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<div class="title">WorkloadFactory.cpp</div> </div>
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<a href="_workload_factory_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include <<a class="code" href="_layer_8hpp.xhtml">Layer.hpp</a>></span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include <<a class="code" href="_layers_fwd_8hpp.xhtml">LayersFwd.hpp</a>></span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_types_8hpp.xhtml">armnn/Types.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_i_backend_internal_8hpp.xhtml">armnn/backends/IBackendInternal.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_i_layer_support_8hpp.xhtml">armnn/backends/ILayerSupport.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_backend_helper_8hpp.xhtml">armnn/BackendHelper.hpp</a>></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_backend_registry_8hpp.xhtml">armnn/BackendRegistry.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <<a class="code" href="_polymorphic_downcast_8hpp.xhtml">armnn/utility/PolymorphicDowncast.hpp</a>></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <<a class="code" href="_transform_iterator_8hpp.xhtml">armnn/utility/TransformIterator.hpp</a>></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <<a class="code" href="include_2armnn_2backends_2_workload_factory_8hpp.xhtml">armnn/backends/WorkloadFactory.hpp</a>></span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <<a class="code" href="include_2armnn_2backends_2_tensor_handle_8hpp.xhtml">armnn/backends/TensorHandle.hpp</a>></span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> </div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include <sstream></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="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> {</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> {</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="keyword">using</span> LayerList = std::list<Layer*>;</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="keyword">using</span> Iterator = LayerList::const_iterator; <span class="comment">// Const so pointers in the list can't be modified externally.</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="keyword">const</span> TensorInfo OverrideDataType(<span class="keyword">const</span> TensorInfo& info, Optional<DataType> type)</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> {</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keywordflow">if</span> (!type)</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  {</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keywordflow">return</span> info;</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  }</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordflow">return</span> TensorInfo(info.GetShape(),</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  type.value(),</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  info.GetQuantizationScale(),</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  info.GetQuantizationOffset(),</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  info.IsConstant());</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> }</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> } <span class="comment">// anonymous namespace</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="keyword">inline</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::DataType></a> <a class="code" href="namespacearmnn.xhtml#ada0fb4f79f3673b4ebd94a42175bf78d">GetBiasTypeFromWeightsType</a>(<a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::DataType></a> weightsType)</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> {</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordflow">if</span> (!weightsType)</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  {</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keywordflow">return</span> weightsType;</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> </div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordflow">switch</span>(weightsType.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>())</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  {</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>:</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a>:</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordflow">return</span> weightsType;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>:</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>:</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>:</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(<span class="keyword">false</span>, <span class="stringliteral">"GetBiasTypeFromWeightsType(): Unsupported data type."</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>  <span class="keywordflow">return</span> <a class="code" href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a>();</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> }</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> </div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> <span class="keywordtype">bool</span> IWorkloadFactory::IsLayerConfigurationSupported(<span class="keyword">const</span> BackendId& backendId,</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keyword">const</span> IConnectableLayer& connectableLayer,</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  Optional<DataType> dataType,</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  std::string& outReasonIfUnsupported,</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a5b6893cda5b69359a4244c06054da18f">ModelOptions</a>& modelOptions)</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> {</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  Optional<std::string&> reason = outReasonIfUnsupported;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keywordtype">bool</span> result;</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn_serializer.xhtml#a9a8118be7780e95363d631cbca7e7800">Layer</a>& layer = *(PolymorphicDowncast<const Layer*>(&connectableLayer));</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keyword">auto</span> <span class="keyword">const</span>& backendRegistry = <a class="code" href="namespacearmnn.xhtml#ac2807505b850738bc8a1991ce669dd47">BackendRegistryInstance</a>();</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordflow">if</span> (!backendRegistry.IsBackendRegistered(backendId))</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  {</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  std::stringstream ss;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  ss << connectableLayer.GetName() << <span class="stringliteral">" is not supported on "</span> << backendId</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  << <span class="stringliteral">" because this backend is not registered."</span>;</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  outReasonIfUnsupported = ss.str();</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">return</span> <span class="keyword">false</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> </div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keyword">auto</span> backendFactory = backendRegistry.GetFactory(backendId);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keyword">auto</span> backendObject = backendFactory();</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keyword">auto</span> layerSupportObject = LayerSupportHandle(backendObject->GetLayerSupport(modelOptions), backendId);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> </div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordflow">switch</span>(layer.GetType())</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="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">LayerType::Activation</a>:</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  {</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const ActivationLayer*>(&layer);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  result = layerSupportObject.IsActivationSupported(</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  OverrideDataType(input, dataType),</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  cLayer->GetParameters(),</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  reason);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  }</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f">LayerType::Addition</a>:</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="keyword">const</span> TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keyword">const</span> TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  result = layerSupportObject.IsAdditionSupported(</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  OverrideDataType(input0, dataType),</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  OverrideDataType(input1, dataType),</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  reason);</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  }</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2139684546b147c106b329f41547640c">LayerType::ArgMinMax</a>:</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  {</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const ArgMinMaxLayer*>(&layer);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keyword">const</span> ArgMinMaxDescriptor& descriptor = cLayer->GetParameters();</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> </div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  result = layerSupportObject.IsArgMinMaxSupported(</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  OverrideDataType(input, dataType),</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  OverrideDataType(output, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>),</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  descriptor,</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  reason);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  }</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae4743c3ec15d1d84169b17264634692e">LayerType::BatchNormalization</a>:</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  {</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const BatchNormalizationLayer*>(&layer);</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keyword">const</span> TensorInfo& mean = cLayer->m_Mean->GetTensorInfo();</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keyword">const</span> TensorInfo& var = cLayer->m_Variance->GetTensorInfo();</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keyword">const</span> TensorInfo& beta = cLayer->m_Beta->GetTensorInfo();</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keyword">const</span> TensorInfo& gamma = cLayer->m_Gamma->GetTensorInfo();</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  result = layerSupportObject.IsBatchNormalizationSupported(</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  OverrideDataType(input, dataType),</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  OverrideDataType(mean, dataType),</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  OverrideDataType(var, dataType),</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  OverrideDataType(beta, dataType),</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  OverrideDataType(gamma, dataType),</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  cLayer->GetParameters(),</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  reason);</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  }</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6ee06c6045d0c5b6565a247955ef0fc2">LayerType::BatchToSpaceNd</a>:</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>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const BatchToSpaceNdLayer*>(&layer);</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> </div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  result = layerSupportObject.IsBatchToSpaceNdSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  cLayer->GetParameters(),</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  reason);</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  }</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4cd9f3996d60790cd11c04f842ebc43c">LayerType::Cast</a>:</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  {</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> </div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  result = layerSupportObject.IsCastSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  reason);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  }</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a0ca5f33c1d35fd4105d3a26a2823f9dd">LayerType::ChannelShuffle</a>:</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  {</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const ChannelShuffleLayer*>(&layer);</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>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> </div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keyword">const</span> ChannelShuffleDescriptor descriptor = cLayer->GetParameters();</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> </div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  result = layerSupportObject.IsChannelShuffleSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  descriptor,</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  reason);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  }</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af6c0e3a1c3cfabd32ae8d3ae741fcf0a">LayerType::Comparison</a>:</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  {</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const ComparisonLayer*>(&layer);</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>  <span class="keyword">const</span> TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keyword">const</span> TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> </div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  result = layerSupportObject.IsComparisonSupported(OverrideDataType(input0, dataType),</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  OverrideDataType(input1, dataType),</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  OverrideDataType(output, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a>),</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  cLayer->GetParameters(),</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  reason);</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  }</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a14d24d90ab4ba2956e92e27890ba4c91acb17869fe51048b5a5c4c6106551a255">LayerType::Constant</a>:</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  {</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  result = layerSupportObject.IsConstantSupported(OverrideDataType(output, dataType), reason);</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  }</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2ba4c46787312a2467153f40c542851a">LayerType::ConvertBf16ToFp32</a>:</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  {</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  result = layerSupportObject.IsConvertBf16ToFp32Supported(input, output, reason);</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  }</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a912a4b4d73726c282e3f79aa2c390d6c">LayerType::ConvertFp16ToFp32</a>:</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  {</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  result = layerSupportObject.IsConvertFp16ToFp32Supported(input, output, reason);</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keywordflow">break</span>;</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6512859957de2cf2a5fe4dccb07bd9da">LayerType::ConvertFp32ToBf16</a>:</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  {</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  result = layerSupportObject.IsConvertFp32ToBf16Supported(input, output, reason);</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  }</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4addf4f83b056acd5549949fc0350e9aad">LayerType::ConvertFp32ToFp16</a>:</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  {</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  result = layerSupportObject.IsConvertFp32ToFp16Supported(input, output, reason);</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  }</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a">LayerType::Convolution2d</a>:</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  {</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const Convolution2dLayer*>(&layer);</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span> </div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keyword">const</span> TensorInfo input = OverrideDataType(layer.GetInputSlot(0).GetConnection()->GetTensorInfo(),</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  dataType);</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="keyword">const</span> TensorInfo output = OverrideDataType(layer.GetOutputSlot(0).GetTensorInfo(), dataType);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(layer.GetInputSlot(1).GetConnection(),</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="stringliteral">"Convolution2dLayer: Weights should be connected as a Constant Layer."</span>);</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keyword">const</span> TensorInfo weights = OverrideDataType(layer.GetInputSlot(1).GetConnection()->GetTensorInfo(),</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  dataType);</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span> </div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keyword">const</span> Convolution2dDescriptor& descriptor = cLayer->GetParameters();</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> </div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="comment">// Construct optional biases object based on the value of m_BiasEnabled</span></div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  Optional<TensorInfo> biases;</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keywordflow">if</span> (descriptor.m_BiasEnabled)</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="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(layer.GetInputSlot(2).GetConnection(),</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="stringliteral">"Convolution2dLayer: Bias should be connected as a Constant Layer."</span>);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  biases = OverrideDataType(layer.GetInputSlot(2).GetConnection()->GetTensorInfo(),</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <a class="code" href="namespacearmnn.xhtml#ada0fb4f79f3673b4ebd94a42175bf78d">GetBiasTypeFromWeightsType</a>(dataType));</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  }</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> </div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  result = layerSupportObject.IsConvolution2dSupported(</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  input,</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  output,</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  descriptor,</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  weights,</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  biases,</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  reason);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  }</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a583550d0f265fd3756f7de0e42c51953">LayerType::Convolution3d</a>:</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  {</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const Convolution3dLayer*>(&layer);</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> </div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keyword">const</span> TensorInfo input = OverrideDataType(layer.GetInputSlot(0).GetConnection()->GetTensorInfo(),</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  dataType);</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <span class="keyword">const</span> TensorInfo output = OverrideDataType(layer.GetOutputSlot(0).GetTensorInfo(), dataType);</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> </div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(layer.GetInputSlot(1).GetConnection(),</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="stringliteral">"Convolution3dLayer: Weights should be connected as a Constant Layer."</span>);</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="keyword">const</span> TensorInfo weights = OverrideDataType(layer.GetInputSlot(1).GetConnection()->GetTensorInfo(),</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  dataType);</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> </div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keyword">const</span> Convolution3dDescriptor& descriptor = cLayer->GetParameters();</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> </div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="comment">// Construct optional biases object based on the value of m_BiasEnabled</span></div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  Optional<TensorInfo> biases;</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="keywordflow">if</span> (descriptor.m_BiasEnabled)</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  {</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  biases = OverrideDataType(layer.GetInputSlot(2).GetConnection()->GetTensorInfo(),</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <a class="code" href="namespacearmnn.xhtml#ada0fb4f79f3673b4ebd94a42175bf78d">GetBiasTypeFromWeightsType</a>(dataType));</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  }</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span> </div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  result = layerSupportObject.IsConvolution3dSupported(</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  input,</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  output,</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  descriptor,</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  weights,</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  biases,</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  reason);</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  }</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa603905470e2a5b8c13e96b579ef0dba">LayerType::Debug</a>:</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  {</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</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>  result = layerSupportObject.IsDebugSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  reason);</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="keywordflow">break</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a731729ad1b2c0eb9399b62c770b3482d">LayerType::DepthToSpace</a>:</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  {</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const DepthToSpaceLayer*>(&layer);</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> </div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> </div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  result = layerSupportObject.IsDepthToSpaceSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  cLayer->GetParameters(),</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  reason);</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  }</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af97adbfc88b7012a0243215b1076e7e7">LayerType::DepthwiseConvolution2d</a>:</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  {</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const DepthwiseConvolution2dLayer*>(&layer);</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <span class="keyword">const</span> TensorInfo& input = OverrideDataType(layer.GetInputSlot(0).GetConnection()->GetTensorInfo(),</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  dataType);</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <span class="keyword">const</span> TensorInfo& output = OverrideDataType(layer.GetOutputSlot(0).GetTensorInfo(), dataType);</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="keyword">const</span> TensorInfo& weights = OverrideDataType(layer.GetInputSlot(1).GetConnection()->GetTensorInfo(),</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  dataType);</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> </div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(cLayer->GetInputSlot(1).GetConnection() != <span class="keyword">nullptr</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="keyword">const</span> DepthwiseConvolution2dDescriptor& descriptor = cLayer->GetParameters();</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>  <span class="comment">// Construct optional biases object based on the value of m_BiasEnabled</span></div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  Optional<TensorInfo> biases;</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keywordflow">if</span> (descriptor.m_BiasEnabled)</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  {</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  biases = OverrideDataType(cLayer->GetInputSlot(2).GetConnection()->GetTensorInfo(),</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <a class="code" href="namespacearmnn.xhtml#ada0fb4f79f3673b4ebd94a42175bf78d">GetBiasTypeFromWeightsType</a>(dataType));</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> </div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  result = layerSupportObject.IsDepthwiseConvolutionSupported(input,</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  output,</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  descriptor,</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  weights,</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  biases,</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  reason);</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <span class="keywordflow">break</span>;</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">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a9bc35e069257a508e14ed82965a8661d">LayerType::Dequantize</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>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span> </div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  result = layerSupportObject.IsDequantizeSupported(input,</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  reason);</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  }</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a1db19222ac424bd7162142ddf929fd2a">LayerType::DetectionPostProcess</a>:</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  {</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const DetectionPostProcessLayer*>(&layer);</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="keyword">const</span> TensorInfo& boxEncodings = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="keyword">const</span> TensorInfo& scores = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="keyword">const</span> TensorInfo& anchors = cLayer->m_Anchors->GetTensorInfo();</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span> </div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <span class="keyword">const</span> TensorInfo& detectionBoxes = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <span class="keyword">const</span> TensorInfo& detectionClasses = layer.GetOutputSlot(1).GetTensorInfo();</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="keyword">const</span> TensorInfo& detectionScores = layer.GetOutputSlot(2).GetTensorInfo();</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <span class="keyword">const</span> TensorInfo& numDetections = layer.GetOutputSlot(3).GetTensorInfo();</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span> </div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="keyword">const</span> DetectionPostProcessDescriptor& descriptor = cLayer->GetParameters();</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  result = layerSupportObject.IsDetectionPostProcessSupported(boxEncodings,</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  scores,</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  anchors,</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  detectionBoxes,</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  detectionClasses,</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  detectionScores,</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  numDetections,</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  descriptor,</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  reason);</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  }</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4abcd30d7ea97ad20c2cddc0f47e6b70c7">LayerType::ElementwiseUnary</a>:</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  {</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const ElementwiseUnaryLayer*>(&layer);</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span> </div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span> </div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  result = layerSupportObject.IsElementwiseUnarySupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  cLayer->GetParameters(),</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  reason);</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#a56943a0946e5f15e5e58054b8e7a04a4adb3e3f51c9107e26c9bccf9a188ce2ed">LayerType::Fill</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>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const FillLayer*>(&layer);</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  <span class="keyword">const</span> FillDescriptor& descriptor = cLayer->GetParameters();</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span> </div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  result = layerSupportObject.IsFillSupported(</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  OverrideDataType(input, dataType),</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  descriptor,</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  reason);</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  }</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a3489c7b05e180496cb2ce8ac73887f48">LayerType::FakeQuantization</a>:</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  {</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const FakeQuantizationLayer*>(&layer);</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  result = layerSupportObject.IsFakeQuantizationSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  cLayer->GetParameters(),</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  reason);</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  }</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">LayerType::Floor</a>:</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  {</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  result = layerSupportObject.IsFloorSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  reason);</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  }</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acab78faff25393e9defd1911cb58133e">LayerType::FullyConnected</a>:</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>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const FullyConnectedLayer*>(&layer);</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span> </div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <span class="keyword">const</span> FullyConnectedDescriptor& descriptor = cLayer->GetParameters();</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  TensorInfo weightsInfo;</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <span class="keyword">const</span> TensorInfo* weightsInfoPtr = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span> </div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  weightsInfo = OverrideDataType(layer.GetInputSlot(1).GetConnection()->GetTensorInfo(), dataType);</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  weightsInfoPtr = &weightsInfo;</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>  TensorInfo biasInfo;</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="keyword">const</span> TensorInfo* biasInfoPtr = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <span class="keyword">static</span> <span class="keyword">const</span> TensorInfo dummyBFloat16Bias(TensorShape({1,1,1,1}), <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>);</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <span class="keyword">static</span> <span class="keyword">const</span> TensorInfo dummyFloat16Bias(TensorShape({1,1,1,1}), <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>);</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keyword">static</span> <span class="keyword">const</span> TensorInfo dummyFloat32Bias(TensorShape({1,1,1,1}), <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="keyword">static</span> <span class="keyword">const</span> TensorInfo dummyQA8Bias(TensorShape({1,1,1,1}), <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>);</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> </div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <span class="keywordflow">if</span> (descriptor.m_BiasEnabled)</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  {</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  biasInfo = OverrideDataType(layer.GetInputSlot(2).GetConnection()->GetTensorInfo(), dataType);</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  biasInfoPtr = &biasInfo;</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  }</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  {</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <span class="comment">// If biases are not enabled pass a dummy tensorinfo for the validation</span></div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <span class="keywordflow">switch</span>(input.GetDataType())</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  {</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>:</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  {</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  biasInfoPtr = &dummyBFloat16Bias;</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  }</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>:</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  {</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  biasInfoPtr = &dummyFloat16Bias;</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  }</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>:</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  {</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  biasInfoPtr = &dummyFloat32Bias;</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  }</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>:</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>:</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>:</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>:</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  {</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  biasInfoPtr = &dummyQA8Bias;</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  }</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <span class="keywordflow">default</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="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(<span class="keyword">false</span>, <span class="stringliteral">"Unexpected bias type"</span>);</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  }</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  }</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  }</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  result = layerSupportObject.IsFullyConnectedSupported(</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  OverrideDataType(input, dataType),</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  *weightsInfoPtr,</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  *biasInfoPtr,</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  descriptor,</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  reason);</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  <span class="keywordflow">break</span>;</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aad22c799930d644e8468fe44c0312d53">LayerType::Gather</a>:</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  {</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <span class="keyword">const</span> TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <span class="keyword">const</span> TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const GatherLayer*>(&layer);</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  <span class="keyword">const</span> GatherDescriptor& descriptor = cLayer->GetParameters();</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  result = layerSupportObject.IsGatherSupported(OverrideDataType(input0, dataType),</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  input1,</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  descriptor,</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  reason);</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  }</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a3028cc42e40f9a1f4f8b35556d9715a4">LayerType::GatherNd</a>:</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  {</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <span class="keyword">const</span> TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <span class="keyword">const</span> TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  result = layerSupportObject.IsGatherNdSupported(OverrideDataType(input0, dataType),</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  input1,</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  reason);</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  }</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">LayerType::Input</a>:</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  {</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  result = layerSupportObject.IsInputSupported(OverrideDataType(input, dataType), reason);</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  }</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a21baa4498161d195f5bb2e3627344ba4">LayerType::InstanceNormalization</a>:</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  {</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const InstanceNormalizationLayer*>(&layer);</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  <span class="keyword">const</span> InstanceNormalizationDescriptor& descriptor = cLayer->GetParameters();</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span> </div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span> </div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  result = layerSupportObject.IsInstanceNormalizationSupported(</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  OverrideDataType(input, dataType),</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  descriptor,</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  reason);</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  }</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a1189467870bc421ae59277e750263eb2">LayerType::L2Normalization</a>:</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  {</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const L2NormalizationLayer*>(&layer);</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  <span class="keyword">const</span> L2NormalizationDescriptor& descriptor = cLayer->GetParameters();</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span> </div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span> </div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  result = layerSupportObject.IsL2NormalizationSupported(</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  OverrideDataType(input, dataType),</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  descriptor,</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  reason);</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  }</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af4f53c8297dc1cb53d4e6f8151070a30">LayerType::LogicalBinary</a>:</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  {</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const LogicalBinaryLayer*>(&layer);</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span> </div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <span class="keyword">const</span> TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="keyword">const</span> TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span> </div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  result = layerSupportObject.IsLogicalBinarySupported(input0,</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  input1,</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  output,</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  cLayer->GetParameters(),</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  reason);</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  }</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ac21dbda57d88c21ec9857f5d1522c488">LayerType::LogSoftmax</a>:</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  {</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const LogSoftmaxLayer*>(&layer);</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span> </div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span> </div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  result = layerSupportObject.IsLogSoftmaxSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  cLayer->GetParameters(),</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  reason);</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  }</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a06b091bc9aea697ba473c71f0bb55925">LayerType::Lstm</a>:</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  {</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const LstmLayer*>(&layer);</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  <span class="keyword">const</span> LstmDescriptor& descriptor = cLayer->GetParameters();</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span> </div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  <span class="comment">// All inputs.</span></div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <span class="keyword">const</span> TensorInfo& input = OverrideDataType(layer.GetInputSlot(0).GetConnection()->GetTensorInfo(),</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  dataType);</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  <span class="keyword">const</span> TensorInfo& outputStateIn = OverrideDataType(layer.GetInputSlot(1).GetConnection()->GetTensorInfo(),</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  dataType);</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <span class="keyword">const</span> TensorInfo& cellStateIn = OverrideDataType(layer.GetInputSlot(2).GetConnection()->GetTensorInfo(),</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  dataType);</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <span class="comment">// All outputs</span></div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  <span class="keyword">const</span> TensorInfo& scratchBuffer = OverrideDataType(layer.GetOutputSlot(0).GetTensorInfo(), dataType);</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  <span class="keyword">const</span> TensorInfo& outputStateOut = OverrideDataType(layer.GetOutputSlot(1).GetTensorInfo(), dataType);</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  <span class="keyword">const</span> TensorInfo& cellStateOut = OverrideDataType(layer.GetOutputSlot(2).GetTensorInfo(), dataType);</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  <span class="keyword">const</span> TensorInfo& output = OverrideDataType(layer.GetOutputSlot(3).GetTensorInfo(), dataType);</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span> </div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  <span class="comment">// Basic parameters</span></div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  <span class="keyword">const</span> TensorInfo& inputToForgetWeights</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  = OverrideDataType(cLayer->m_BasicParameters.m_InputToForgetWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <span class="keyword">const</span> TensorInfo& inputToCellWeights</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  = OverrideDataType(cLayer->m_BasicParameters.m_InputToCellWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <span class="keyword">const</span> TensorInfo& inputToOutputWeights</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  = OverrideDataType(cLayer->m_BasicParameters.m_InputToOutputWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  <span class="keyword">const</span> TensorInfo& recurrentToForgetWeights</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  = OverrideDataType(cLayer->m_BasicParameters.m_RecurrentToForgetWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  <span class="keyword">const</span> TensorInfo& recurrentToCellWeights</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  = OverrideDataType(cLayer->m_BasicParameters.m_RecurrentToCellWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  <span class="keyword">const</span> TensorInfo& recurrentToOutputWeights</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  = OverrideDataType(cLayer->m_BasicParameters.m_RecurrentToOutputWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <span class="keyword">const</span> TensorInfo& forgetGateBias</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  = OverrideDataType(cLayer->m_BasicParameters.m_ForgetGateBias->GetTensorInfo(), dataType);</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  <span class="keyword">const</span> TensorInfo& cellBias</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  = OverrideDataType(cLayer->m_BasicParameters.m_CellBias->GetTensorInfo(), dataType);</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  <span class="keyword">const</span> TensorInfo& outputGateBias</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  = OverrideDataType(cLayer->m_BasicParameters.m_OutputGateBias->GetTensorInfo(), dataType);</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span> </div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  LstmInputParamsInfo paramsInfo;</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span> </div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  paramsInfo.m_InputToForgetWeights = &inputToForgetWeights;</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  paramsInfo.m_InputToCellWeights = &inputToCellWeights;</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  paramsInfo.m_InputToOutputWeights = &inputToOutputWeights;</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  paramsInfo.m_RecurrentToForgetWeights = &recurrentToForgetWeights;</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  paramsInfo.m_RecurrentToCellWeights = &recurrentToCellWeights;</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  paramsInfo.m_RecurrentToOutputWeights = &recurrentToOutputWeights;</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  paramsInfo.m_ForgetGateBias = &forgetGateBias;</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  paramsInfo.m_CellBias = &cellBias;</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  paramsInfo.m_OutputGateBias = &outputGateBias;</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span> </div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span> </div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  <span class="comment">// Optional parameters</span></div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  TensorInfo optInputToInputWeights;</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  TensorInfo optRecurrentToInputWeights;</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  TensorInfo optCellToInputWeights;</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  TensorInfo optInputGateBias;</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  TensorInfo optProjectionWeights;</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  TensorInfo optProjectionBias;</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  TensorInfo optCellToForgetWeights;</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  TensorInfo optCellToOutputWeights;</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  TensorInfo optInputLayerNormWeights;</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  TensorInfo optForgetLayerNormWeights;</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  TensorInfo optCellLayerNormWeights;</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  TensorInfo optOutputLayerNormWeights;</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span> </div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <span class="keywordflow">if</span>(!descriptor.m_CifgEnabled)</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  {</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  optInputToInputWeights =</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  OverrideDataType(cLayer->m_CifgParameters.m_InputToInputWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  paramsInfo.m_InputToInputWeights = &optInputToInputWeights;</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span> </div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  optRecurrentToInputWeights =</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  OverrideDataType(cLayer->m_CifgParameters.m_RecurrentToInputWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  paramsInfo.m_RecurrentToInputWeights = &optRecurrentToInputWeights;</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  optInputGateBias =</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  OverrideDataType(cLayer->m_CifgParameters.m_InputGateBias->GetTensorInfo(), dataType);</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  paramsInfo.m_InputGateBias = &optInputGateBias;</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  }</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span> </div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <span class="keywordflow">if</span>(descriptor.m_ProjectionEnabled)</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  {</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  optProjectionWeights =</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  OverrideDataType(cLayer->m_ProjectionParameters.m_ProjectionWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  paramsInfo.m_ProjectionWeights = &optProjectionWeights;</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  <span class="keywordflow">if</span> (cLayer->m_ProjectionParameters.m_ProjectionBias != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  {</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  optProjectionBias =</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  OverrideDataType(cLayer->m_ProjectionParameters.m_ProjectionBias->GetTensorInfo(), dataType);</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  paramsInfo.m_ProjectionBias = &optProjectionBias;</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  }</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  }</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span> </div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  <span class="keywordflow">if</span>(descriptor.m_PeepholeEnabled)</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  {</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  <span class="keywordflow">if</span>(!descriptor.m_CifgEnabled)</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  {</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  optCellToInputWeights =</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  OverrideDataType(cLayer->m_PeepholeParameters.m_CellToInputWeights->GetTensorInfo(),</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  dataType);</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  paramsInfo.m_CellToInputWeights = &optCellToInputWeights;</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  }</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  optCellToForgetWeights =</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  OverrideDataType(cLayer->m_PeepholeParameters.m_CellToForgetWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  paramsInfo.m_CellToForgetWeights = &optCellToForgetWeights;</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  optCellToOutputWeights =</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  OverrideDataType(cLayer->m_PeepholeParameters.m_CellToOutputWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  paramsInfo.m_CellToOutputWeights = &optCellToOutputWeights;</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  }</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span> </div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <span class="keywordflow">if</span>(descriptor.m_LayerNormEnabled)</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  {</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  <span class="keywordflow">if</span> (!descriptor.m_CifgEnabled)</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  {</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  optInputLayerNormWeights = OverrideDataType(</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  cLayer->m_LayerNormParameters.m_InputLayerNormWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  paramsInfo.m_InputLayerNormWeights = &optInputLayerNormWeights;</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  }</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span> </div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  optForgetLayerNormWeights = OverrideDataType(</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  cLayer->m_LayerNormParameters.m_ForgetLayerNormWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  paramsInfo.m_ForgetLayerNormWeights = &optForgetLayerNormWeights;</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span> </div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  optCellLayerNormWeights = OverrideDataType(</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  cLayer->m_LayerNormParameters.m_CellLayerNormWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  paramsInfo.m_CellLayerNormWeights = &optCellLayerNormWeights;</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span> </div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  optOutputLayerNormWeights = OverrideDataType(</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  cLayer->m_LayerNormParameters.m_OutputLayerNormWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  paramsInfo.m_OutputLayerNormWeights = &optOutputLayerNormWeights;</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  }</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span> </div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  result = layerSupportObject.IsLstmSupported(</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  input,</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  outputStateIn,</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  cellStateIn,</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  scratchBuffer,</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  outputStateOut,</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  cellStateOut,</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  output,</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  descriptor,</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  paramsInfo,</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  reason);</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  }</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a8321e79c278ec510f63675c040594892">LayerType::Maximum</a>:</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  {</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  <span class="keyword">const</span> TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  <span class="keyword">const</span> TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span> </div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  result = layerSupportObject.IsMaximumSupported(OverrideDataType(input0, dataType),</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  OverrideDataType(input1, dataType),</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  reason);</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  }</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">LayerType::MemCopy</a>:</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  {</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span> </div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  result = layerSupportObject.IsMemCopySupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  reason);</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  }</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a115bfc5d633eae55c67f9588acdd2bf9">LayerType::MemImport</a>:</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  {</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span> </div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  result = layerSupportObject.IsMemImportSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  reason);</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  }</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a68be4837f6c739877233e527a996dd00">LayerType::Merge</a>:</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  {</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  <span class="keyword">const</span> TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  <span class="keyword">const</span> TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span> </div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  result = layerSupportObject.IsMergeSupported(OverrideDataType(input0, dataType),</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  OverrideDataType(input1, dataType),</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  reason);</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  }</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae20f0f2826a6549809f050b86274567f">LayerType::Concat</a>:</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  {</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const ConcatLayer*>(&layer);</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span> </div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  <span class="comment">// Get vector of all inputs.</span></div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  <span class="keyword">auto</span> getTensorInfo = [&dataType](<span class="keyword">const</span> InputSlot& slot)</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  {</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  <span class="keywordflow">return</span> OverrideDataType(slot.GetConnectedOutputSlot()->GetTensorInfo(), dataType);</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  };</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span> </div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  <span class="keyword">auto</span> beginI = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(layer.GetInputSlots().begin(), getTensorInfo);</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  <span class="keyword">auto</span> endI = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(layer.GetInputSlots().end(), getTensorInfo);</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  std::vector<TensorInfo> inputs(beginI, endI);</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span> </div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <span class="keyword">auto</span> getTensorInfoPtr = [](<span class="keyword">const</span> TensorInfo& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  {</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  <span class="keywordflow">return</span> &<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  };</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span> </div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  <span class="keyword">auto</span> beginPtr = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(inputs.begin(), getTensorInfoPtr);</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  <span class="keyword">auto</span> endPtr = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(inputs.end(), getTensorInfoPtr);</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  std::vector<const TensorInfo*> inputPtrs(beginPtr, endPtr);</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span> </div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span> </div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  result = layerSupportObject.IsConcatSupported(inputPtrs, output, cLayer->GetParameters(), reason);</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span> </div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span> </div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  }</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a27d1a1f7b7c2180e5b20ce9e3d00e2dd">LayerType::Multiplication</a>:</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  {</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  <span class="keyword">const</span> TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  <span class="keyword">const</span> TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  result = layerSupportObject.IsMultiplicationSupported(</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  OverrideDataType(input0, dataType),</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  OverrideDataType(input1, dataType),</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  reason);</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  }</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aac61f2e17250a818dee4d12b112aa88f">LayerType::Normalization</a>:</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  {</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const NormalizationLayer*>(&layer);</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  result = layerSupportObject.IsNormalizationSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  cLayer->GetParameters(),</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  reason);</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  }</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">LayerType::Output</a>:</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  {</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  result = layerSupportObject.IsOutputSupported(OverrideDataType(output, dataType), reason);</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  }</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">LayerType::Permute</a>:</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  {</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const PermuteLayer*>(&layer);</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  result = layerSupportObject.IsPermuteSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  cLayer->GetParameters(),</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  reason);</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  }</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ade43468adaf6acb2c38ebc0c1176f82f">LayerType::Pad</a>:</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  {</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const PadLayer*>(&layer);</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  result = layerSupportObject.IsPadSupported(</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  OverrideDataType(input, dataType),</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  cLayer->GetParameters(),</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  reason);</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  }</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad662867a41bfb30b9f75dda2b5849001">LayerType::Pooling2d</a>:</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  {</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const Pooling2dLayer*>(&layer);</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  result = layerSupportObject.IsPooling2dSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  cLayer->GetParameters(),</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  reason);</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  }</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2b3140dc366b9fcd25ed786a79d1817c">LayerType::Pooling3d</a>:</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  {</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const Pooling3dLayer*>(&layer);</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  result = layerSupportObject.IsPooling3dSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  cLayer->GetParameters(),</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  reason);</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  }</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a1ba143ebe524d46181a4b53470693278">LayerType::PreCompiled</a>:</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  {</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const PreCompiledLayer*>(&layer);</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  result = layerSupportObject.IsPreCompiledSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  cLayer->GetParameters(),</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  reason);</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  }</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aca39930e22f40d10155a57dba32240bb">LayerType::Quantize</a>:</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  {</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  result = layerSupportObject.IsQuantizeSupported(input, output, reason);</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  }</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a91880b71ea6d007439b7bc7c320b5c25">LayerType::QLstm</a>:</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  {</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const QLstmLayer*>(&layer);</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  <span class="keyword">const</span> QLstmDescriptor& descriptor = cLayer->GetParameters();</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span> </div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  <span class="comment">// Inputs</span></div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  <span class="keyword">const</span> TensorInfo& previousOutputIn = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  <span class="keyword">const</span> TensorInfo& previousCellStateIn = layer.GetInputSlot(2).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span> </div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  <span class="comment">// Outputs</span></div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  <span class="keyword">const</span> TensorInfo& outputStateOut = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span>  <span class="keyword">const</span> TensorInfo& cellStateOut = layer.GetOutputSlot(1).GetTensorInfo();</div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(2).GetTensorInfo();</div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span> </div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  <span class="comment">// Lstm parameters</span></div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  LstmInputParamsInfo paramsInfo;</div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span> </div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  <span class="comment">// Basic parameters</span></div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(cLayer->m_BasicParameters.m_InputToForgetWeights.get() != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(cLayer->m_BasicParameters.m_InputToCellWeights.get() != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(cLayer->m_BasicParameters.m_InputToOutputWeights.get() != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  paramsInfo.m_InputToForgetWeights = &cLayer->m_BasicParameters.m_InputToForgetWeights->GetTensorInfo();</div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  paramsInfo.m_InputToCellWeights = &cLayer->m_BasicParameters.m_InputToCellWeights->GetTensorInfo();</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  paramsInfo.m_InputToOutputWeights = &cLayer->m_BasicParameters.m_InputToOutputWeights->GetTensorInfo();</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span> </div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  paramsInfo.m_RecurrentToForgetWeights =</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  &cLayer->m_BasicParameters.m_RecurrentToForgetWeights->GetTensorInfo();</div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  paramsInfo.m_RecurrentToCellWeights =</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  &cLayer->m_BasicParameters.m_RecurrentToCellWeights->GetTensorInfo();</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  paramsInfo.m_RecurrentToOutputWeights =</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  &cLayer->m_BasicParameters.m_RecurrentToOutputWeights->GetTensorInfo();</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span> </div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  paramsInfo.m_ForgetGateBias = &cLayer->m_BasicParameters.m_ForgetGateBias->GetTensorInfo();</div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  paramsInfo.m_CellBias = &cLayer->m_BasicParameters.m_CellBias->GetTensorInfo();</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  paramsInfo.m_OutputGateBias = &cLayer->m_BasicParameters.m_OutputGateBias->GetTensorInfo();</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span> </div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  <span class="keywordflow">if</span>(!descriptor.m_CifgEnabled)</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  {</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  paramsInfo.m_InputToInputWeights = &cLayer->m_CifgParameters.m_InputToInputWeights->GetTensorInfo();</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  paramsInfo.m_RecurrentToInputWeights =</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  &cLayer->m_CifgParameters.m_RecurrentToInputWeights->GetTensorInfo();</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  paramsInfo.m_InputGateBias = &cLayer->m_CifgParameters.m_InputGateBias->GetTensorInfo();</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  }</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span> </div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  <span class="keywordflow">if</span>(descriptor.m_ProjectionEnabled)</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  {</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  paramsInfo.m_ProjectionWeights = &cLayer->m_ProjectionParameters.m_ProjectionWeights->GetTensorInfo();</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span> </div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  <span class="comment">// Projection bias is optional even if projection is enabled</span></div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  <span class="keywordflow">if</span> (cLayer->m_ProjectionParameters.m_ProjectionBias != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  {</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  paramsInfo.m_ProjectionBias = &cLayer->m_ProjectionParameters.m_ProjectionBias->GetTensorInfo();</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  }</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  }</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span> </div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  <span class="keywordflow">if</span>(descriptor.m_PeepholeEnabled)</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  {</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  <span class="keywordflow">if</span> (!descriptor.m_CifgEnabled)</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  {</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  paramsInfo.m_CellToInputWeights =</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  &cLayer->m_PeepholeParameters.m_CellToInputWeights->GetTensorInfo();</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  }</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span> </div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  paramsInfo.m_CellToForgetWeights =</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  &cLayer->m_PeepholeParameters.m_CellToForgetWeights->GetTensorInfo();</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>  paramsInfo.m_CellToOutputWeights = &cLayer->m_PeepholeParameters.m_CellToOutputWeights->GetTensorInfo();</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  }</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span> </div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  <span class="keywordflow">if</span>(descriptor.m_LayerNormEnabled)</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  {</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  <span class="keywordflow">if</span> (!descriptor.m_CifgEnabled)</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  {</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  paramsInfo.m_InputLayerNormWeights =</div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  &cLayer->m_LayerNormParameters.m_InputLayerNormWeights->GetTensorInfo();</div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  }</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span> </div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  paramsInfo.m_ForgetLayerNormWeights =</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  &cLayer->m_LayerNormParameters.m_ForgetLayerNormWeights->GetTensorInfo();</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  paramsInfo.m_CellLayerNormWeights =</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  &cLayer->m_LayerNormParameters.m_CellLayerNormWeights->GetTensorInfo();</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  paramsInfo.m_OutputLayerNormWeights =</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  &cLayer->m_LayerNormParameters.m_OutputLayerNormWeights->GetTensorInfo();</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  }</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span> </div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span>  result = layerSupportObject.IsQLstmSupported(input,</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  previousOutputIn,</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  previousCellStateIn,</div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  outputStateOut,</div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  cellStateOut,</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  output,</div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span>  descriptor,</div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span>  paramsInfo,</div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span>  reason);</div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  }</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a890a37ff3bfe123414ba7e6f052b49f3">LayerType::QuantizedLstm</a>:</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  {</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const QuantizedLstmLayer*>(&layer);</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span> </div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>  <span class="comment">// Inputs</span></div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  <span class="keyword">const</span> TensorInfo& previousCellStateIn = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  <span class="keyword">const</span> TensorInfo& previousOutputIn = layer.GetInputSlot(2).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span> </div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  <span class="comment">// Outputs</span></div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  <span class="keyword">const</span> TensorInfo& cellStateOut = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(1).GetTensorInfo();</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span> </div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  <span class="comment">// QuantizedLstm parameters</span></div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  QuantizedLstmInputParamsInfo paramsInfo;</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span> </div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  paramsInfo.m_InputToInputWeights =</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  &cLayer->m_QuantizedLstmParameters.m_InputToInputWeights->GetTensorInfo();</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  paramsInfo.m_InputToForgetWeights =</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>  &cLayer->m_QuantizedLstmParameters.m_InputToForgetWeights->GetTensorInfo();</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>  paramsInfo.m_InputToCellWeights =</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  &cLayer->m_QuantizedLstmParameters.m_InputToCellWeights->GetTensorInfo();</div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>  paramsInfo.m_InputToOutputWeights =</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  &cLayer->m_QuantizedLstmParameters.m_InputToOutputWeights->GetTensorInfo();</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span> </div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  paramsInfo.m_RecurrentToInputWeights =</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>  &cLayer->m_QuantizedLstmParameters.m_RecurrentToInputWeights->GetTensorInfo();</div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  paramsInfo.m_RecurrentToForgetWeights =</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  &cLayer->m_QuantizedLstmParameters.m_RecurrentToForgetWeights->GetTensorInfo();</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  paramsInfo.m_RecurrentToCellWeights =</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  &cLayer->m_QuantizedLstmParameters.m_RecurrentToCellWeights->GetTensorInfo();</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  paramsInfo.m_RecurrentToOutputWeights =</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  &cLayer->m_QuantizedLstmParameters.m_RecurrentToOutputWeights->GetTensorInfo();</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span> </div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>  paramsInfo.m_InputGateBias =</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>  &cLayer->m_QuantizedLstmParameters.m_InputGateBias->GetTensorInfo();</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  paramsInfo.m_ForgetGateBias =</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  &cLayer->m_QuantizedLstmParameters.m_ForgetGateBias->GetTensorInfo();</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>  paramsInfo.m_CellBias =</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>  &cLayer->m_QuantizedLstmParameters.m_CellBias->GetTensorInfo();</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>  paramsInfo.m_OutputGateBias =</div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>  &cLayer->m_QuantizedLstmParameters.m_OutputGateBias->GetTensorInfo();;</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span> </div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  result = layerSupportObject.IsQuantizedLstmSupported(input,</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  previousCellStateIn,</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  previousOutputIn,</div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>  cellStateOut,</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>  output,</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  paramsInfo,</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>  reason);</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>  }</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a3025cdaab2deb0bb2cd642449e570833">LayerType::Division</a>:</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>  {</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>  <span class="keyword">const</span> TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  <span class="keyword">const</span> TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>  result = layerSupportObject.IsDivisionSupported(</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  OverrideDataType(input0, dataType),</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  OverrideDataType(input1, dataType),</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  reason);</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>  }</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a021da1b20f73dc252361a54d80497ef3">LayerType::Rank</a>:</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  {</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>  result = layerSupportObject.IsRankSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>  reason);</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  }</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa7c59ccedc6a3bd90c17f3b990afefad">LayerType::Reshape</a>:</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>  {</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const ReshapeLayer*>(&layer);</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>  result = layerSupportObject.IsReshapeSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>  cLayer->GetParameters(),</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>  reason);</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>  }</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a9d723d04c40bfd81835c0766a698cf63">LayerType::Resize</a>:</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>  {</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const ResizeLayer*>(&layer);</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  result = layerSupportObject.IsResizeSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>  cLayer->GetParameters(),</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  reason);</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>  }</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a880c1273b27d27cfc82004c3a4b205c9">LayerType::Shape</a>:</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  {</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span> </div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  result = layerSupportObject.IsShapeSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>  reason);</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>  }</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad140d37ad98c12ccd8e1c432f548bcdb">LayerType::Slice</a>:</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>  {</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const SliceLayer*>(&layer);</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span> </div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span> </div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>  result = layerSupportObject.IsSliceSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>  cLayer->GetParameters(),</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>  reason);</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  }</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a31d953b9d49a6b4378f45097047976d0">LayerType::Softmax</a>:</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>  {</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const SoftmaxLayer*>(&layer);</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>  result = layerSupportObject.IsSoftmaxSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  cLayer->GetParameters(),</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>  reason);</div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  }</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a337c392144dca0d18290c6b4711a2279">LayerType::SpaceToBatchNd</a>:</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>  {</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const SpaceToBatchNdLayer*>(&layer);</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  result = layerSupportObject.IsSpaceToBatchNdSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  cLayer->GetParameters(),</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  reason);</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>  }</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a5e7ff12da912dc79e7e547281823fa4a">LayerType::SpaceToDepth</a>:</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  {</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const SpaceToDepthLayer*>(&layer);</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span> </div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span> </div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  result = layerSupportObject.IsSpaceToDepthSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  cLayer->GetParameters(),</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  reason);</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>  }</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a41cb9b797ebc6f6f6314e3ded935f4cf">LayerType::Splitter</a>:</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  {</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const SplitterLayer*>(&layer);</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span> </div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  <span class="comment">// Get vector of all outputs.</span></div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  <span class="keyword">auto</span> getTensorInfo = [&dataType](<span class="keyword">const</span> OutputSlot& slot)</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  {</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>  <span class="keywordflow">return</span> OverrideDataType(slot.GetTensorInfo(), dataType);</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  };</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>  <span class="keyword">auto</span> beginI = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(layer.GetOutputSlots().begin(), getTensorInfo);</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>  <span class="keyword">auto</span> endI = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(layer.GetOutputSlots().end(), getTensorInfo);</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>  std::vector<TensorInfo> outputs(beginI, endI);</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span> </div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  <span class="keyword">const</span> std::vector<std::reference_wrapper<TensorInfo>> outputPtrs(outputs.begin(), outputs.end());</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span> </div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  result = layerSupportObject.IsSplitterSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  outputPtrs,</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  cLayer->GetParameters(),</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  reason);</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  }</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2187e1021a911b3807cc1bea2eb1a9ca">LayerType::Stack</a>:</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  {</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const StackLayer*>(&layer);</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span> </div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  <span class="comment">// Get vector of all inputs.</span></div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  <span class="keyword">auto</span> getTensorInfo = [&dataType](<span class="keyword">const</span> InputSlot& slot)</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  {</div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  <span class="keywordflow">return</span> OverrideDataType(slot.GetConnectedOutputSlot()->GetTensorInfo(), dataType);</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  };</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  <span class="keyword">auto</span> beginI = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(layer.GetInputSlots().begin(), getTensorInfo);</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  <span class="keyword">auto</span> endI = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(layer.GetInputSlots().end(), getTensorInfo);</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  std::vector<TensorInfo> inputs(beginI, endI);</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span> </div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  <span class="keyword">auto</span> getTensorInfoPtr = [](<span class="keyword">const</span> TensorInfo& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  {</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>  <span class="keywordflow">return</span> &<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  };</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  <span class="keyword">auto</span> beginPtr = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(inputs.begin(), getTensorInfoPtr);</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  <span class="keyword">auto</span> endPtr = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(inputs.end(), getTensorInfoPtr);</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  std::vector<const TensorInfo*> inputPtrs(beginPtr, endPtr);</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span> </div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span> </div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  result = layerSupportObject.IsStackSupported(inputPtrs, output, cLayer->GetParameters(), reason);</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span> </div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>  }</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a93bca63ecbb003649425dd0e4ba79a99">LayerType::StandIn</a>:</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  {</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const StandInLayer*>(&layer);</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span> </div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  <span class="comment">// Get vector of all inputs.</span></div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>  <span class="keyword">auto</span> getTensorInfoIn = [&dataType](<span class="keyword">const</span> InputSlot& slot)</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>  {</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>  <span class="keywordflow">return</span> OverrideDataType(slot.GetConnectedOutputSlot()->GetTensorInfo(), dataType);</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>  };</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>  <span class="keyword">auto</span> getTensorInfoOut = [&dataType](<span class="keyword">const</span> OutputSlot& slot)</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>  {</div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  <span class="keywordflow">return</span> OverrideDataType(slot.GetTensorInfo(), dataType);</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>  };</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>  <span class="keyword">auto</span> beginI = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(layer.GetInputSlots().begin(), getTensorInfoIn);</div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>  <span class="keyword">auto</span> endI = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(layer.GetInputSlots().end(), getTensorInfoIn);</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  std::vector<TensorInfo> inputs(beginI, endI);</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span> </div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>  <span class="keyword">auto</span> beginO = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(layer.GetOutputSlots().begin(), getTensorInfoOut);</div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>  <span class="keyword">auto</span> endO = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(layer.GetOutputSlots().end(), getTensorInfoOut);</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>  std::vector<TensorInfo> outputs(beginO, endO);</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span> </div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span> </div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  <span class="keyword">auto</span> getTensorInfoPtr = [](<span class="keyword">const</span> TensorInfo& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  {</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  <span class="keywordflow">return</span> &<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  };</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>  <span class="keyword">auto</span> beginPtrI = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(inputs.begin(), getTensorInfoPtr);</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>  <span class="keyword">auto</span> endPtrI = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(inputs.end(), getTensorInfoPtr);</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>  std::vector<const TensorInfo*> inputPtrs(beginPtrI, endPtrI);</div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span> </div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>  <span class="keyword">auto</span> beginPtrO = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(outputs.begin(), getTensorInfoPtr);</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>  <span class="keyword">auto</span> endPtrO = <a class="code" href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">MakeTransformIterator</a>(outputs.end(), getTensorInfoPtr);</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  std::vector<const TensorInfo*> outputPtrs(beginPtrO, endPtrO);</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span> </div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span> </div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>  result = layerSupportObject.IsStandInSupported(inputPtrs,</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>  outputPtrs,</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>  cLayer->GetParameters(),</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>  reason);</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>  }</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa31904f2b3479b5a00137fd985974b4d">LayerType::StridedSlice</a>:</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  {</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const StridedSliceLayer*>(&layer);</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>  result = layerSupportObject.IsStridedSliceSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>  cLayer->GetParameters(),</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  reason);</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>  }</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6eb8b8b560161603402c0238b3a7d8b0">LayerType::Subtraction</a>:</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  {</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>  <span class="keyword">const</span> TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  <span class="keyword">const</span> TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>  result = layerSupportObject.IsSubtractionSupported(</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>  OverrideDataType(input0, dataType),</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>  OverrideDataType(input1, dataType),</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>  reason);</div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>  }</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4abbc155fb2b111bf61c4f5ff892915e6b">LayerType::Switch</a>:</div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>  {</div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  <span class="keyword">const</span> TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>  <span class="keyword">const</span> TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>  <span class="keyword">const</span> TensorInfo& output0 = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>  <span class="keyword">const</span> TensorInfo& output1 = layer.GetOutputSlot(1).GetTensorInfo();</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>  result = layerSupportObject.IsSwitchSupported(OverrideDataType(input0, dataType),</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>  OverrideDataType(input1, dataType),</div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  OverrideDataType(output0, dataType),</div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>  OverrideDataType(output1, dataType),</div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>  reason);</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>  }</div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0a3d6c9ac08ada31c184094bbc67afe00d">LayerType::Mean</a>:</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>  {</div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const MeanLayer*>(&layer);</div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>  result = layerSupportObject.IsMeanSupported(</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>  OverrideDataType(input, dataType),</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>  cLayer->GetParameters(),</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>  reason);</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>  }</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa1d0ec6d56f8833a078b5a7ac4caf2d4">LayerType::Minimum</a>:</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>  {</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>  <span class="keyword">const</span> TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>  <span class="keyword">const</span> TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  result = layerSupportObject.IsMinimumSupported(OverrideDataType(input0, dataType),</div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>  OverrideDataType(input1, dataType),</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>  reason);</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>  }</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a0c5967f09e0669c840ebb1ed0da85e32">LayerType::Prelu</a>:</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>  {</div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>  <span class="keyword">const</span> TensorInfo& alpha = layer.GetInputSlot(1).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>  result = layerSupportObject.IsPreluSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>  OverrideDataType(alpha, dataType),</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>  reason);</div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>  }</div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aaf70b1ac863830a4e1ce6268c8399f54">LayerType::Transpose</a>:</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>  {</div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const TransposeLayer*>(&layer);</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>  result = layerSupportObject.IsTransposeSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>  cLayer->GetParameters(),</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>  reason);</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  }</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a71b23d26c0f5d20416d6c77754f9806a">LayerType::TransposeConvolution2d</a>:</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>  {</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const TransposeConvolution2dLayer*>(&layer);</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span> </div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  <span class="keyword">const</span> TensorInfo input = OverrideDataType(layer.GetInputSlot(0).GetConnection()->GetTensorInfo(),</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>  dataType);</div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>  <span class="keyword">const</span> TensorInfo output = OverrideDataType(layer.GetOutputSlot(0).GetTensorInfo(), dataType);</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span> </div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>  <span class="keyword">const</span> TransposeConvolution2dDescriptor& descriptor = cLayer->GetParameters();</div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span> </div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>  Optional<TensorInfo> biases;</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>  <span class="keywordflow">if</span> (descriptor.m_BiasEnabled)</div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>  {</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(cLayer->m_Bias.get() != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>  biases = OverrideDataType(cLayer->m_Bias->GetTensorInfo(),</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>  <a class="code" href="namespacearmnn.xhtml#ada0fb4f79f3673b4ebd94a42175bf78d">GetBiasTypeFromWeightsType</a>(dataType));</div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>  }</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span> </div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>  <a class="code" href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(cLayer->m_Weight.get() != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span>  <span class="keyword">const</span> TensorInfo weights = OverrideDataType(cLayer->m_Weight->GetTensorInfo(), dataType);</div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span> </div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>  result = layerSupportObject.IsTransposeConvolution2dSupported(input,</div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>  output,</div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span>  descriptor,</div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>  weights,</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>  biases,</div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>  reason);</div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span> </div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>  }</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aec4875f03ff0bb0b26cf76ac7f41e3c8">LayerType::Reduce</a>:</div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>  {</div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const ReduceLayer*>(&layer);</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>  <span class="keyword">const</span> TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo();</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>  <span class="keyword">const</span> TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo();</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span> </div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>  result = layerSupportObject.IsReduceSupported(OverrideDataType(input, dataType),</div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>  OverrideDataType(output, dataType),</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>  cLayer->GetParameters(),</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>  reason);</div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>  }</div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a300124b2433e0376ec4b19251ac3a9e5">LayerType::UnidirectionalSequenceLstm</a>:</div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>  {</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>  <span class="keyword">auto</span> cLayer = PolymorphicDowncast<const UnidirectionalSequenceLstmLayer*>(&layer);</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ae6c5f1b51bd32133c4dcc632045d6b58">UnidirectionalSequenceLstmDescriptor</a>& descriptor = cLayer->GetParameters();</div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span> </div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>  <span class="comment">// All inputs.</span></div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span>  <span class="keyword">const</span> TensorInfo& input = OverrideDataType(layer.GetInputSlot(0).GetConnection()->GetTensorInfo(),</div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span>  dataType);</div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span>  <span class="keyword">const</span> TensorInfo& outputStateIn = OverrideDataType(layer.GetInputSlot(1).GetConnection()->GetTensorInfo(),</div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span>  dataType);</div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>  <span class="keyword">const</span> TensorInfo& cellStateIn = OverrideDataType(layer.GetInputSlot(2).GetConnection()->GetTensorInfo(),</div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>  dataType);</div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span>  <span class="comment">// Outputs</span></div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>  <span class="keyword">const</span> TensorInfo& outputStateOut = OverrideDataType(layer.GetOutputSlot(0).GetTensorInfo(), dataType);</div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>  <span class="keyword">const</span> TensorInfo& cellStateOut = OverrideDataType(layer.GetOutputSlot(1).GetTensorInfo(), dataType);</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>  <span class="keyword">const</span> TensorInfo& output = OverrideDataType(layer.GetOutputSlot(2).GetTensorInfo(), dataType);</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span> </div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>  <span class="comment">// Basic parameters</span></div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>  <span class="keyword">const</span> TensorInfo& inputToForgetWeights</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span>  = OverrideDataType(cLayer->m_BasicParameters.m_InputToForgetWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>  <span class="keyword">const</span> TensorInfo& inputToCellWeights</div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span>  = OverrideDataType(cLayer->m_BasicParameters.m_InputToCellWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>  <span class="keyword">const</span> TensorInfo& inputToOutputWeights</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>  = OverrideDataType(cLayer->m_BasicParameters.m_InputToOutputWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span>  <span class="keyword">const</span> TensorInfo& recurrentToForgetWeights</div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>  = OverrideDataType(cLayer->m_BasicParameters.m_RecurrentToForgetWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>  <span class="keyword">const</span> TensorInfo& recurrentToCellWeights</div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>  = OverrideDataType(cLayer->m_BasicParameters.m_RecurrentToCellWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>  <span class="keyword">const</span> TensorInfo& recurrentToOutputWeights</div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>  = OverrideDataType(cLayer->m_BasicParameters.m_RecurrentToOutputWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>  <span class="keyword">const</span> TensorInfo& forgetGateBias</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>  = OverrideDataType(cLayer->m_BasicParameters.m_ForgetGateBias->GetTensorInfo(), dataType);</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>  <span class="keyword">const</span> TensorInfo& cellBias</div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>  = OverrideDataType(cLayer->m_BasicParameters.m_CellBias->GetTensorInfo(), dataType);</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span>  <span class="keyword">const</span> TensorInfo& outputGateBias</div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>  = OverrideDataType(cLayer->m_BasicParameters.m_OutputGateBias->GetTensorInfo(), dataType);</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span> </div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>  LstmInputParamsInfo paramsInfo;</div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span> </div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>  paramsInfo.m_InputToForgetWeights = &inputToForgetWeights;</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>  paramsInfo.m_InputToCellWeights = &inputToCellWeights;</div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>  paramsInfo.m_InputToOutputWeights = &inputToOutputWeights;</div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>  paramsInfo.m_RecurrentToForgetWeights = &recurrentToForgetWeights;</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  paramsInfo.m_RecurrentToCellWeights = &recurrentToCellWeights;</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span>  paramsInfo.m_RecurrentToOutputWeights = &recurrentToOutputWeights;</div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span>  paramsInfo.m_ForgetGateBias = &forgetGateBias;</div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>  paramsInfo.m_CellBias = &cellBias;</div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>  paramsInfo.m_OutputGateBias = &outputGateBias;</div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span> </div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span>  <span class="comment">// Optional parameters</span></div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>  TensorInfo optInputToInputWeights;</div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span>  TensorInfo optRecurrentToInputWeights;</div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>  TensorInfo optCellToInputWeights;</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>  TensorInfo optInputGateBias;</div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>  TensorInfo optProjectionWeights;</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>  TensorInfo optProjectionBias;</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>  TensorInfo optCellToForgetWeights;</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>  TensorInfo optCellToOutputWeights;</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>  TensorInfo optInputLayerNormWeights;</div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>  TensorInfo optForgetLayerNormWeights;</div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>  TensorInfo optCellLayerNormWeights;</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>  TensorInfo optOutputLayerNormWeights;</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span> </div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>  <span class="keywordflow">if</span>(!descriptor.m_CifgEnabled)</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>  {</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>  optInputToInputWeights =</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>  OverrideDataType(cLayer->m_CifgParameters.m_InputToInputWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span>  paramsInfo.m_InputToInputWeights = &optInputToInputWeights;</div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span> </div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>  optRecurrentToInputWeights =</div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span>  OverrideDataType(cLayer->m_CifgParameters.m_RecurrentToInputWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>  paramsInfo.m_RecurrentToInputWeights = &optRecurrentToInputWeights;</div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>  optInputGateBias =</div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>  OverrideDataType(cLayer->m_CifgParameters.m_InputGateBias->GetTensorInfo(), dataType);</div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span>  paramsInfo.m_InputGateBias = &optInputGateBias;</div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>  }</div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span> </div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>  <span class="keywordflow">if</span>(descriptor.m_ProjectionEnabled)</div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>  {</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>  optProjectionWeights =</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>  OverrideDataType(cLayer->m_ProjectionParameters.m_ProjectionWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>  paramsInfo.m_ProjectionWeights = &optProjectionWeights;</div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span>  <span class="keywordflow">if</span> (cLayer->m_ProjectionParameters.m_ProjectionBias != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span>  {</div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span>  optProjectionBias =</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>  OverrideDataType(cLayer->m_ProjectionParameters.m_ProjectionBias->GetTensorInfo(), dataType);</div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>  paramsInfo.m_ProjectionBias = &optProjectionBias;</div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>  }</div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span>  }</div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span> </div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span>  <span class="keywordflow">if</span>(descriptor.m_PeepholeEnabled)</div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span>  {</div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>  <span class="keywordflow">if</span>(!descriptor.m_CifgEnabled)</div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>  {</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>  optCellToInputWeights =</div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span>  OverrideDataType(cLayer->m_PeepholeParameters.m_CellToInputWeights->GetTensorInfo(),</div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span>  dataType);</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span>  paramsInfo.m_CellToInputWeights = &optCellToInputWeights;</div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>  }</div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span>  optCellToForgetWeights =</div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span>  OverrideDataType(cLayer->m_PeepholeParameters.m_CellToForgetWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span>  paramsInfo.m_CellToForgetWeights = &optCellToForgetWeights;</div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span>  optCellToOutputWeights =</div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span>  OverrideDataType(cLayer->m_PeepholeParameters.m_CellToOutputWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span>  paramsInfo.m_CellToOutputWeights = &optCellToOutputWeights;</div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>  }</div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span> </div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span>  <span class="keywordflow">if</span>(descriptor.m_LayerNormEnabled)</div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span>  {</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span>  <span class="keywordflow">if</span> (!descriptor.m_CifgEnabled)</div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>  {</div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>  optInputLayerNormWeights = OverrideDataType(</div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>  cLayer->m_LayerNormParameters.m_InputLayerNormWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>  paramsInfo.m_InputLayerNormWeights = &optInputLayerNormWeights;</div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>  }</div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span> </div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</span>  optForgetLayerNormWeights = OverrideDataType(</div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span>  cLayer->m_LayerNormParameters.m_ForgetLayerNormWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>  paramsInfo.m_ForgetLayerNormWeights = &optForgetLayerNormWeights;</div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span> </div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span>  optCellLayerNormWeights = OverrideDataType(</div><div class="line"><a name="l01486"></a><span class="lineno"> 1486</span>  cLayer->m_LayerNormParameters.m_CellLayerNormWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span>  paramsInfo.m_CellLayerNormWeights = &optCellLayerNormWeights;</div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span> </div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span>  optOutputLayerNormWeights = OverrideDataType(</div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>  cLayer->m_LayerNormParameters.m_OutputLayerNormWeights->GetTensorInfo(), dataType);</div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>  paramsInfo.m_OutputLayerNormWeights = &optOutputLayerNormWeights;</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>  }</div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span> </div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span>  result = layerSupportObject.IsUnidirectionalSequenceLstmSupported(input,</div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span>  outputStateIn,</div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>  cellStateIn,</div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>  outputStateOut,</div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>  cellStateOut,</div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span>  output,</div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span>  descriptor,</div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>  paramsInfo,</div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span>  reason);</div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01504"></a><span class="lineno"> 1504</span>  }</div><div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01506"></a><span class="lineno"> 1506</span>  {</div><div class="line"><a name="l01507"></a><span class="lineno"> 1507</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(<span class="keyword">false</span>, <span class="stringliteral">"WorkloadFactory did not recognise type of layer."</span>);</div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span>  reason.value() = <span class="stringliteral">"Unrecognised layer type"</span>;</div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>  result = <span class="keyword">false</span>;</div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>  }</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>  }</div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span> }</div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span> </div><div class="line"><a name="l01516"></a><span class="lineno"><a class="line" href="classarmnn_1_1_i_workload_factory.xhtml#a74dc9ec1a223eab8b072368b2dacee87"> 1516</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a74dc9ec1a223eab8b072368b2dacee87">IWorkloadFactory::IsLayerSupported</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_backend_id.xhtml">BackendId</a>& backendId,</div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>& connectableLayer,</div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<DataType></a> dataType,</div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>  std::string& outReasonIfUnsupported)</div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span> {</div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span>  <span class="keywordflow">return</span> IsLayerConfigurationSupported(backendId, connectableLayer, dataType, outReasonIfUnsupported);</div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span> }</div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span> </div><div class="line"><a name="l01524"></a><span class="lineno"><a class="line" href="classarmnn_1_1_i_workload_factory.xhtml#a7d94ea841143b76fe08ccb308839bfd7"> 1524</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a74dc9ec1a223eab8b072368b2dacee87">IWorkloadFactory::IsLayerSupported</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>& connectableLayer,</div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<DataType></a> dataType,</div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span>  std::string& outReasonIfUnsupported)</div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span> {</div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>  <span class="keyword">auto</span> layer = PolymorphicDowncast<const Layer*>(&connectableLayer);</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>  <span class="keywordflow">return</span> IsLayerConfigurationSupported(layer->GetBackendId(), connectableLayer, dataType, outReasonIfUnsupported);</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span> }</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span> </div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span> <span class="comment">// TODO merge with defaulted modelOptions above</span></div><div class="line"><a name="l01533"></a><span class="lineno"><a class="line" href="classarmnn_1_1_i_workload_factory.xhtml#aeaff50773427132e1066a7de56a53db1"> 1533</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a74dc9ec1a223eab8b072368b2dacee87">IWorkloadFactory::IsLayerSupported</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>& connectableLayer,</div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<DataType></a> dataType,</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span>  std::string& outReasonIfUnsupported,</div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a5b6893cda5b69359a4244c06054da18f">ModelOptions</a>& modelOptions)</div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span> {</div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>  <span class="keyword">auto</span> layer = PolymorphicDowncast<const Layer*>(&connectableLayer);</div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>  <span class="keywordflow">return</span> IsLayerConfigurationSupported(layer->GetBackendId(),</div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>  connectableLayer,</div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span>  dataType,</div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span>  outReasonIfUnsupported,</div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>  modelOptions);</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span> }</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span> </div><div class="line"><a name="l01546"></a><span class="lineno"><a class="line" href="classarmnn_1_1_i_workload_factory.xhtml#a52ba8d60c6582a847ef7bc914116d394"> 1546</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a74dc9ec1a223eab8b072368b2dacee87">IWorkloadFactory::IsLayerSupported</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_backend_id.xhtml">BackendId</a>& backendId,</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>& connectableLayer,</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<DataType></a> dataType,</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span>  std::string& outReasonIfUnsupported,</div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a5b6893cda5b69359a4244c06054da18f">ModelOptions</a>& modelOptions)</div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span> {</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>  <span class="keywordflow">return</span> IsLayerConfigurationSupported(backendId,</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>  connectableLayer,</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span>  dataType,</div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>  outReasonIfUnsupported,</div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>  modelOptions);</div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span> }</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span> <a class="code" href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l01559"></a><span class="lineno"><a class="line" href="classarmnn_1_1_i_workload_factory.xhtml#a13060ebb89d2c21a7a5d897e99cccf72"> 1559</a></span> std::unique_ptr<IWorkload> <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a13060ebb89d2c21a7a5d897e99cccf72">IWorkloadFactory::CreateWorkload</a>(<a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4">LayerType</a> type,</div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml">QueueDescriptor</a>& descriptor,</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& info)<span class="keyword"> const</span></div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span> <span class="keyword"></span>{</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>  <span class="keywordflow">switch</span>(type)</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>  {</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">LayerType::Activation</a> :</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>  {</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>  <span class="keyword">auto</span> activationQueueDescriptor = PolymorphicDowncast<const ActivationQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span>  <span class="keywordflow">return</span> CreateActivation(*activationQueueDescriptor, info);</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>  }</div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f">LayerType::Addition</a> :</div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span>  {</div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span>  <span class="keyword">auto</span> additionQueueDescriptor = PolymorphicDowncast<const AdditionQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>  <span class="keywordflow">return</span> CreateAddition(*additionQueueDescriptor, info);</div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span>  }</div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2139684546b147c106b329f41547640c">LayerType::ArgMinMax</a> :</div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span>  {</div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>  <span class="keyword">auto</span> argMinMaxQueueDescriptor = PolymorphicDowncast<const ArgMinMaxQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span>  <span class="keywordflow">return</span> CreateArgMinMax(*argMinMaxQueueDescriptor, info);</div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span>  }</div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae4743c3ec15d1d84169b17264634692e">LayerType::BatchNormalization</a> :</div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span>  {</div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</span>  <span class="keyword">auto</span> batchNormQueueDescriptor = PolymorphicDowncast<const BatchNormalizationQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span>  <span class="keywordflow">return</span> CreateBatchNormalization(*batchNormQueueDescriptor, info);</div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span>  }</div><div class="line"><a name="l01585"></a><span class="lineno"> 1585</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6ee06c6045d0c5b6565a247955ef0fc2">LayerType::BatchToSpaceNd</a> :</div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span>  {</div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>  <span class="keyword">auto</span> batchToSpaceNdQueueDescriptor</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>  = PolymorphicDowncast<const BatchToSpaceNdQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01589"></a><span class="lineno"> 1589</span>  <span class="keywordflow">return</span> CreateBatchToSpaceNd(*batchToSpaceNdQueueDescriptor, info);</div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>  }</div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4cd9f3996d60790cd11c04f842ebc43c">LayerType::Cast</a> :</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>  {</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>  <span class="keyword">auto</span> castQueueDescriptor = PolymorphicDowncast<const CastQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>  <span class="keywordflow">return</span> CreateCast(*castQueueDescriptor, info);</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span>  }</div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a0ca5f33c1d35fd4105d3a26a2823f9dd">LayerType::ChannelShuffle</a> :</div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>  {</div><div class="line"><a name="l01598"></a><span class="lineno"> 1598</span>  <span class="keyword">auto</span> channelShuffleQueueDescriptor</div><div class="line"><a name="l01599"></a><span class="lineno"> 1599</span>  = PolymorphicDowncast<const ChannelShuffleQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>  <span class="keywordflow">return</span> CreateChannelShuffle(*channelShuffleQueueDescriptor, info);</div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span>  }</div><div class="line"><a name="l01602"></a><span class="lineno"> 1602</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af6c0e3a1c3cfabd32ae8d3ae741fcf0a">LayerType::Comparison</a> :</div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</span>  {</div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span>  <span class="keyword">auto</span> comparisonQueueDescriptor = PolymorphicDowncast<const ComparisonQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span>  <span class="keywordflow">return</span> CreateComparison(*comparisonQueueDescriptor, info);</div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span>  }</div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae20f0f2826a6549809f050b86274567f">LayerType::Concat</a> :</div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span>  {</div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span>  <span class="keyword">auto</span> concatQueueDescriptor = PolymorphicDowncast<const ConcatQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>  <span class="keywordflow">return</span> CreateConcat(*concatQueueDescriptor, info);</div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span>  }</div><div class="line"><a name="l01612"></a><span class="lineno"> 1612</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a14d24d90ab4ba2956e92e27890ba4c91acb17869fe51048b5a5c4c6106551a255">LayerType::Constant</a> :</div><div class="line"><a name="l01613"></a><span class="lineno"> 1613</span>  {</div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>  <span class="keyword">auto</span> constantQueueDescriptor = PolymorphicDowncast<const ConstantQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>  <span class="keywordflow">return</span> CreateConstant(*constantQueueDescriptor, info);</div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span>  }</div><div class="line"><a name="l01617"></a><span class="lineno"> 1617</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2ba4c46787312a2467153f40c542851a">LayerType::ConvertBf16ToFp32</a> :</div><div class="line"><a name="l01618"></a><span class="lineno"> 1618</span>  {</div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span>  <span class="keyword">auto</span> convertBf16ToFp32QueueDescriptor</div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span>  = PolymorphicDowncast<const ConvertBf16ToFp32QueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01621"></a><span class="lineno"> 1621</span>  <span class="keywordflow">return</span> CreateConvertBf16ToFp32(*convertBf16ToFp32QueueDescriptor, info);</div><div class="line"><a name="l01622"></a><span class="lineno"> 1622</span>  }</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a912a4b4d73726c282e3f79aa2c390d6c">LayerType::ConvertFp16ToFp32</a>:</div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span>  {</div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>  <span class="keyword">auto</span> convertFp16ToFp32QueueDescriptor</div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>  = PolymorphicDowncast<const ConvertFp16ToFp32QueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>  <span class="keywordflow">return</span> CreateConvertFp16ToFp32(*convertFp16ToFp32QueueDescriptor, info);</div><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span>  }</div><div class="line"><a name="l01629"></a><span class="lineno"> 1629</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6512859957de2cf2a5fe4dccb07bd9da">LayerType::ConvertFp32ToBf16</a>:</div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span>  {</div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>  <span class="keyword">auto</span> convertFp32ToBf16QueueDescriptor</div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span>  = PolymorphicDowncast<const ConvertFp32ToBf16QueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</span>  <span class="keywordflow">return</span> CreateConvertFp32ToBf16(*convertFp32ToBf16QueueDescriptor, info);</div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span>  }</div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4addf4f83b056acd5549949fc0350e9aad">LayerType::ConvertFp32ToFp16</a>:</div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span>  {</div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span>  <span class="keyword">auto</span> convertFp32ToFp16QueueDescriptor</div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span>  = PolymorphicDowncast<const ConvertFp32ToFp16QueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span>  <span class="keywordflow">return</span> CreateConvertFp32ToFp16(*convertFp32ToFp16QueueDescriptor, info);</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span>  }</div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a">LayerType::Convolution2d</a>:</div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span>  {</div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span>  <span class="keyword">auto</span> convolution2dQueueDescriptor = PolymorphicDowncast<const Convolution2dQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span>  <span class="keywordflow">return</span> CreateConvolution2d(*convolution2dQueueDescriptor, info);</div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span>  }</div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a583550d0f265fd3756f7de0e42c51953">LayerType::Convolution3d</a>:</div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>  {</div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>  <span class="keyword">auto</span> convolution3dQueueDescriptor = PolymorphicDowncast<const Convolution3dQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>  <span class="keywordflow">return</span> CreateConvolution3d(*convolution3dQueueDescriptor, info);</div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>  }</div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa603905470e2a5b8c13e96b579ef0dba">LayerType::Debug</a>:</div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>  {</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span>  <span class="keyword">auto</span> debugQueueDescriptor = PolymorphicDowncast<const DebugQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>  <span class="keywordflow">return</span> CreateDebug(*debugQueueDescriptor, info);</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>  }</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a731729ad1b2c0eb9399b62c770b3482d">LayerType::DepthToSpace</a>:</div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>  {</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>  <span class="keyword">auto</span> depthToSpaceQueueDescriptor = PolymorphicDowncast<const DepthToSpaceQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>  <span class="keywordflow">return</span> CreateDepthToSpace(*depthToSpaceQueueDescriptor, info);</div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span>  }</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af97adbfc88b7012a0243215b1076e7e7">LayerType::DepthwiseConvolution2d</a>:</div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span>  {</div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</span>  <span class="keyword">auto</span> depthwiseConvolution2DQueueDescriptor</div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span>  = PolymorphicDowncast<const DepthwiseConvolution2dQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>  <span class="keywordflow">return</span> CreateDepthwiseConvolution2d(*depthwiseConvolution2DQueueDescriptor, info);</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span>  }</div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a9bc35e069257a508e14ed82965a8661d">LayerType::Dequantize</a>:</div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span>  {</div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span>  <span class="keyword">auto</span> dequantizeQueueDescriptor = PolymorphicDowncast<const DequantizeQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span>  <span class="keywordflow">return</span> CreateDequantize(*dequantizeQueueDescriptor, info);</div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span>  }</div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a1db19222ac424bd7162142ddf929fd2a">LayerType::DetectionPostProcess</a>:</div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span>  {</div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span>  <span class="keyword">auto</span> detectionPostProcessQueueDescriptor</div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>  = PolymorphicDowncast<const DetectionPostProcessQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span>  <span class="keywordflow">return</span> CreateDetectionPostProcess(*detectionPostProcessQueueDescriptor, info);</div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>  }</div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a3025cdaab2deb0bb2cd642449e570833">LayerType::Division</a>:</div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span>  {</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span>  <span class="keyword">auto</span> divisionQueueDescriptor = PolymorphicDowncast<const DivisionQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span>  <span class="keywordflow">return</span> CreateDivision(*divisionQueueDescriptor, info);</div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span>  }</div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4abcd30d7ea97ad20c2cddc0f47e6b70c7">LayerType::ElementwiseUnary</a>:</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>  {</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>  <span class="keyword">auto</span> elementwiseUnaryQueueDescriptor</div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>  = PolymorphicDowncast<const ElementwiseUnaryQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span>  <span class="keywordflow">return</span> CreateElementwiseUnary(*elementwiseUnaryQueueDescriptor, info);</div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</span> </div><div class="line"><a name="l01689"></a><span class="lineno"> 1689</span>  }</div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a3489c7b05e180496cb2ce8ac73887f48">LayerType::FakeQuantization</a>:</div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span>  {</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>  <span class="keyword">auto</span> fakeQuantizationQueueDescriptor</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span>  = PolymorphicDowncast<const FakeQuantizationQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span>  <span class="keywordflow">return</span> CreateFakeQuantization(*fakeQuantizationQueueDescriptor, info);</div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span>  }</div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4adb3e3f51c9107e26c9bccf9a188ce2ed">LayerType::Fill</a>:</div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span>  {</div><div class="line"><a name="l01698"></a><span class="lineno"> 1698</span>  <span class="keyword">auto</span> fillQueueDescriptor = PolymorphicDowncast<const FillQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>  <span class="keywordflow">return</span> CreateFill(*fillQueueDescriptor, info);</div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>  }</div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">LayerType::Floor</a>:</div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span>  {</div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span>  <span class="keyword">auto</span> floorQueueDescriptor = PolymorphicDowncast<const FloorQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span>  <span class="keywordflow">return</span> CreateFloor(*floorQueueDescriptor, info);</div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span>  }</div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acab78faff25393e9defd1911cb58133e">LayerType::FullyConnected</a>:</div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span>  {</div><div class="line"><a name="l01708"></a><span class="lineno"> 1708</span>  <span class="keyword">auto</span> fullyConnectedQueueDescriptor</div><div class="line"><a name="l01709"></a><span class="lineno"> 1709</span>  = PolymorphicDowncast<const FullyConnectedQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01710"></a><span class="lineno"> 1710</span>  <span class="keywordflow">return</span> CreateFullyConnected(*fullyConnectedQueueDescriptor, info);</div><div class="line"><a name="l01711"></a><span class="lineno"> 1711</span>  }</div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aad22c799930d644e8468fe44c0312d53">LayerType::Gather</a>:</div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>  {</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span>  <span class="keyword">auto</span> gatherQueueDescriptor = PolymorphicDowncast<const GatherQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span>  <span class="keywordflow">return</span> CreateGather(*gatherQueueDescriptor, info);</div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>  }</div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">LayerType::Input</a>:</div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span>  {</div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>  <span class="keyword">auto</span> inputQueueDescriptor = PolymorphicDowncast<const InputQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#aa83593035de93eb4b6dddb9dc8f5ced6">CreateInput</a>(*inputQueueDescriptor, info);</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>  }</div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a21baa4498161d195f5bb2e3627344ba4">LayerType::InstanceNormalization</a>:</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>  {</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>  <span class="keyword">auto</span> instanceNormalizationQueueDescriptor</div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>  = PolymorphicDowncast<const InstanceNormalizationQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>  <span class="keywordflow">return</span> CreateInstanceNormalization(*instanceNormalizationQueueDescriptor, info);</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>  }</div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a1189467870bc421ae59277e750263eb2">LayerType::L2Normalization</a>:</div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>  {</div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>  <span class="keyword">auto</span> l2NormalizationQueueDescriptor</div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span>  = PolymorphicDowncast<const L2NormalizationQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span>  <span class="keywordflow">return</span> CreateL2Normalization(*l2NormalizationQueueDescriptor, info);</div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>  }</div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af4f53c8297dc1cb53d4e6f8151070a30">LayerType::LogicalBinary</a>:</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span>  {</div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span>  <span class="keyword">auto</span> logicalBinaryQueueDescriptor = PolymorphicDowncast<const LogicalBinaryQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span>  <span class="keywordflow">return</span> CreateLogicalBinary(*logicalBinaryQueueDescriptor, info);</div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span>  }</div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ac21dbda57d88c21ec9857f5d1522c488">LayerType::LogSoftmax</a>:</div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span>  {</div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>  <span class="keyword">auto</span> logSoftmaxQueueDescriptor = PolymorphicDowncast<const LogSoftmaxQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>  <span class="keywordflow">return</span> CreateLogSoftmax(*logSoftmaxQueueDescriptor, info);</div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>  }</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a06b091bc9aea697ba473c71f0bb55925">LayerType::Lstm</a>:</div><div class="line"><a name="l01745"></a><span class="lineno"> 1745</span>  {</div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span>  <span class="keyword">auto</span> lstmQueueDescriptor = PolymorphicDowncast<const LstmQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>  <span class="keywordflow">return</span> CreateLstm(*lstmQueueDescriptor, info);</div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span>  }</div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a8321e79c278ec510f63675c040594892">LayerType::Maximum</a>:</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>  {</div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>  <span class="keyword">auto</span> maximumQueueDescriptor = PolymorphicDowncast<const MaximumQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span>  <span class="keywordflow">return</span> CreateMaximum(*maximumQueueDescriptor, info);</div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span>  }</div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0a3d6c9ac08ada31c184094bbc67afe00d">LayerType::Mean</a>:</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>  {</div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span>  <span class="keyword">auto</span> meanQueueDescriptor = PolymorphicDowncast<const MeanQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span>  <span class="keywordflow">return</span> CreateMean(*meanQueueDescriptor, info);</div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span>  }</div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">LayerType::MemCopy</a>:</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span>  {</div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>  <span class="keyword">auto</span> memCopyQueueDescriptor = PolymorphicDowncast<const MemCopyQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>  <span class="keywordflow">return</span> CreateMemCopy(*memCopyQueueDescriptor, info);</div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>  }</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a115bfc5d633eae55c67f9588acdd2bf9">LayerType::MemImport</a>:</div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>  {</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span>  <span class="keyword">auto</span> memImportQueueDescriptor = PolymorphicDowncast<const MemImportQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span>  <span class="keywordflow">return</span> CreateMemImport(*memImportQueueDescriptor, info);</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>  }</div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa1d0ec6d56f8833a078b5a7ac4caf2d4">LayerType::Minimum</a>:</div><div class="line"><a name="l01770"></a><span class="lineno"> 1770</span>  {</div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span>  <span class="keyword">auto</span> minimumQueueDescriptor = PolymorphicDowncast<const MinimumQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span>  <span class="keywordflow">return</span> CreateMinimum(*minimumQueueDescriptor, info);</div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span>  }</div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a27d1a1f7b7c2180e5b20ce9e3d00e2dd">LayerType::Multiplication</a>:</div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>  {</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>  <span class="keyword">auto</span> multiplicationQueueDescriptor</div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span>  = PolymorphicDowncast<const MultiplicationQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>  <span class="keywordflow">return</span> CreateMultiplication(*multiplicationQueueDescriptor, info);</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>  }</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aac61f2e17250a818dee4d12b112aa88f">LayerType::Normalization</a>:</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>  {</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>  <span class="keyword">auto</span> normalizationQueueDescriptor = PolymorphicDowncast<const NormalizationQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>  <span class="keywordflow">return</span> CreateNormalization(*normalizationQueueDescriptor, info);</div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>  }</div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">LayerType::Output</a>:</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span>  {</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span>  <span class="keyword">auto</span> outputQueueDescriptor = PolymorphicDowncast<const OutputQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>  <span class="keywordflow">return</span> CreateOutput(*outputQueueDescriptor, info);</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span>  }</div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ade43468adaf6acb2c38ebc0c1176f82f">LayerType::Pad</a>:</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span>  {</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>  <span class="keyword">auto</span> padQueueDescriptor = PolymorphicDowncast<const PadQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>  <span class="keywordflow">return</span> CreatePad(*padQueueDescriptor, info);</div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>  }</div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">LayerType::Permute</a>:</div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span>  {</div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span>  <span class="keyword">auto</span> permuteQueueDescriptor = PolymorphicDowncast<const PermuteQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span>  <span class="keywordflow">return</span> CreatePermute(*permuteQueueDescriptor, info);</div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span>  }</div><div class="line"><a name="l01800"></a><span class="lineno"> 1800</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad662867a41bfb30b9f75dda2b5849001">LayerType::Pooling2d</a>:</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>  {</div><div class="line"><a name="l01802"></a><span class="lineno"> 1802</span>  <span class="keyword">auto</span> pooling2dQueueDescriptor = PolymorphicDowncast<const Pooling2dQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01803"></a><span class="lineno"> 1803</span>  <span class="keywordflow">return</span> CreatePooling2d(*pooling2dQueueDescriptor, info);</div><div class="line"><a name="l01804"></a><span class="lineno"> 1804</span>  }</div><div class="line"><a name="l01805"></a><span class="lineno"> 1805</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2b3140dc366b9fcd25ed786a79d1817c">LayerType::Pooling3d</a>:</div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span>  {</div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>  <span class="keyword">auto</span> pooling3dQueueDescriptor = PolymorphicDowncast<const Pooling3dQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>  <span class="keywordflow">return</span> CreatePooling3d(*pooling3dQueueDescriptor, info);</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>  }</div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a1ba143ebe524d46181a4b53470693278">LayerType::PreCompiled</a>:</div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>  {</div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span>  <span class="keyword">auto</span> preCompiledQueueDescriptor = PolymorphicDowncast<const PreCompiledQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01813"></a><span class="lineno"> 1813</span>  <span class="keywordflow">return</span> CreatePreCompiled(*preCompiledQueueDescriptor, info);</div><div class="line"><a name="l01814"></a><span class="lineno"> 1814</span>  }</div><div class="line"><a name="l01815"></a><span class="lineno"> 1815</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a0c5967f09e0669c840ebb1ed0da85e32">LayerType::Prelu</a>:</div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span>  {</div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span>  <span class="keyword">auto</span> preluQueueDescriptor = PolymorphicDowncast<const PreluQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01818"></a><span class="lineno"> 1818</span>  <span class="keywordflow">return</span> CreatePrelu(*preluQueueDescriptor, info);</div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</span>  }</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a91880b71ea6d007439b7bc7c320b5c25">LayerType::QLstm</a>:</div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>  {</div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span>  <span class="keyword">auto</span> qlstmQueueDescriptor = PolymorphicDowncast<const QLstmQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span>  <span class="keywordflow">return</span> CreateQLstm(*qlstmQueueDescriptor, info);</div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span>  }</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aca39930e22f40d10155a57dba32240bb">LayerType::Quantize</a>:</div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span>  {</div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span>  <span class="keyword">auto</span> quantizeQueueDescriptor = PolymorphicDowncast<const QuantizeQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01828"></a><span class="lineno"> 1828</span>  <span class="keywordflow">return</span> CreateQuantize(*quantizeQueueDescriptor, info);</div><div class="line"><a name="l01829"></a><span class="lineno"> 1829</span>  }</div><div class="line"><a name="l01830"></a><span class="lineno"> 1830</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a021da1b20f73dc252361a54d80497ef3">LayerType::Rank</a>:</div><div class="line"><a name="l01831"></a><span class="lineno"> 1831</span>  {</div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span>  <span class="keyword">auto</span> rankQueueDescriptor = PolymorphicDowncast<const RankQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span>  <span class="keywordflow">return</span> CreateRank(*rankQueueDescriptor, info);</div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>  }</div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aec4875f03ff0bb0b26cf76ac7f41e3c8">LayerType::Reduce</a>:</div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>  {</div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span>  <span class="keyword">auto</span> reduceQueueDescriptor = PolymorphicDowncast<const ReduceQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01838"></a><span class="lineno"> 1838</span>  <span class="keywordflow">return</span> CreateReduce(*reduceQueueDescriptor, info);</div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span>  }</div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa7c59ccedc6a3bd90c17f3b990afefad">LayerType::Reshape</a>:</div><div class="line"><a name="l01841"></a><span class="lineno"> 1841</span>  {</div><div class="line"><a name="l01842"></a><span class="lineno"> 1842</span>  <span class="keyword">auto</span> reshapeQueueDescriptor = PolymorphicDowncast<const ReshapeQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01843"></a><span class="lineno"> 1843</span>  <span class="keywordflow">return</span> CreateReshape(*reshapeQueueDescriptor, info);</div><div class="line"><a name="l01844"></a><span class="lineno"> 1844</span>  }</div><div class="line"><a name="l01845"></a><span class="lineno"> 1845</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a9d723d04c40bfd81835c0766a698cf63">LayerType::Resize</a>:</div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span>  {</div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>  <span class="keyword">auto</span> resizeQueueDescriptor = PolymorphicDowncast<const ResizeQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01848"></a><span class="lineno"> 1848</span>  <span class="keywordflow">return</span> CreateResize(*resizeQueueDescriptor, info);</div><div class="line"><a name="l01849"></a><span class="lineno"> 1849</span>  }</div><div class="line"><a name="l01850"></a><span class="lineno"> 1850</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a880c1273b27d27cfc82004c3a4b205c9">LayerType::Shape</a>:</div><div class="line"><a name="l01851"></a><span class="lineno"> 1851</span>  {</div><div class="line"><a name="l01852"></a><span class="lineno"> 1852</span>  <span class="keyword">auto</span> shapeQueueDescriptor = PolymorphicDowncast<const ShapeQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span>  <span class="keywordflow">return</span> CreateShape(*shapeQueueDescriptor, info);</div><div class="line"><a name="l01854"></a><span class="lineno"> 1854</span>  }</div><div class="line"><a name="l01855"></a><span class="lineno"> 1855</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad140d37ad98c12ccd8e1c432f548bcdb">LayerType::Slice</a>:</div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>  {</div><div class="line"><a name="l01857"></a><span class="lineno"> 1857</span>  <span class="keyword">auto</span> sliceQueueDescriptor = PolymorphicDowncast<const SliceQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</span>  <span class="keywordflow">return</span> CreateSlice(*sliceQueueDescriptor, info);</div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</span>  }</div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a31d953b9d49a6b4378f45097047976d0">LayerType::Softmax</a>:</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span>  {</div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span>  <span class="keyword">auto</span> softmaxQueueDescriptor = PolymorphicDowncast<const SoftmaxQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span>  <span class="keywordflow">return</span> CreateSoftmax(*softmaxQueueDescriptor, info);</div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span>  }</div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a337c392144dca0d18290c6b4711a2279">LayerType::SpaceToBatchNd</a>:</div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span>  {</div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>  <span class="keyword">auto</span> spaceToBatchNdQueueDescriptor</div><div class="line"><a name="l01868"></a><span class="lineno"> 1868</span>  = PolymorphicDowncast<const SpaceToBatchNdQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01869"></a><span class="lineno"> 1869</span>  <span class="keywordflow">return</span> CreateSpaceToBatchNd(*spaceToBatchNdQueueDescriptor, info);</div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>  }</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a5e7ff12da912dc79e7e547281823fa4a">LayerType::SpaceToDepth</a>:</div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span>  {</div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span>  <span class="keyword">auto</span> spaceToDepthQueueDescriptor = PolymorphicDowncast<const SpaceToDepthQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>  <span class="keywordflow">return</span> CreateSpaceToDepth(*spaceToDepthQueueDescriptor, info);</div><div class="line"><a name="l01875"></a><span class="lineno"> 1875</span>  }</div><div class="line"><a name="l01876"></a><span class="lineno"> 1876</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a41cb9b797ebc6f6f6314e3ded935f4cf">LayerType::Splitter</a>:</div><div class="line"><a name="l01877"></a><span class="lineno"> 1877</span>  {</div><div class="line"><a name="l01878"></a><span class="lineno"> 1878</span>  <span class="keyword">auto</span> splitterQueueDescriptor = PolymorphicDowncast<const SplitterQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</span>  <span class="keywordflow">return</span> CreateSplitter(*splitterQueueDescriptor, info);</div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span>  }</div><div class="line"><a name="l01881"></a><span class="lineno"> 1881</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2187e1021a911b3807cc1bea2eb1a9ca">LayerType::Stack</a>:</div><div class="line"><a name="l01882"></a><span class="lineno"> 1882</span>  {</div><div class="line"><a name="l01883"></a><span class="lineno"> 1883</span>  <span class="keyword">auto</span> stackQueueDescriptor = PolymorphicDowncast<const StackQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01884"></a><span class="lineno"> 1884</span>  <span class="keywordflow">return</span> CreateStack(*stackQueueDescriptor, info);</div><div class="line"><a name="l01885"></a><span class="lineno"> 1885</span>  }</div><div class="line"><a name="l01886"></a><span class="lineno"> 1886</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa31904f2b3479b5a00137fd985974b4d">LayerType::StridedSlice</a>:</div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span>  {</div><div class="line"><a name="l01888"></a><span class="lineno"> 1888</span>  <span class="keyword">auto</span> stridedSliceQueueDescriptor = PolymorphicDowncast<const StridedSliceQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>  <span class="keywordflow">return</span> CreateStridedSlice(*stridedSliceQueueDescriptor, info);</div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span>  }</div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6eb8b8b560161603402c0238b3a7d8b0">LayerType::Subtraction</a>:</div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>  {</div><div class="line"><a name="l01893"></a><span class="lineno"> 1893</span>  <span class="keyword">auto</span> subtractionQueueDescriptor = PolymorphicDowncast<const SubtractionQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>  <span class="keywordflow">return</span> CreateSubtraction(*subtractionQueueDescriptor, info);</div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>  }</div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aaf70b1ac863830a4e1ce6268c8399f54">LayerType::Transpose</a>:</div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span>  {</div><div class="line"><a name="l01898"></a><span class="lineno"> 1898</span>  <span class="keyword">auto</span> transposeQueueDescriptor = PolymorphicDowncast<const TransposeQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span>  <span class="keywordflow">return</span> CreateTranspose(*transposeQueueDescriptor, info);</div><div class="line"><a name="l01900"></a><span class="lineno"> 1900</span>  }</div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a71b23d26c0f5d20416d6c77754f9806a">LayerType::TransposeConvolution2d</a>:</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span>  {</div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span>  <span class="keyword">auto</span> transposeConvolution2dQueueDescriptor</div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>  = PolymorphicDowncast<const TransposeConvolution2dQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>  <span class="keywordflow">return</span> CreateTransposeConvolution2d(*transposeConvolution2dQueueDescriptor, info);</div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>  }</div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a300124b2433e0376ec4b19251ac3a9e5">LayerType::UnidirectionalSequenceLstm</a>:</div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>  {</div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>  <span class="keyword">auto</span> unidirectionalSequenceLstmQueueDescriptor</div><div class="line"><a name="l01910"></a><span class="lineno"> 1910</span>  = PolymorphicDowncast<const UnidirectionalSequenceLstmQueueDescriptor*>(&descriptor);</div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>  <span class="keywordflow">return</span> CreateUnidirectionalSequenceLstm(*unidirectionalSequenceLstmQueueDescriptor, info);</div><div class="line"><a name="l01912"></a><span class="lineno"> 1912</span>  }</div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>  }</div><div class="line"><a name="l01916"></a><span class="lineno"> 1916</span> }</div><div class="line"><a name="l01917"></a><span class="lineno"> 1917</span> <a class="code" href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</span> </div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateActivation(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_activation_queue_descriptor.xhtml">ActivationQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span> <span class="keyword"></span>{</div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l01923"></a><span class="lineno"> 1923</span> }</div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span> </div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateAddition(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_addition_queue_descriptor.xhtml">AdditionQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span> <span class="keyword"></span>{</div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span> }</div><div class="line"><a name="l01930"></a><span class="lineno"> 1930</span> </div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateArgMinMax(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_arg_min_max_queue_descriptor.xhtml">ArgMinMaxQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l01933"></a><span class="lineno"> 1933</span> <span class="keyword"></span>{</div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span> }</div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span> </div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateBatchNormalization(</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml">BatchNormalizationQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>, <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span> <span class="keyword"></span>{</div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span> }</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span> </div><div class="line"><a name="l01943"></a><span class="lineno"> 1943</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateBatchToSpaceNd(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_batch_to_space_nd_queue_descriptor.xhtml">BatchToSpaceNdQueueDescriptor</a>& <span class="comment">/*desc*/</span>,</div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*Info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span> <span class="keyword"></span>{</div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span> }</div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span> </div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateCast(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_cast_queue_descriptor.xhtml">CastQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l01950"></a><span class="lineno"> 1950</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l01951"></a><span class="lineno"> 1951</span> <span class="keyword"></span>{</div><div class="line"><a name="l01952"></a><span class="lineno"> 1952</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span> }</div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span> </div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateChannelShuffle(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_channel_shuffle_queue_descriptor.xhtml">ChannelShuffleQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l01957"></a><span class="lineno"> 1957</span> <span class="keyword"></span>{</div><div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span> }</div><div class="line"><a name="l01960"></a><span class="lineno"> 1960</span> </div><div class="line"><a name="l01961"></a><span class="lineno"> 1961</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateComparison(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_comparison_queue_descriptor.xhtml">ComparisonQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span> <span class="keyword"></span>{</div><div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l01965"></a><span class="lineno"> 1965</span> }</div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span> </div><div class="line"><a name="l01967"></a><span class="lineno"> 1967</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateConcat(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml">ConcatQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l01968"></a><span class="lineno"> 1968</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span> <span class="keyword"></span>{</div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span> }</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span> </div><div class="line"><a name="l01973"></a><span class="lineno"> 1973</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateConstant(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_constant_queue_descriptor.xhtml">ConstantQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l01975"></a><span class="lineno"> 1975</span> <span class="keyword"></span>{</div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span> }</div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span> </div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateConvertBf16ToFp32(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_convert_bf16_to_fp32_queue_descriptor.xhtml">ConvertBf16ToFp32QueueDescriptor</a>& <span class="comment">/*desc*/</span>,</div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span> <span class="keyword"></span>{</div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span> }</div><div class="line"><a name="l01984"></a><span class="lineno"> 1984</span> </div><div class="line"><a name="l01985"></a><span class="lineno"> 1985</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateConvertFp16ToFp32(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_convert_fp16_to_fp32_queue_descriptor.xhtml">ConvertFp16ToFp32QueueDescriptor</a>& <span class="comment">/*desc*/</span>,</div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l01987"></a><span class="lineno"> 1987</span> <span class="keyword"></span>{</div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span> }</div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span> </div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateConvertFp32ToBf16(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_convert_fp32_to_bf16_queue_descriptor.xhtml">ConvertFp32ToBf16QueueDescriptor</a>& <span class="comment">/*desc*/</span>,</div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l01993"></a><span class="lineno"> 1993</span> <span class="keyword"></span>{</div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span> }</div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span> </div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateConvertFp32ToFp16(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_convert_fp32_to_fp16_queue_descriptor.xhtml">ConvertFp32ToFp16QueueDescriptor</a>& <span class="comment">/*desc*/</span>,</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span> <span class="keyword"></span>{</div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02001"></a><span class="lineno"> 2001</span> }</div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span> </div><div class="line"><a name="l02003"></a><span class="lineno"> 2003</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateConvolution2d(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">Convolution2dQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span> <span class="keyword"></span>{</div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02007"></a><span class="lineno"> 2007</span> }</div><div class="line"><a name="l02008"></a><span class="lineno"> 2008</span> </div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateConvolution3d(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_convolution3d_queue_descriptor.xhtml">Convolution3dQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span> <span class="keyword"></span>{</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span> }</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span> </div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateDebug(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_debug_queue_descriptor.xhtml">DebugQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span> <span class="keyword"></span>{</div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span> }</div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span> </div><div class="line"><a name="l02021"></a><span class="lineno"> 2021</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateDepthToSpace(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_depth_to_space_queue_descriptor.xhtml">DepthToSpaceQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02023"></a><span class="lineno"> 2023</span> <span class="keyword"></span>{</div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span> }</div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span> </div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateDepthwiseConvolution2d(</div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">DepthwiseConvolution2dQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>, <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span> <span class="keyword"></span>{</div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span> }</div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span> </div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateDequantize(</div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_dequantize_queue_descriptor.xhtml">DequantizeQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>, <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span> <span class="keyword"></span>{</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span> }</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span> </div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateDetectionPostProcess(</div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_detection_post_process_queue_descriptor.xhtml">DetectionPostProcessQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>, <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span> <span class="keyword"></span>{</div><div class="line"><a name="l02042"></a><span class="lineno"> 2042</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02043"></a><span class="lineno"> 2043</span> }</div><div class="line"><a name="l02044"></a><span class="lineno"> 2044</span> </div><div class="line"><a name="l02045"></a><span class="lineno"> 2045</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateDivision(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_division_queue_descriptor.xhtml">DivisionQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span> <span class="keyword"></span>{</div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span> }</div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span> </div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateElementwiseUnary(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_elementwise_unary_queue_descriptor.xhtml">ElementwiseUnaryQueueDescriptor</a>& <span class="comment">/*desc*/</span>,</div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span> <span class="keyword"></span>{</div><div class="line"><a name="l02054"></a><span class="lineno"> 2054</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span> }</div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span> </div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateFakeQuantization(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_fake_quantization_queue_descriptor.xhtml">FakeQuantizationQueueDescriptor</a>& <span class="comment">/*desc*/</span>,</div><div class="line"><a name="l02058"></a><span class="lineno"> 2058</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02059"></a><span class="lineno"> 2059</span> <span class="keyword"></span>{</div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02061"></a><span class="lineno"> 2061</span> }</div><div class="line"><a name="l02062"></a><span class="lineno"> 2062</span> </div><div class="line"><a name="l02063"></a><span class="lineno"> 2063</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateFill(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_fill_queue_descriptor.xhtml">FillQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02064"></a><span class="lineno"> 2064</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02065"></a><span class="lineno"> 2065</span> <span class="keyword"></span>{</div><div class="line"><a name="l02066"></a><span class="lineno"> 2066</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02067"></a><span class="lineno"> 2067</span> }</div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span> </div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateFloor(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_floor_queue_descriptor.xhtml">FloorQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span> <span class="keyword"></span>{</div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span> }</div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span> </div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateFullyConnected(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml">FullyConnectedQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02076"></a><span class="lineno"> 2076</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02077"></a><span class="lineno"> 2077</span> <span class="keyword"></span>{</div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span> }</div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span> </div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateGather(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_gather_queue_descriptor.xhtml">GatherQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span> <span class="keyword"></span>{</div><div class="line"><a name="l02084"></a><span class="lineno"> 2084</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02085"></a><span class="lineno"> 2085</span> }</div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span> </div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateInstanceNormalization(</div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_instance_normalization_queue_descriptor.xhtml">InstanceNormalizationQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02089"></a><span class="lineno"> 2089</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02090"></a><span class="lineno"> 2090</span> <span class="keyword"></span>{</div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span> }</div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span> </div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateL2Normalization(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_l2_normalization_queue_descriptor.xhtml">L2NormalizationQueueDescriptor</a>& <span class="comment">/*desc*/</span>,</div><div class="line"><a name="l02095"></a><span class="lineno"> 2095</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</span> <span class="keyword"></span>{</div><div class="line"><a name="l02097"></a><span class="lineno"> 2097</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02098"></a><span class="lineno"> 2098</span> }</div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span> </div><div class="line"><a name="l02100"></a><span class="lineno"> 2100</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateLogicalBinary(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_logical_binary_queue_descriptor.xhtml">LogicalBinaryQueueDescriptor</a>& <span class="comment">/*desc*/</span>,</div><div class="line"><a name="l02101"></a><span class="lineno"> 2101</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02102"></a><span class="lineno"> 2102</span> <span class="keyword"></span>{</div><div class="line"><a name="l02103"></a><span class="lineno"> 2103</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span> }</div><div class="line"><a name="l02105"></a><span class="lineno"> 2105</span> </div><div class="line"><a name="l02106"></a><span class="lineno"> 2106</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateLogicalUnary(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_elementwise_unary_queue_descriptor.xhtml">ElementwiseUnaryQueueDescriptor</a>& <span class="comment">/*desc*/</span>,</div><div class="line"><a name="l02107"></a><span class="lineno"> 2107</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02108"></a><span class="lineno"> 2108</span> <span class="keyword"></span>{</div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02110"></a><span class="lineno"> 2110</span> }</div><div class="line"><a name="l02111"></a><span class="lineno"> 2111</span> </div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateLogSoftmax(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_log_softmax_queue_descriptor.xhtml">LogSoftmaxQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02113"></a><span class="lineno"> 2113</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02114"></a><span class="lineno"> 2114</span> <span class="keyword"></span>{</div><div class="line"><a name="l02115"></a><span class="lineno"> 2115</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02116"></a><span class="lineno"> 2116</span> }</div><div class="line"><a name="l02117"></a><span class="lineno"> 2117</span> </div><div class="line"><a name="l02118"></a><span class="lineno"> 2118</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateLstm(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml">LstmQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02120"></a><span class="lineno"> 2120</span> <span class="keyword"></span>{</div><div class="line"><a name="l02121"></a><span class="lineno"> 2121</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02122"></a><span class="lineno"> 2122</span> }</div><div class="line"><a name="l02123"></a><span class="lineno"> 2123</span> </div><div class="line"><a name="l02124"></a><span class="lineno"> 2124</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateMaximum(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_maximum_queue_descriptor.xhtml">MaximumQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02125"></a><span class="lineno"> 2125</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02126"></a><span class="lineno"> 2126</span> <span class="keyword"></span>{</div><div class="line"><a name="l02127"></a><span class="lineno"> 2127</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02128"></a><span class="lineno"> 2128</span> }</div><div class="line"><a name="l02129"></a><span class="lineno"> 2129</span> </div><div class="line"><a name="l02130"></a><span class="lineno"> 2130</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateMean(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_mean_queue_descriptor.xhtml">MeanQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02131"></a><span class="lineno"> 2131</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*Info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02132"></a><span class="lineno"> 2132</span> <span class="keyword"></span>{</div><div class="line"><a name="l02133"></a><span class="lineno"> 2133</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02134"></a><span class="lineno"> 2134</span> }</div><div class="line"><a name="l02135"></a><span class="lineno"> 2135</span> </div><div class="line"><a name="l02136"></a><span class="lineno"> 2136</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateMemCopy(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_mem_copy_queue_descriptor.xhtml">MemCopyQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02137"></a><span class="lineno"> 2137</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02138"></a><span class="lineno"> 2138</span> <span class="keyword"></span>{</div><div class="line"><a name="l02139"></a><span class="lineno"> 2139</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02140"></a><span class="lineno"> 2140</span> }</div><div class="line"><a name="l02141"></a><span class="lineno"> 2141</span> </div><div class="line"><a name="l02142"></a><span class="lineno"> 2142</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateMemImport(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_mem_import_queue_descriptor.xhtml">MemImportQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02143"></a><span class="lineno"> 2143</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02144"></a><span class="lineno"> 2144</span> <span class="keyword"></span>{</div><div class="line"><a name="l02145"></a><span class="lineno"> 2145</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span> }</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span> </div><div class="line"><a name="l02148"></a><span class="lineno"> 2148</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateMerge(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_merge_queue_descriptor.xhtml">MergeQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02149"></a><span class="lineno"> 2149</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02150"></a><span class="lineno"> 2150</span> <span class="keyword"></span>{</div><div class="line"><a name="l02151"></a><span class="lineno"> 2151</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02152"></a><span class="lineno"> 2152</span> }</div><div class="line"><a name="l02153"></a><span class="lineno"> 2153</span> </div><div class="line"><a name="l02154"></a><span class="lineno"> 2154</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateMinimum(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_minimum_queue_descriptor.xhtml">MinimumQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02155"></a><span class="lineno"> 2155</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span> <span class="keyword"></span>{</div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span> }</div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span> </div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateMultiplication(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_multiplication_queue_descriptor.xhtml">MultiplicationQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02162"></a><span class="lineno"> 2162</span> <span class="keyword"></span>{</div><div class="line"><a name="l02163"></a><span class="lineno"> 2163</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span> }</div><div class="line"><a name="l02165"></a><span class="lineno"> 2165</span> </div><div class="line"><a name="l02166"></a><span class="lineno"> 2166</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateNormalization(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_normalization_queue_descriptor.xhtml">NormalizationQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02167"></a><span class="lineno"> 2167</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02168"></a><span class="lineno"> 2168</span> <span class="keyword"></span>{</div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02170"></a><span class="lineno"> 2170</span> }</div><div class="line"><a name="l02171"></a><span class="lineno"> 2171</span> </div><div class="line"><a name="l02172"></a><span class="lineno"> 2172</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateOutput(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_mem_copy_queue_descriptor.xhtml">OutputQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02173"></a><span class="lineno"> 2173</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02174"></a><span class="lineno"> 2174</span> <span class="keyword"></span>{</div><div class="line"><a name="l02175"></a><span class="lineno"> 2175</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02176"></a><span class="lineno"> 2176</span> }</div><div class="line"><a name="l02177"></a><span class="lineno"> 2177</span> </div><div class="line"><a name="l02178"></a><span class="lineno"> 2178</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreatePad(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_pad_queue_descriptor.xhtml">PadQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02179"></a><span class="lineno"> 2179</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*Info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02180"></a><span class="lineno"> 2180</span> <span class="keyword"></span>{</div><div class="line"><a name="l02181"></a><span class="lineno"> 2181</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02182"></a><span class="lineno"> 2182</span> }</div><div class="line"><a name="l02183"></a><span class="lineno"> 2183</span> </div><div class="line"><a name="l02184"></a><span class="lineno"> 2184</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreatePermute(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_permute_queue_descriptor.xhtml">PermuteQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02185"></a><span class="lineno"> 2185</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02186"></a><span class="lineno"> 2186</span> <span class="keyword"></span>{</div><div class="line"><a name="l02187"></a><span class="lineno"> 2187</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02188"></a><span class="lineno"> 2188</span> }</div><div class="line"><a name="l02189"></a><span class="lineno"> 2189</span> </div><div class="line"><a name="l02190"></a><span class="lineno"> 2190</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreatePooling2d(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_pooling2d_queue_descriptor.xhtml">Pooling2dQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02191"></a><span class="lineno"> 2191</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02192"></a><span class="lineno"> 2192</span> <span class="keyword"></span>{</div><div class="line"><a name="l02193"></a><span class="lineno"> 2193</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02194"></a><span class="lineno"> 2194</span> }</div><div class="line"><a name="l02195"></a><span class="lineno"> 2195</span> </div><div class="line"><a name="l02196"></a><span class="lineno"> 2196</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreatePooling3d(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_pooling3d_queue_descriptor.xhtml">Pooling3dQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02197"></a><span class="lineno"> 2197</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02198"></a><span class="lineno"> 2198</span> <span class="keyword"></span>{</div><div class="line"><a name="l02199"></a><span class="lineno"> 2199</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02200"></a><span class="lineno"> 2200</span> }</div><div class="line"><a name="l02201"></a><span class="lineno"> 2201</span> </div><div class="line"><a name="l02202"></a><span class="lineno"> 2202</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreatePreCompiled(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_pre_compiled_queue_descriptor.xhtml">PreCompiledQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02203"></a><span class="lineno"> 2203</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02204"></a><span class="lineno"> 2204</span> <span class="keyword"></span>{</div><div class="line"><a name="l02205"></a><span class="lineno"> 2205</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02206"></a><span class="lineno"> 2206</span> }</div><div class="line"><a name="l02207"></a><span class="lineno"> 2207</span> </div><div class="line"><a name="l02208"></a><span class="lineno"> 2208</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreatePrelu(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_prelu_queue_descriptor.xhtml">PreluQueueDescriptor</a> &<span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02209"></a><span class="lineno"> 2209</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> &<span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02210"></a><span class="lineno"> 2210</span> <span class="keyword"></span>{</div><div class="line"><a name="l02211"></a><span class="lineno"> 2211</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02212"></a><span class="lineno"> 2212</span> }</div><div class="line"><a name="l02213"></a><span class="lineno"> 2213</span> </div><div class="line"><a name="l02214"></a><span class="lineno"> 2214</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateQuantize(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantize_queue_descriptor.xhtml">QuantizeQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02215"></a><span class="lineno"> 2215</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*Info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02216"></a><span class="lineno"> 2216</span> <span class="keyword"></span>{</div><div class="line"><a name="l02217"></a><span class="lineno"> 2217</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02218"></a><span class="lineno"> 2218</span> }</div><div class="line"><a name="l02219"></a><span class="lineno"> 2219</span> </div><div class="line"><a name="l02220"></a><span class="lineno"> 2220</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateQLstm(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_q_lstm_queue_descriptor.xhtml">QLstmQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02221"></a><span class="lineno"> 2221</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02222"></a><span class="lineno"> 2222</span> <span class="keyword"></span>{</div><div class="line"><a name="l02223"></a><span class="lineno"> 2223</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02224"></a><span class="lineno"> 2224</span> }</div><div class="line"><a name="l02225"></a><span class="lineno"> 2225</span> </div><div class="line"><a name="l02226"></a><span class="lineno"> 2226</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateQuantizedLstm(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml">QuantizedLstmQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02227"></a><span class="lineno"> 2227</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02228"></a><span class="lineno"> 2228</span> <span class="keyword"></span>{</div><div class="line"><a name="l02229"></a><span class="lineno"> 2229</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02230"></a><span class="lineno"> 2230</span> }</div><div class="line"><a name="l02231"></a><span class="lineno"> 2231</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateRank(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_rank_queue_descriptor.xhtml">RankQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02232"></a><span class="lineno"> 2232</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02233"></a><span class="lineno"> 2233</span> <span class="keyword"></span>{</div><div class="line"><a name="l02234"></a><span class="lineno"> 2234</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02235"></a><span class="lineno"> 2235</span> }</div><div class="line"><a name="l02236"></a><span class="lineno"> 2236</span> </div><div class="line"><a name="l02237"></a><span class="lineno"> 2237</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateReduce(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_reduce_queue_descriptor.xhtml">ReduceQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02238"></a><span class="lineno"> 2238</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02239"></a><span class="lineno"> 2239</span> <span class="keyword"></span>{</div><div class="line"><a name="l02240"></a><span class="lineno"> 2240</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02241"></a><span class="lineno"> 2241</span> }</div><div class="line"><a name="l02242"></a><span class="lineno"> 2242</span> </div><div class="line"><a name="l02243"></a><span class="lineno"> 2243</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateReshape(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_reshape_queue_descriptor.xhtml">ReshapeQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02244"></a><span class="lineno"> 2244</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02245"></a><span class="lineno"> 2245</span> <span class="keyword"></span>{</div><div class="line"><a name="l02246"></a><span class="lineno"> 2246</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02247"></a><span class="lineno"> 2247</span> }</div><div class="line"><a name="l02248"></a><span class="lineno"> 2248</span> </div><div class="line"><a name="l02249"></a><span class="lineno"> 2249</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateResize(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_resize_queue_descriptor.xhtml">ResizeQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02250"></a><span class="lineno"> 2250</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02251"></a><span class="lineno"> 2251</span> <span class="keyword"></span>{</div><div class="line"><a name="l02252"></a><span class="lineno"> 2252</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02253"></a><span class="lineno"> 2253</span> }</div><div class="line"><a name="l02254"></a><span class="lineno"> 2254</span> </div><div class="line"><a name="l02255"></a><span class="lineno"> 2255</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateShape(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_shape_queue_descriptor.xhtml">ShapeQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02256"></a><span class="lineno"> 2256</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02257"></a><span class="lineno"> 2257</span> <span class="keyword"></span>{</div><div class="line"><a name="l02258"></a><span class="lineno"> 2258</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02259"></a><span class="lineno"> 2259</span> }</div><div class="line"><a name="l02260"></a><span class="lineno"> 2260</span> </div><div class="line"><a name="l02261"></a><span class="lineno"> 2261</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateSlice(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_slice_queue_descriptor.xhtml">SliceQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02262"></a><span class="lineno"> 2262</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02263"></a><span class="lineno"> 2263</span> <span class="keyword"></span>{</div><div class="line"><a name="l02264"></a><span class="lineno"> 2264</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02265"></a><span class="lineno"> 2265</span> }</div><div class="line"><a name="l02266"></a><span class="lineno"> 2266</span> </div><div class="line"><a name="l02267"></a><span class="lineno"> 2267</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateSoftmax(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_softmax_queue_descriptor.xhtml">SoftmaxQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02268"></a><span class="lineno"> 2268</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02269"></a><span class="lineno"> 2269</span> <span class="keyword"></span>{</div><div class="line"><a name="l02270"></a><span class="lineno"> 2270</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02271"></a><span class="lineno"> 2271</span> }</div><div class="line"><a name="l02272"></a><span class="lineno"> 2272</span> </div><div class="line"><a name="l02273"></a><span class="lineno"> 2273</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateSplitter(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_splitter_queue_descriptor.xhtml">SplitterQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02274"></a><span class="lineno"> 2274</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02275"></a><span class="lineno"> 2275</span> <span class="keyword"></span>{</div><div class="line"><a name="l02276"></a><span class="lineno"> 2276</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02277"></a><span class="lineno"> 2277</span> }</div><div class="line"><a name="l02278"></a><span class="lineno"> 2278</span> </div><div class="line"><a name="l02279"></a><span class="lineno"> 2279</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateSpaceToBatchNd(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_space_to_batch_nd_queue_descriptor.xhtml">SpaceToBatchNdQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02280"></a><span class="lineno"> 2280</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02281"></a><span class="lineno"> 2281</span> <span class="keyword"></span>{</div><div class="line"><a name="l02282"></a><span class="lineno"> 2282</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02283"></a><span class="lineno"> 2283</span> }</div><div class="line"><a name="l02284"></a><span class="lineno"> 2284</span> </div><div class="line"><a name="l02285"></a><span class="lineno"> 2285</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateSpaceToDepth(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_space_to_depth_queue_descriptor.xhtml">SpaceToDepthQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02286"></a><span class="lineno"> 2286</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02287"></a><span class="lineno"> 2287</span> <span class="keyword"></span>{</div><div class="line"><a name="l02288"></a><span class="lineno"> 2288</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02289"></a><span class="lineno"> 2289</span> }</div><div class="line"><a name="l02290"></a><span class="lineno"> 2290</span> </div><div class="line"><a name="l02291"></a><span class="lineno"> 2291</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateStack(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_stack_queue_descriptor.xhtml">StackQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02292"></a><span class="lineno"> 2292</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02293"></a><span class="lineno"> 2293</span> <span class="keyword"></span>{</div><div class="line"><a name="l02294"></a><span class="lineno"> 2294</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02295"></a><span class="lineno"> 2295</span> }</div><div class="line"><a name="l02296"></a><span class="lineno"> 2296</span> </div><div class="line"><a name="l02297"></a><span class="lineno"> 2297</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateStridedSlice(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_strided_slice_queue_descriptor.xhtml">StridedSliceQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02298"></a><span class="lineno"> 2298</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02299"></a><span class="lineno"> 2299</span> <span class="keyword"></span>{</div><div class="line"><a name="l02300"></a><span class="lineno"> 2300</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02301"></a><span class="lineno"> 2301</span> }</div><div class="line"><a name="l02302"></a><span class="lineno"> 2302</span> </div><div class="line"><a name="l02303"></a><span class="lineno"> 2303</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateSubtraction(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_subtraction_queue_descriptor.xhtml">SubtractionQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02304"></a><span class="lineno"> 2304</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02305"></a><span class="lineno"> 2305</span> <span class="keyword"></span>{</div><div class="line"><a name="l02306"></a><span class="lineno"> 2306</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02307"></a><span class="lineno"> 2307</span> }</div><div class="line"><a name="l02308"></a><span class="lineno"> 2308</span> </div><div class="line"><a name="l02309"></a><span class="lineno"> 2309</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateSwitch(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_switch_queue_descriptor.xhtml">SwitchQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02310"></a><span class="lineno"> 2310</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02311"></a><span class="lineno"> 2311</span> <span class="keyword"></span>{</div><div class="line"><a name="l02312"></a><span class="lineno"> 2312</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02313"></a><span class="lineno"> 2313</span> }</div><div class="line"><a name="l02314"></a><span class="lineno"> 2314</span> </div><div class="line"><a name="l02315"></a><span class="lineno"> 2315</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateTranspose(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_transpose_queue_descriptor.xhtml">TransposeQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02316"></a><span class="lineno"> 2316</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02317"></a><span class="lineno"> 2317</span> <span class="keyword"></span>{</div><div class="line"><a name="l02318"></a><span class="lineno"> 2318</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02319"></a><span class="lineno"> 2319</span> }</div><div class="line"><a name="l02320"></a><span class="lineno"> 2320</span> </div><div class="line"><a name="l02321"></a><span class="lineno"> 2321</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateTransposeConvolution2d(</div><div class="line"><a name="l02322"></a><span class="lineno"> 2322</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_transpose_convolution2d_queue_descriptor.xhtml">TransposeConvolution2dQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02323"></a><span class="lineno"> 2323</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02324"></a><span class="lineno"> 2324</span> <span class="keyword"></span>{</div><div class="line"><a name="l02325"></a><span class="lineno"> 2325</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02326"></a><span class="lineno"> 2326</span> }</div><div class="line"><a name="l02327"></a><span class="lineno"> 2327</span> </div><div class="line"><a name="l02328"></a><span class="lineno"> 2328</span> std::unique_ptr<IWorkload> IWorkloadFactory::CreateUnidirectionalSequenceLstm(</div><div class="line"><a name="l02329"></a><span class="lineno"> 2329</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml">UnidirectionalSequenceLstmQueueDescriptor</a>& <span class="comment">/*descriptor*/</span>,</div><div class="line"><a name="l02330"></a><span class="lineno"> 2330</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*info*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02331"></a><span class="lineno"> 2331</span> <span class="keyword"></span>{</div><div class="line"><a name="l02332"></a><span class="lineno"> 2332</span>  <span class="keywordflow">return</span> std::unique_ptr<IWorkload>();</div><div class="line"><a name="l02333"></a><span class="lineno"> 2333</span> }</div><div class="line"><a name="l02334"></a><span class="lineno"> 2334</span> </div><div class="line"><a name="l02335"></a><span class="lineno"> 2335</span> } <span class="comment">// namepsace armnn</span></div><div class="ttc" id="structarmnn_1_1_multiplication_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_multiplication_queue_descriptor.xhtml">armnn::MultiplicationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00286">WorkloadData.hpp:286</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a583550d0f265fd3756f7de0e42c51953"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a583550d0f265fd3756f7de0e42c51953">armnn::LayerType::Convolution3d</a></div></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="structarmnn_1_1_cast_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_cast_queue_descriptor.xhtml">armnn::CastQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00168">WorkloadData.hpp:168</a></div></div>
<div class="ttc" id="structarmnn_1_1_instance_normalization_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_instance_normalization_queue_descriptor.xhtml">armnn::InstanceNormalizationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00385">WorkloadData.hpp:385</a></div></div>
<div class="ttc" id="structarmnn_1_1_permute_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_permute_queue_descriptor.xhtml">armnn::PermuteQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00195">WorkloadData.hpp:195</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_quantize_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_quantize_queue_descriptor.xhtml">armnn::QuantizeQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00321">WorkloadData.hpp:321</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#l00066">INetwork.hpp:66</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="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4af6c0e3a1c3cfabd32ae8d3ae741fcf0a"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af6c0e3a1c3cfabd32ae8d3ae741fcf0a">armnn::LayerType::Comparison</a></div></div>
<div class="ttc" id="structarmnn_1_1_maximum_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_maximum_queue_descriptor.xhtml">armnn::MaximumQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00304">WorkloadData.hpp:304</a></div></div>
<div class="ttc" id="structarmnn_1_1_detection_post_process_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_queue_descriptor.xhtml">armnn::DetectionPostProcessQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00261">WorkloadData.hpp:261</a></div></div>
<div class="ttc" id="structarmnn_1_1_gather_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_gather_queue_descriptor.xhtml">armnn::GatherQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00547">WorkloadData.hpp:547</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_splitter_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_splitter_queue_descriptor.xhtml">armnn::SplitterQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00111">WorkloadData.hpp:111</a></div></div>
<div class="ttc" id="structarmnn_1_1_constant_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_constant_queue_descriptor.xhtml">armnn::ConstantQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00400">WorkloadData.hpp:400</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3"><div class="ttname"><a href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a></div></div>
<div class="ttc" id="structarmnn_1_1_quantized_lstm_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml">armnn::QuantizedLstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00654">WorkloadData.hpp:654</a></div></div>
<div class="ttc" id="_i_backend_internal_8hpp_xhtml"><div class="ttname"><a href="_i_backend_internal_8hpp.xhtml">IBackendInternal.hpp</a></div></div>
<div class="ttc" id="_deprecated_8hpp_xhtml_ab66a241a0ed3ee89c866e777b035d0ed"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_BEGIN</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00033">Deprecated.hpp:33</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="structarmnn_1_1_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml">armnn::QueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00024">WorkloadData.hpp:24</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a5b6893cda5b69359a4244c06054da18f"><div class="ttname"><a href="namespacearmnn.xhtml#a5b6893cda5b69359a4244c06054da18f">armnn::ModelOptions</a></div><div class="ttdeci">std::vector< BackendOptions > ModelOptions</div><div class="ttdef"><b>Definition:</b> <a href="_backend_options_8hpp_source.xhtml#l00018">BackendOptions.hpp:18</a></div></div>
<div class="ttc" id="structarmnn_1_1_convert_bf16_to_fp32_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convert_bf16_to_fp32_queue_descriptor.xhtml">armnn::ConvertBf16ToFp32QueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00484">WorkloadData.hpp:484</a></div></div>
<div class="ttc" id="namespacearmnn_serializer_xhtml_a9a8118be7780e95363d631cbca7e7800"><div class="ttname"><a href="namespacearmnn_serializer.xhtml#a9a8118be7780e95363d631cbca7e7800">armnnSerializer::Layer</a></div><div class="ttdeci">Layer</div><div class="ttdef"><b>Definition:</b> <a href="_armnn_schema__generated_8h_source.xhtml#l01249">ArmnnSchema_generated.h:1249</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="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_a56943a0946e5f15e5e58054b8e7a04a4a4cd9f3996d60790cd11c04f842ebc43c"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4cd9f3996d60790cd11c04f842ebc43c">armnn::LayerType::Cast</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a2cf1ea7140f419eba6d60d01dd0a795a"><div class="ttname"><a href="namespacearmnn.xhtml#a2cf1ea7140f419eba6d60d01dd0a795a">armnn::MakeTransformIterator</a></div><div class="ttdeci">constexpr TransformIterator< Function, Iterator > MakeTransformIterator(Iterator i, Function f)</div><div class="ttdef"><b>Definition:</b> <a href="_transform_iterator_8hpp_source.xhtml#l00081">TransformIterator.hpp:81</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_convolution3d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution3d_queue_descriptor.xhtml">armnn::Convolution3dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00229">WorkloadData.hpp:229</a></div></div>
<div class="ttc" id="structarmnn_1_1_stack_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_stack_queue_descriptor.xhtml">armnn::StackQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00152">WorkloadData.hpp:152</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4addf4f83b056acd5549949fc0350e9aad"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4addf4f83b056acd5549949fc0350e9aad">armnn::LayerType::ConvertFp32ToFp16</a></div></div>
<div class="ttc" id="structarmnn_1_1_merge_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_merge_queue_descriptor.xhtml">armnn::MergeQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00569">WorkloadData.hpp:569</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_ac2807505b850738bc8a1991ce669dd47"><div class="ttname"><a href="namespacearmnn.xhtml#ac2807505b850738bc8a1991ce669dd47">armnn::BackendRegistryInstance</a></div><div class="ttdeci">BackendRegistry & BackendRegistryInstance()</div><div class="ttdef"><b>Definition:</b> <a href="_backend_registry_8cpp_source.xhtml#l00015">BackendRegistry.cpp:15</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_ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a></div></div>
<div class="ttc" id="structarmnn_1_1_addition_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_addition_queue_descriptor.xhtml">armnn::AdditionQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00280">WorkloadData.hpp:280</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aec4875f03ff0bb0b26cf76ac7f41e3c8"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aec4875f03ff0bb0b26cf76ac7f41e3c8">armnn::LayerType::Reduce</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div>
<div class="ttc" id="include_2armnn_2backends_2_workload_factory_8hpp_xhtml"><div class="ttname"><a href="include_2armnn_2backends_2_workload_factory_8hpp.xhtml">WorkloadFactory.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_minimum_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_minimum_queue_descriptor.xhtml">armnn::MinimumQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00515">WorkloadData.hpp:515</a></div></div>
<div class="ttc" id="_backend_helper_8hpp_xhtml"><div class="ttname"><a href="_backend_helper_8hpp.xhtml">BackendHelper.hpp</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__quick__start_8dox_source.xhtml#l00006">01_00_quick_start.dox:6</a></div></div>
<div class="ttc" id="structarmnn_1_1_fully_connected_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml">armnn::FullyConnectedQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00180">WorkloadData.hpp:180</a></div></div>
<div class="ttc" id="structarmnn_1_1_prelu_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_prelu_queue_descriptor.xhtml">armnn::PreluQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00579">WorkloadData.hpp:579</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_batch_to_space_nd_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_queue_descriptor.xhtml">armnn::BatchToSpaceNdQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00504">WorkloadData.hpp:504</a></div></div>
<div class="ttc" id="structarmnn_1_1_softmax_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_softmax_queue_descriptor.xhtml">armnn::SoftmaxQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00105">WorkloadData.hpp:105</a></div></div>
<div class="ttc" id="structarmnn_1_1_division_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_division_queue_descriptor.xhtml">armnn::DivisionQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00292">WorkloadData.hpp:292</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="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a68be4837f6c739877233e527a996dd00"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a68be4837f6c739877233e527a996dd00">armnn::LayerType::Merge</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling3d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling3d_queue_descriptor.xhtml">armnn::Pooling3dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00207">WorkloadData.hpp:207</a></div></div>
<div class="ttc" id="structarmnn_1_1_subtraction_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_subtraction_queue_descriptor.xhtml">armnn::SubtractionQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00298">WorkloadData.hpp:298</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a1189467870bc421ae59277e750263eb2"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a1189467870bc421ae59277e750263eb2">armnn::LayerType::L2Normalization</a></div></div>
<div class="ttc" id="_backend_registry_8hpp_xhtml"><div class="ttname"><a href="_backend_registry_8hpp.xhtml">BackendRegistry.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_pad_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pad_queue_descriptor.xhtml">armnn::PadQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00316">WorkloadData.hpp:316</a></div></div>
<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml">armnn::ConcatQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00130">WorkloadData.hpp:130</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="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4af4f53c8297dc1cb53d4e6f8151070a30"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af4f53c8297dc1cb53d4e6f8151070a30">armnn::LayerType::LogicalBinary</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ada0fb4f79f3673b4ebd94a42175bf78d"><div class="ttname"><a href="namespacearmnn.xhtml#ada0fb4f79f3673b4ebd94a42175bf78d">armnn::GetBiasTypeFromWeightsType</a></div><div class="ttdeci">armnn::Optional< armnn::DataType > GetBiasTypeFromWeightsType(armnn::Optional< armnn::DataType > weightsType)</div><div class="ttdef"><b>Definition:</b> <a href="_layer_support_rules_8hpp_source.xhtml#l00014">LayerSupportRules.hpp:14</a></div></div>
<div class="ttc" id="structarmnn_1_1_space_to_depth_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_space_to_depth_queue_descriptor.xhtml">armnn::SpaceToDepthQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00422">WorkloadData.hpp:422</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aca39930e22f40d10155a57dba32240bb"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aca39930e22f40d10155a57dba32240bb">armnn::LayerType::Quantize</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_aa83593035de93eb4b6dddb9dc8f5ced6"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#aa83593035de93eb4b6dddb9dc8f5ced6">armnn::IWorkloadFactory::CreateInput</a></div><div class="ttdeci">virtual std::unique_ptr< IWorkload > CreateInput(const InputQueueDescriptor &descriptor, const WorkloadInfo &info) const =0</div></div>
<div class="ttc" id="structarmnn_1_1_resize_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_resize_queue_descriptor.xhtml">armnn::ResizeQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00366">WorkloadData.hpp:366</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_space_to_batch_nd_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_space_to_batch_nd_queue_descriptor.xhtml">armnn::SpaceToBatchNdQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00417">WorkloadData.hpp:417</a></div></div>
<div class="ttc" id="structarmnn_1_1_floor_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_floor_queue_descriptor.xhtml">armnn::FloorQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00427">WorkloadData.hpp:427</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a912a4b4d73726c282e3f79aa2c390d6c"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a912a4b4d73726c282e3f79aa2c390d6c">armnn::LayerType::ConvertFp16ToFp32</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a6eb8b8b560161603402c0238b3a7d8b0"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6eb8b8b560161603402c0238b3a7d8b0">armnn::LayerType::Subtraction</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a6512859957de2cf2a5fe4dccb07bd9da"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6512859957de2cf2a5fe4dccb07bd9da">armnn::LayerType::ConvertFp32ToBf16</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a1db19222ac424bd7162142ddf929fd2a"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a1db19222ac424bd7162142ddf929fd2a">armnn::LayerType::DetectionPostProcess</a></div></div>
<div class="ttc" id="structarmnn_1_1_fake_quantization_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fake_quantization_queue_descriptor.xhtml">armnn::FakeQuantizationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00371">WorkloadData.hpp:371</a></div></div>
<div class="ttc" id="_deprecated_8hpp_xhtml_ad762b11b48e5c1d1c1743f529485728a"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_END</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00034">Deprecated.hpp:34</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="structarmnn_1_1_lstm_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml">armnn::LstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00432">WorkloadData.hpp:432</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div>
<div class="ttc" id="_types_8hpp_xhtml"><div class="ttname"><a href="_types_8hpp.xhtml">Types.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aad22c799930d644e8468fe44c0312d53"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aad22c799930d644e8468fe44c0312d53">armnn::LayerType::Gather</a></div></div>
<div class="ttc" id="_transform_iterator_8hpp_xhtml"><div class="ttname"><a href="_transform_iterator_8hpp.xhtml">TransformIterator.hpp</a></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="structarmnn_1_1_comparison_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_comparison_queue_descriptor.xhtml">armnn::ComparisonQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00706">WorkloadData.hpp:706</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_a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a4dd48794eb3305a0f5aece88b111a97b">armnn::LayerType::MemCopy</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="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a8321e79c278ec510f63675c040594892"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a8321e79c278ec510f63675c040594892">armnn::LayerType::Maximum</a></div></div>
<div class="ttc" id="structarmnn_1_1_convert_fp16_to_fp32_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convert_fp16_to_fp32_queue_descriptor.xhtml">armnn::ConvertFp16ToFp32QueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00494">WorkloadData.hpp:494</a></div></div>
<div class="ttc" id="_i_layer_support_8hpp_xhtml"><div class="ttname"><a href="_i_layer_support_8hpp.xhtml">ILayerSupport.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a1ba143ebe524d46181a4b53470693278"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a1ba143ebe524d46181a4b53470693278">armnn::LayerType::PreCompiled</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="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aa7c59ccedc6a3bd90c17f3b990afefad"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa7c59ccedc6a3bd90c17f3b990afefad">armnn::LayerType::Reshape</a></div></div>
<div class="ttc" id="include_2armnn_2backends_2_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="include_2armnn_2backends_2_tensor_handle_8hpp.xhtml">TensorHandle.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ae6c5f1b51bd32133c4dcc632045d6b58"><div class="ttname"><a href="namespacearmnn.xhtml#ae6c5f1b51bd32133c4dcc632045d6b58">armnn::UnidirectionalSequenceLstmDescriptor</a></div><div class="ttdeci">LstmDescriptor UnidirectionalSequenceLstmDescriptor</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01150">Descriptors.hpp:1150</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="classarmnn_1_1_i_workload_factory_xhtml_a74dc9ec1a223eab8b072368b2dacee87"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a74dc9ec1a223eab8b072368b2dacee87">armnn::IWorkloadFactory::IsLayerSupported</a></div><div class="ttdeci">static bool IsLayerSupported(const BackendId &backendId, const IConnectableLayer &layer, Optional< DataType > dataType, std::string &outReasonIfUnsupported)</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01516">WorkloadFactory.cpp:1516</a></div></div>
<div class="ttc" id="structarmnn_1_1_reduce_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_reduce_queue_descriptor.xhtml">armnn::ReduceQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00721">WorkloadData.hpp:721</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a9bc35e069257a508e14ed82965a8661d"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a9bc35e069257a508e14ed82965a8661d">armnn::LayerType::Dequantize</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a2b3140dc366b9fcd25ed786a79d1817c"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2b3140dc366b9fcd25ed786a79d1817c">armnn::LayerType::Pooling3d</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="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a></div></div>
<div class="ttc" id="structarmnn_1_1_rank_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_rank_queue_descriptor.xhtml">armnn::RankQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00351">WorkloadData.hpp:351</a></div></div>
<div class="ttc" id="structarmnn_1_1_logical_binary_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_logical_binary_queue_descriptor.xhtml">armnn::LogicalBinaryQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00716">WorkloadData.hpp:716</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a0ca5f33c1d35fd4105d3a26a2823f9dd"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a0ca5f33c1d35fd4105d3a26a2823f9dd">armnn::LayerType::ChannelShuffle</a></div></div>
<div class="ttc" id="structarmnn_1_1_convert_fp32_to_fp16_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convert_fp32_to_fp16_queue_descriptor.xhtml">armnn::ConvertFp32ToFp16QueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00499">WorkloadData.hpp:499</a></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_transpose_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_queue_descriptor.xhtml">armnn::TransposeConvolution2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00584">WorkloadData.hpp:584</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00214">WorkloadData.hpp:214</a></div></div>
<div class="ttc" id="structarmnn_1_1_mem_copy_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_mem_copy_queue_descriptor.xhtml">armnn::MemCopyQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00086">WorkloadData.hpp:86</a></div></div>
<div class="ttc" id="structarmnn_1_1_fill_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fill_queue_descriptor.xhtml">armnn::FillQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00174">WorkloadData.hpp:174</a></div></div>
<div class="ttc" id="structarmnn_1_1_slice_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_slice_queue_descriptor.xhtml">armnn::SliceQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00696">WorkloadData.hpp:696</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a021da1b20f73dc252361a54d80497ef3"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a021da1b20f73dc252361a54d80497ef3">armnn::LayerType::Rank</a></div></div>
<div class="ttc" id="_layer_8hpp_xhtml"><div class="ttname"><a href="_layer_8hpp.xhtml">Layer.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_empty_optional_xhtml"><div class="ttname"><a href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a></div><div class="ttdoc">EmptyOptional is used to initialize the Optional class in case we want to have default value for an O...</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00032">Optional.hpp:32</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a93bca63ecbb003649425dd0e4ba79a99"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a93bca63ecbb003649425dd0e4ba79a99">armnn::LayerType::StandIn</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a115bfc5d633eae55c67f9588acdd2bf9"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a115bfc5d633eae55c67f9588acdd2bf9">armnn::LayerType::MemImport</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a3028cc42e40f9a1f4f8b35556d9715a4"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a3028cc42e40f9a1f4f8b35556d9715a4">armnn::LayerType::GatherNd</a></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_depth_to_space_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depth_to_space_queue_descriptor.xhtml">armnn::DepthToSpaceQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00701">WorkloadData.hpp:701</a></div></div>
<div class="ttc" id="structarmnn_1_1_l2_normalization_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_queue_descriptor.xhtml">armnn::L2NormalizationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00390">WorkloadData.hpp:390</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a880c1273b27d27cfc82004c3a4b205c9"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a880c1273b27d27cfc82004c3a4b205c9">armnn::LayerType::Shape</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4abbc155fb2b111bf61c4f5ff892915e6b"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4abbc155fb2b111bf61c4f5ff892915e6b">armnn::LayerType::Switch</a></div></div>
<div class="ttc" id="structarmnn_1_1_transpose_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_queue_descriptor.xhtml">armnn::TransposeQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00597">WorkloadData.hpp:597</a></div></div>
<div class="ttc" id="structarmnn_1_1_strided_slice_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_strided_slice_queue_descriptor.xhtml">armnn::StridedSliceQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00509">WorkloadData.hpp:509</a></div></div>
<div class="ttc" id="structarmnn_1_1_q_lstm_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_q_lstm_queue_descriptor.xhtml">armnn::QLstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00602">WorkloadData.hpp:602</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="structarmnn_1_1_arg_min_max_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_queue_descriptor.xhtml">armnn::ArgMinMaxQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00163">WorkloadData.hpp:163</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml">armnn::BatchNormalizationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00333">WorkloadData.hpp:333</a></div></div>
<div class="ttc" id="structarmnn_1_1_mem_import_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_mem_import_queue_descriptor.xhtml">armnn::MemImportQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00094">WorkloadData.hpp:94</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a890a37ff3bfe123414ba7e6f052b49f3"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a890a37ff3bfe123414ba7e6f052b49f3">armnn::LayerType::QuantizedLstm</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="structarmnn_1_1_convert_fp32_to_bf16_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convert_fp32_to_bf16_queue_descriptor.xhtml">armnn::ConvertFp32ToBf16QueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00489">WorkloadData.hpp:489</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a06b091bc9aea697ba473c71f0bb55925"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a06b091bc9aea697ba473c71f0bb55925">armnn::LayerType::Lstm</a></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="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about TensorInfos of a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></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="_layers_fwd_8hpp_xhtml"><div class="ttname"><a href="_layers_fwd_8hpp.xhtml">LayersFwd.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_switch_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_switch_queue_descriptor.xhtml">armnn::SwitchQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00574">WorkloadData.hpp:574</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a3489c7b05e180496cb2ce8ac73887f48"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a3489c7b05e180496cb2ce8ac73887f48">armnn::LayerType::FakeQuantization</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</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="structarmnn_1_1_channel_shuffle_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_channel_shuffle_queue_descriptor.xhtml">armnn::ChannelShuffleQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00783">WorkloadData.hpp:783</a></div></div>
<div class="ttc" id="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_unidirectional_sequence_lstm_queue_descriptor.xhtml">armnn::UnidirectionalSequenceLstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00731">WorkloadData.hpp:731</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aa1d0ec6d56f8833a078b5a7ac4caf2d4"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa1d0ec6d56f8833a078b5a7ac4caf2d4">armnn::LayerType::Minimum</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="structarmnn_1_1_pre_compiled_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pre_compiled_queue_descriptor.xhtml">armnn::PreCompiledQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00552">WorkloadData.hpp:552</a></div></div>
<div class="ttc" id="structarmnn_1_1_dequantize_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_dequantize_queue_descriptor.xhtml">armnn::DequantizeQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00564">WorkloadData.hpp:564</a></div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_queue_descriptor.xhtml">armnn::Pooling2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00201">WorkloadData.hpp:201</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_mean_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_mean_queue_descriptor.xhtml">armnn::MeanQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00310">WorkloadData.hpp:310</a></div></div>
<div class="ttc" id="structarmnn_1_1_log_softmax_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_log_softmax_queue_descriptor.xhtml">armnn::LogSoftmaxQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00395">WorkloadData.hpp:395</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a14d24d90ab4ba2956e92e27890ba4c91acb17869fe51048b5a5c4c6106551a255"><div class="ttname"><a href="namespacearmnn.xhtml#a14d24d90ab4ba2956e92e27890ba4c91acb17869fe51048b5a5c4c6106551a255">armnn::PaddingMode::Constant</a></div></div>
<div class="ttc" id="structarmnn_1_1_shape_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_shape_queue_descriptor.xhtml">armnn::ShapeQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00726">WorkloadData.hpp:726</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a13060ebb89d2c21a7a5d897e99cccf72"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a13060ebb89d2c21a7a5d897e99cccf72">armnn::IWorkloadFactory::CreateWorkload</a></div><div class="ttdeci">virtual std::unique_ptr< IWorkload > CreateWorkload(LayerType type, const QueueDescriptor &descriptor, const WorkloadInfo &info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01559">WorkloadFactory.cpp:1559</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="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a3025cdaab2deb0bb2cd642449e570833"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a3025cdaab2deb0bb2cd642449e570833">armnn::LayerType::Division</a></div></div>
<div class="ttc" id="structarmnn_1_1_reshape_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_reshape_queue_descriptor.xhtml">armnn::ReshapeQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00412">WorkloadData.hpp:412</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a300124b2433e0376ec4b19251ac3a9e5"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a300124b2433e0376ec4b19251ac3a9e5">armnn::LayerType::UnidirectionalSequenceLstm</a></div></div>
<div class="ttc" id="structarmnn_1_1_debug_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_debug_queue_descriptor.xhtml">armnn::DebugQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00526">WorkloadData.hpp:526</a></div></div>
<div class="ttc" id="structarmnn_1_1_elementwise_unary_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_elementwise_unary_queue_descriptor.xhtml">armnn::ElementwiseUnaryQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00711">WorkloadData.hpp:711</a></div></div>
<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a></div><div class="ttdoc">Depthwise Convolution 2D layer workload data. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00247">WorkloadData.hpp:247</a></div></div>
<div class="ttc" id="structarmnn_1_1_activation_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_activation_queue_descriptor.xhtml">armnn::ActivationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00158">WorkloadData.hpp:158</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a2ba4c46787312a2467153f40c542851a"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2ba4c46787312a2467153f40c542851a">armnn::LayerType::ConvertBf16ToFp32</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aa603905470e2a5b8c13e96b579ef0dba"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa603905470e2a5b8c13e96b579ef0dba">armnn::LayerType::Debug</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4">armnn::LayerType</a></div><div class="ttdeci">LayerType</div><div class="ttdoc">When adding a new layer, adapt also the LastLayer enum value in the enum class LayerType below...</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00467">Types.hpp:467</a></div></div>
<div class="ttc" id="classarmnn_1_1_backend_id_xhtml"><div class="ttname"><a href="classarmnn_1_1_backend_id.xhtml">armnn::BackendId</a></div><div class="ttdef"><b>Definition:</b> <a href="_backend_id_8hpp_source.xhtml#l00075">BackendId.hpp:75</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a91880b71ea6d007439b7bc7c320b5c25"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a91880b71ea6d007439b7bc7c320b5c25">armnn::LayerType::QLstm</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_normalization_queue_descriptor.xhtml">armnn::NormalizationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00274">WorkloadData.hpp:274</a></div></div>
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