhtml#a6a5561395e9693f02258b49dfcc009b4">armnn::IQuantizationScheme::ComputeScheme</a></div><div class="ttdeci">virtual OffsetScalePair ComputeScheme(double min, double max) const =0</div></div>
<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">armnn::BatchToSpaceNdDescriptor</a></div><div class="ttdoc">A BatchToSpaceNdDescriptor for the BatchToSpaceNdLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00673">Descriptors.hpp:673</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ab8cf8f9fb6792e654c2d8d8382f6f01b"><div class="ttname"><a href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a></div><div class="ttdeci">int LayerBindingId</div><div class="ttdoc">Type of identifiers for bindable layers (inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00210">Types.hpp:210</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a0c5967f09e0669c840ebb1ed0da85e32"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a0c5967f09e0669c840ebb1ed0da85e32">armnn::LayerType::Prelu</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_abc05539fc6e7907f32ef0fb242e3b3b0a3d6c9ac08ada31c184094bbc67afe00d"><div class="ttname"><a href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0a3d6c9ac08ada31c184094bbc67afe00d">armnn::ReduceOperation::Mean</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &tensorInfo)=0</div></div>
<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml">armnn::ResizeDescriptor</a></div><div class="ttdoc">A ResizeDescriptor for the ResizeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00794">Descriptors.hpp:794</a></div></div>
<div class="ttc" id="classarmnn_1_1_range_tracker_xhtml_a507bae23f59e94b4161886ebe663cdf4"><div class="ttname"><a href="classarmnn_1_1_range_tracker.xhtml#a507bae23f59e94b4161886ebe663cdf4">armnn::RangeTracker::GetRange</a></div><div class="ttdeci">MinMaxRange GetRange(LayerGuid guid, unsigned int idx) const</div><div class="ttdoc">Retrieve the Range for a particular output slot on a particular layer. </div><div class="ttdef"><b>Definition:</b> <a href="_range_tracker_8cpp_source.xhtml#l00029">RangeTracker.cpp:29</a></div></div>
<div class="ttc" id="structarmnn_1_1_base_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a></div><div class="ttdoc">Base class for all descriptors. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00022">Descriptors.hpp:22</a></div></div>
<div class="ttc" id="structarmnn_1_1_stack_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_stack_descriptor.xhtml">armnn::StackDescriptor</a></div><div class="ttdoc">A StackDescriptor for the StackLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01024">Descriptors.hpp:1024</a></div></div>
<div class="ttc" id="_polymorphic_downcast_8hpp_xhtml"><div class="ttname"><a href="_polymorphic_downcast_8hpp.xhtml">PolymorphicDowncast.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_pad_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a></div><div class="ttdoc">A PadDescriptor for the PadLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00975">Descriptors.hpp:975</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a6eb8b8b560161603402c0238b3a7d8b0"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6eb8b8b560161603402c0238b3a7d8b0">armnn::LayerType::Subtraction</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00032">Types.hpp:32</a></div></div>
<div class="ttc" id="_quantizer_strategy_8hpp_xhtml"><div class="ttname"><a href="_quantizer_strategy_8hpp.xhtml">QuantizerStrategy.hpp</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml_a91c4dfde57907d7698c7531785690a7f"><div class="ttname"><a href="_assert_8hpp.xhtml#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.xhtml#l00015">Assert.hpp:15</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a></div><div class="ttdoc">An output connection slot for a layer. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00038">INetwork.hpp:38</a></div></div>
<div class="ttc" id="classarmnn_1_1_quantizer_strategy_xhtml_a7f9b9f65046b39e047e674ab5cd7a18f"><div class="ttname"><a href="classarmnn_1_1_quantizer_strategy.xhtml#a7f9b9f65046b39e047e674ab5cd7a18f">armnn::QuantizerStrategy::QuantizerStrategy</a></div><div class="ttdeci">QuantizerStrategy(const RangeTracker &rangeTracker, const IQuantizationScheme *quantizationScheme, bool preserveType)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_strategy_8cpp_source.xhtml#l00012">QuantizerStrategy.cpp:12</a></div></div>
<div class="ttc" id="structarmnn_1_1_arg_min_max_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_descriptor.xhtml">armnn::ArgMinMaxDescriptor</a></div><div class="ttdoc">An ArgMinMaxDescriptor for ArgMinMaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00056">Descriptors.hpp:56</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00452">Tensor.cpp:452</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00194">Tensor.hpp:194</a></div></div>
<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a></div><div class="ttdoc">An OriginsDescriptor for the ConcatLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00163">Descriptors.hpp:163</a></div></div>
<div class="ttc" id="classarmnn_1_1_optional_base_xhtml_a86b749ce2c4bc627fa8a1fcfaf0e314f"><div class="ttname"><a href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">armnn::OptionalBase::has_value</a></div><div class="ttdeci">bool has_value() const noexcept</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00053">Optional.hpp:53</a></div></div>
<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a></div><div class="ttdoc">A FullyConnectedDescriptor for the FullyConnectedLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00389">Descriptors.hpp:389</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_afb5e65c770f6cee222db8af7581541a6"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#afb5e65c770f6cee222db8af7581541a6">armnn::IConnectableLayer::GetGuid</a></div><div class="ttdeci">virtual LayerGuid GetGuid() const =0</div><div class="ttdoc">Returns the unique id of the layer. </div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">armnn::LayerType::Permute</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a9d723d04c40bfd81835c0766a698cf63"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a9d723d04c40bfd81835c0766a698cf63">armnn::LayerType::Resize</a></div></div>
<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00314">Tensor.hpp:314</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_adcf5037208faac36c0788239a073f75c"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">armnn::ResizeDescriptor::m_TargetWidth</a></div><div class="ttdeci">uint32_t m_TargetWidth</div><div class="ttdoc">Target width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00816">Descriptors.hpp:816</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a">armnn::LayerType::Convolution2d</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ade43468adaf6acb2c38ebc0c1176f82f"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ade43468adaf6acb2c38ebc0c1176f82f">armnn::LayerType::Pad</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml">armnn::OutputSlot</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00083">Layer.hpp:83</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a31d953b9d49a6b4378f45097047976d0"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a31d953b9d49a6b4378f45097047976d0">armnn::LayerType::Softmax</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml_a5698be69cbd5dfe6c28fcd9867e8cbed"><div class="ttname"><a href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a></div><div class="ttdeci">#define ARMNN_ASSERT(COND)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00014">Assert.hpp:14</a></div></div>
<div class="ttc" id="classarmnn_1_1_input_slot_xhtml_a9effd325a6d512a3f8ff4bd207d53255"><div class="ttname"><a href="classarmnn_1_1_input_slot.xhtml#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.xhtml#l00055">Layer.hpp:55</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aa7c59ccedc6a3bd90c17f3b990afefad"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa7c59ccedc6a3bd90c17f3b990afefad">armnn::LayerType::Reshape</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ad662867a41bfb30b9f75dda2b5849001"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad662867a41bfb30b9f75dda2b5849001">armnn::LayerType::Pooling2d</a></div></div>
<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a></div><div class="ttdoc">An ActivationDescriptor for the ActivationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00025">Descriptors.hpp:25</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_a8aeddebdcf02e1832b22203c08a6b678"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#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.xhtml#l00282">Tensor.hpp:282</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f">armnn::LayerType::Addition</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_bilinear_descriptor_xhtml_a46c3fa15c46fb0d1dcdc24d0ea5cb5cd"><div class="ttname"><a href="structarmnn_1_1_resize_bilinear_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">armnn::ResizeBilinearDescriptor::m_TargetHeight</a></div><div class="ttdeci">uint32_t m_TargetHeight</div><div class="ttdoc">Target height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00784">Descriptors.hpp:784</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a46c3fa15c46fb0d1dcdc24d0ea5cb5cd"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">armnn::ResizeDescriptor::m_TargetHeight</a></div><div class="ttdeci">uint32_t m_TargetHeight</div><div class="ttdoc">Target height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00818">Descriptors.hpp:818</a></div></div>
<div class="ttc" id="structarmnn_1_1_slice_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_slice_descriptor.xhtml">armnn::SliceDescriptor</a></div><div class="ttdoc">A SliceDescriptor for the SliceLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01001">Descriptors.hpp:1001</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_adceb04ae84c524e4d01881e3754a4d59"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">armnn::IConnectableLayer::GetType</a></div><div class="ttdeci">virtual LayerType GetType() const =0</div><div class="ttdoc">Returns the armnn::LayerType of this layer. </div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a5e7ff12da912dc79e7e547281823fa4a"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a5e7ff12da912dc79e7e547281823fa4a">armnn::LayerType::SpaceToDepth</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4af97adbfc88b7012a0243215b1076e7e7"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af97adbfc88b7012a0243215b1076e7e7">armnn::LayerType::DepthwiseConvolution2d</a></div></div>
<div class="ttc" id="structarmnn_1_1_space_to_batch_nd_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml">armnn::SpaceToBatchNdDescriptor</a></div><div class="ttdoc">A SpaceToBatchNdDescriptor for the SpaceToBatchNdLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00852">Descriptors.hpp:852</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255">armnn::LayerType::Constant</a></div></div>
<div class="ttc" id="structarmnn_1_1_i_quantization_scheme_xhtml_ad23181f9f8fcc85758f62c49fc7ca23f"><div class="ttname"><a href="structarmnn_1_1_i_quantization_scheme.xhtml#ad23181f9f8fcc85758f62c49fc7ca23f">armnn::IQuantizationScheme::GetDataType</a></div><div class="ttdeci">virtual DataType GetDataType() const =0</div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a41cb9b797ebc6f6f6314e3ded935f4cf"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a41cb9b797ebc6f6f6314e3ded935f4cf">armnn::LayerType::Splitter</a></div></div>
<div class="ttc" id="structarmnn_1_1_elementwise_unary_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_elementwise_unary_descriptor.xhtml">armnn::ElementwiseUnaryDescriptor</a></div><div class="ttdoc">A ElementwiseUnaryDescriptor for the ElementwiseUnaryLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00098">Descriptors.hpp:98</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f"><div class="ttname"><a href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f">armnn::ResizeMethod::Bilinear</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a2139684546b147c106b329f41547640c"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2139684546b147c106b329f41547640c">armnn::LayerType::ArgMinMax</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot & GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div>
<div class="ttc" id="structarmnn_1_1_mean_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_mean_descriptor.xhtml">armnn::MeanDescriptor</a></div><div class="ttdoc">A MeanDescriptor for the MeanLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00951">Descriptors.hpp:951</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_input_slot_xhtml_a81fbf6103761e55061b62ba989b00f10"><div class="ttname"><a href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">armnn::IInputSlot::GetConnection</a></div><div class="ttdeci">virtual const IOutputSlot * GetConnection() const =0</div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ae4743c3ec15d1d84169b17264634692e"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae4743c3ec15d1d84169b17264634692e">armnn::LayerType::BatchNormalization</a></div></div>
<div class="ttc" id="structarmnn_1_1_i_quantization_scheme_xhtml"><div class="ttname"><a href="structarmnn_1_1_i_quantization_scheme.xhtml">armnn::IQuantizationScheme</a></div><div class="ttdef"><b>Definition:</b> <a href="_network_quantization_scheme_8hpp_source.xhtml#l00018">NetworkQuantizationScheme.hpp:18</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a375ca3cff9f1b005d1412dc5f3cf5b6e"><div class="ttname"><a href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t< std::is_unsigned< Source >::value &&std::is_unsigned< Dest >::value, Dest > numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00035">NumericCast.hpp:35</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_descriptor.xhtml">armnn::TransposeDescriptor</a></div><div class="ttdoc">A TransposeDescriptor for the TransposeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01263">Descriptors.hpp:1263</a></div></div>
<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml">armnn::StridedSliceDescriptor</a></div><div class="ttdoc">A StridedSliceDescriptor for the StridedSliceLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01076">Descriptors.hpp:1076</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a9943775a364fc4ab53b85ac88f311886"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">armnn::IOutputSlot::GetTensorInfo</a></div><div class="ttdeci">virtual const TensorInfo & GetTensorInfo() const =0</div></div>
<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot & GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index. </div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ac21dbda57d88c21ec9857f5d1522c488"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ac21dbda57d88c21ec9857f5d1522c488">armnn::LayerType::LogSoftmax</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a731729ad1b2c0eb9399b62c770b3482d"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a731729ad1b2c0eb9399b62c770b3482d">armnn::LayerType::DepthToSpace</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4adb3e3f51c9107e26c9bccf9a188ce2ed"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4adb3e3f51c9107e26c9bccf9a188ce2ed">armnn::LayerType::Fill</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &destination)=0</div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a></div><div class="ttdoc">A Pooling2dDescriptor for the Pooling2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00329">Descriptors.hpp:329</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a310dd804fd70eadb1e8854325e63f0bd"><div class="ttname"><a href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">armnn::CreateQuantizedConst</a></div><div class="ttdeci">ConstTensor CreateQuantizedConst(const ConstTensor &tensor, std::vector< uint8_t > &backing)</div><div class="ttdef"><b>Definition:</b> <a href="_network_quantizer_utils_8cpp_source.xhtml#l00015">NetworkQuantizerUtils.cpp:15</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a></div><div class="ttdoc">A NormalizationDescriptor for the NormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00567">Descriptors.hpp:567</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aaf70b1ac863830a4e1ce6268c8399f54"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aaf70b1ac863830a4e1ce6268c8399f54">armnn::LayerType::Transpose</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::ResizeDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00823">Descriptors.hpp:823</a></div></div>
<div class="ttc" id="structarmnn_1_1_instance_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_instance_normalization_descriptor.xhtml">armnn::InstanceNormalizationDescriptor</a></div><div class="ttdoc">An InstanceNormalizationDescriptor for InstanceNormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00645">Descriptors.hpp:645</a></div></div>
<div class="ttc" id="structarmnn_1_1_resize_bilinear_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_resize_bilinear_descriptor.xhtml">armnn::ResizeBilinearDescriptor</a></div><div class="ttdoc">A ResizeBilinearDescriptor for the ResizeBilinearLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00762">Descriptors.hpp:762</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_ada2ad7d1caeeb4ef6195c8925fad6a65"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#ada2ad7d1caeeb4ef6195c8925fad6a65">armnn::OutputSlot::GetTensorInfo</a></div><div class="ttdeci">const TensorInfo & GetTensorInfo() const override</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00063">Layer.cpp:63</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ad140d37ad98c12ccd8e1c432f548bcdb"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad140d37ad98c12ccd8e1c432f548bcdb">armnn::LayerType::Slice</a></div></div>
<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::SoftmaxDescriptor</a></div><div class="ttdoc">A SoftmaxDescriptor for the SoftmaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00139">Descriptors.hpp:139</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_input_slot_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_input_slot.xhtml">armnn::IInputSlot</a></div><div class="ttdoc">An input connection slot for a layer. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00025">INetwork.hpp:25</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a></div><div class="ttdoc">A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00460">Descriptors.hpp:460</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml">armnn::Layer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00210">Layer.hpp:210</a></div></div>
<div class="ttc" id="structarmnn_1_1_fill_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fill_descriptor.xhtml">armnn::FillDescriptor</a></div><div class="ttdoc">A FillDescriptor for the FillLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00723">Descriptors.hpp:723</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml">armnn::BatchNormalizationDescriptor</a></div><div class="ttdoc">A BatchNormalizationDescriptor for the BatchNormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00626">Descriptors.hpp:626</a></div></div>
<div class="ttc" id="structarmnn_1_1_permute_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_permute_descriptor.xhtml">armnn::PermuteDescriptor</a></div><div class="ttdoc">A PermuteDescriptor for the PermuteLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00118">Descriptors.hpp:118</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_a8dc12f0ee5b232d397bd18ced1a72a64"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a8dc12f0ee5b232d397bd18ced1a72a64">armnn::Layer::GetGuid</a></div><div class="ttdeci">LayerGuid GetGuid() const final</div><div class="ttdoc">Returns the unique id of the layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00322">Layer.hpp:322</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_a27545b353e48a739981d345b64bb9dc9"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#a27545b353e48a739981d345b64bb9dc9">armnn::OutputSlot::CalculateIndexOnOwner</a></div><div class="ttdeci">unsigned int CalculateIndexOnOwner() const override</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00130">Layer.cpp:130</a></div></div>
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