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| <a href="_function_helpers_8h.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 (c) 2018 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#ifndef __ARM_COMPUTE_GRAPH_BACKENDS_DETAIL_FUNCTION_HELPERS_H__</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#define __ARM_COMPUTE_GRAPH_BACKENDS_DETAIL_FUNCTION_HELPERS_H__</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="preprocessor">#include "<a class="code" href="graph_2_logger_8h.xhtml">arm_compute/graph/Logger.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="graph_2_tensor_8h.xhtml">arm_compute/graph/Tensor.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2graph_2_type_printer_8h.xhtml">arm_compute/graph/TypePrinter.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2graph_2_types_8h.xhtml">arm_compute/graph/Types.h</a>"</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2graph_2backends_2_utils_8h.xhtml">arm_compute/graph/backends/Utils.h</a>"</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="_nodes_8h.xhtml">arm_compute/graph/nodes/Nodes.h</a>"</span></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="preprocessor">#include "<a class="code" href="_error_8h.xhtml">arm_compute/core/Error.h</a>"</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_helpers_8h.xhtml">arm_compute/core/Helpers.h</a>"</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor">#include "<a class="code" href="_i_tensor_info_8h.xhtml">arm_compute/core/ITensorInfo.h</a>"</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#include "<a class="code" href="_cast_8h.xhtml">arm_compute/core/utils/misc/Cast.h</a>"</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> </div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> {</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="keyword">namespace </span>graph</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> {</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="keyword">namespace </span>backends</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> {</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="keyword">namespace </span>detail</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> {</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="keyword">template</span> <<span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00056"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a69dd1fc17c7a15f4125873be182c8c76"> 56</a></span> <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a69dd1fc17c7a15f4125873be182c8c76">get_backing_tensor</a>(<a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml">arm_compute::graph::Tensor</a> *tensor)</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> {</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keyword">typename</span> TargetInfo::TensorType *backing_tensor = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordflow">if</span>(tensor != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  {</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(tensor-><a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml#ace1b6c0005a4c373e54bcfdc6d5b7d68">desc</a>().<a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a2a7ca82c5e74421cb45f17e936abf964">target</a> != TargetInfo::TargetType);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="comment">// Get backing tensor handle</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <a class="code" href="classarm__compute_1_1graph_1_1_i_tensor_handle.xhtml">ITensorHandle</a> *tensor_handle = tensor-><a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml#a1a4fd35b8e2e8a2c7ea38b6d37508673">handle</a>();</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="comment">// Get backing tensor</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  backing_tensor = (tensor_handle != <span class="keyword">nullptr</span>) ? arm_compute::utils::cast::polymorphic_cast<typename TargetInfo::TensorType *>(&tensor_handle->tensor()) : <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  }</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> </div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keywordflow">return</span> backing_tensor;</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="keyword">template</span> <<span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00072"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aa6fabefcb8c4bd308219565ddcf00928"> 72</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aa6fabefcb8c4bd308219565ddcf00928">validate_node</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml">INode</a> &node, <span class="keywordtype">size_t</span> num_expected_inputs, <span class="keywordtype">size_t</span> num_expected_outputs)</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> {</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <a class="code" href="graph_2_logger_8h.xhtml#a300d153929a99c7b571d4cda3f7987a5">ARM_COMPUTE_LOG_GRAPH_VERBOSE</a>(<span class="stringliteral">"Creating "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a6b2d83e561886647467f86c20ce39bec">type</a>()</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  << <span class="stringliteral">" Target : "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  << <span class="stringliteral">" ID : "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ac2073f3ae2a49f98a91315ed035f8669">id</a>()</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  << <span class="stringliteral">" Name: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a37627d5d5bba7f4a8690c71c2ab3cb07">name</a>()</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  << std::endl);</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(TargetInfo::TargetType != node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a72123a16315dde55dade52690642f56c">assigned_target</a>());</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a7db9a1c221b414ff11bd4a5b7b97ec8d">num_inputs</a>() != num_expected_inputs);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a20ee33c4a581d8d3507dbb898d47d733">num_outputs</a>() != num_expected_outputs);</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> }</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> </div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ActivationLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00095"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a0f5afb0ddd5aec3a8e4df3c56d7d91f4"> 95</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a0f5afb0ddd5aec3a8e4df3c56d7d91f4">create_activation_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_activation_layer_node.xhtml">ActivationLayerNode</a> &node)</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> {</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  validate_node<TargetInfo>(node, 1 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> </div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> act_info = node.activation_info();</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> </div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="comment">// Create function</span></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ActivationLayerFunction>();</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  func->configure(input, output, act_info);</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span> << node.type()</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  << <span class="stringliteral">" Shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  << <span class="stringliteral">" Activation function: "</span> << act_info.activation()</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  << <span class="stringliteral">" a: "</span> << act_info.a()</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  << <span class="stringliteral">" b: "</span> << act_info.b()</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  << <span class="stringliteral">" InPlace : "</span> << <a class="code" href="namespacearm__compute_1_1graph_1_1backends.xhtml#a4403f766b0d02eb3882a9521d0390986">is_in_place_operation</a>(input, output)</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  << std::endl);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> }</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> </div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> <span class="keyword">template</span> <<span class="keyword">typename</span> BatchNormalizationLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00131"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a5d1a73ab4a0b267033a569c46813b9d5"> 131</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a5d1a73ab4a0b267033a569c46813b9d5">create_batch_normalization_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_batch_normalization_layer_node.xhtml">BatchNormalizationLayerNode</a> &node)</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> {</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  validate_node<TargetInfo>(node, 5 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> </div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keyword">typename</span> TargetInfo::TensorType *mean = get_backing_tensor<TargetInfo>(node.input(1));</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keyword">typename</span> TargetInfo::TensorType *var = get_backing_tensor<TargetInfo>(node.input(2));</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab146b9cbab6e73e7588b240dc709fe01">beta</a> = get_backing_tensor<TargetInfo>(node.input(3));</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keyword">typename</span> TargetInfo::TensorType *gamma = get_backing_tensor<TargetInfo>(node.input(4));</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> epsilon = node.epsilon();</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> fused_act = node.fused_activation();</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<BatchNormalizationLayerFunction>();</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  func->configure(input, output, mean, var, beta, gamma, epsilon, fused_act);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span> << node.type()</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  << <span class="stringliteral">" Shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  << <span class="stringliteral">" Epsilon: "</span> << epsilon << <span class="stringliteral">" "</span></div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  << (fused_act.<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a1d79980f25d38aba3d6777d0afe544f3">enabled</a>() ? <a class="code" href="namespacearm__compute.xhtml#ab75d8ff29ba9b398d5740b3efd156e71">to_string</a>(fused_act.<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a63e05ce4946dd9807c005c1619fa337a">activation</a>()) : <span class="stringliteral">""</span>)</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  << <span class="stringliteral">" InPlace : "</span> << <a class="code" href="namespacearm__compute_1_1graph_1_1backends.xhtml#a4403f766b0d02eb3882a9521d0390986">is_in_place_operation</a>(input, output)</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  << std::endl);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> </div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> }</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> </div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ChannelShuffleLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00172"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a3f5c14020836599056281fe52d7e9dd3"> 172</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a3f5c14020836599056281fe52d7e9dd3">create_channel_shuffle_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_channel_shuffle_layer_node.xhtml">ChannelShuffleLayerNode</a> &node)</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> {</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  validate_node<TargetInfo>(node, 1 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> </div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_groups = node.num_groups();</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> </div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="comment">// Create function</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ChannelShuffleLayerFunction>();</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  func->configure(input, output, num_groups);</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>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span> << node.type()</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  << <span class="stringliteral">" Shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  << <span class="stringliteral">" Num groups: "</span> << num_groups</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  << std::endl);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> </div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> }</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> </div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ConcatenateLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00205"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a627f6bdc4a7de6dbb03acb3d8b3a4d6d"> 205</a></span> std::unique_ptr<arm_compute::IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a627f6bdc4a7de6dbb03acb3d8b3a4d6d">create_concatenate_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_concatenate_layer_node.xhtml">ConcatenateLayerNode</a> &node)</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> {</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <a class="code" href="graph_2_logger_8h.xhtml#a300d153929a99c7b571d4cda3f7987a5">ARM_COMPUTE_LOG_GRAPH_VERBOSE</a>(<span class="stringliteral">"Creating Concatenate node with ID : "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ac2073f3ae2a49f98a91315ed035f8669">id</a>() << <span class="stringliteral">" and Name: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a37627d5d5bba7f4a8690c71c2ab3cb07">name</a>() << std::endl);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a20ee33c4a581d8d3507dbb898d47d733">num_outputs</a>() != 1);</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="comment">// Return nullptr if depth concatenate is switched off</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keywordflow">if</span>(!node.<a class="code" href="classarm__compute_1_1graph_1_1_concatenate_layer_node.xhtml#a37c261b90ba4503127a918cec483d859">is_enabled</a>())</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  {</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  }</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> </div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  std::vector<typename TargetInfo::TensorType *> inputs;</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a7db9a1c221b414ff11bd4a5b7b97ec8d">num_inputs</a>(); ++i)</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  {</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  inputs.push_back(get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ac6dfcf4c1c7d4cb129fda6393e8c0b21">input</a>(i)));</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  }</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a427ac30d0f5274436afbf5c78bc4f644">output</a>(0));</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02">DataLayoutDimension</a> concat_axis = node.<a class="code" href="classarm__compute_1_1graph_1_1_concatenate_layer_node.xhtml#ac3ff7bc54b572572d6cbc4141b9f5db6">concatenation_axis</a>();</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> </div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ConcatenateLayerFunction>();</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  func->configure(inputs, output, concat_axis);</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span> </div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_concatenate_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  << <span class="stringliteral">" Data Type: "</span> << output->info()->data_type()</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  << <span class="stringliteral">" Shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  << <span class="stringliteral">" Num Inputs: "</span> << inputs.size()</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  << <span class="stringliteral">" Axis: "</span> << concat_axis</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  << std::endl);</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> </div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> }</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> </div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ConvolutionLayerFunctions, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00252"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a9e38014fa1e7e08dcbf3b5f8c3bdb81e"> 252</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a9e38014fa1e7e08dcbf3b5f8c3bdb81e">create_convolution_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer_node.xhtml">ConvolutionLayerNode</a> &node, <a class="code" href="classarm__compute_1_1graph_1_1_graph_context.xhtml">GraphContext</a> &ctx)</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>  validate_node<TargetInfo>(node, 3 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</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">// Extract IO and info</span></div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keyword">typename</span> TargetInfo::TensorType *weights = get_backing_tensor<TargetInfo>(node.input(1));</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keyword">typename</span> TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.input(2));</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> </div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</a>(input->info()->data_type()))</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  {</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  biases->info()->set_data_type(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</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> </div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a> = node.convolution_info();</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_groups = node.num_groups();</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute_1_1graph.xhtml#a683661ae75dcb7aef16b9c9bde31517d">ConvolutionMethod</a> conv_algorithm = node.convolution_method();</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> fast_math = node.fast_math_hint() == <a class="code" href="namespacearm__compute_1_1graph.xhtml#ac85a46f3ebd3ab09f576a994ac2dce11a00d23a76e43b46dae9ec7aa9dcbebb32">FastMathHint::Enabled</a>;</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span> </div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="comment">// Create and configure function (we assume that functions have been validated before creation)</span></div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  std::shared_ptr<IMemoryManager> mm = <a class="code" href="namespacearm__compute_1_1graph_1_1backends.xhtml#a8919c520c1cb9086dd1116de509bd481">get_memory_manager</a>(ctx, TargetInfo::TargetType);</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  std::unique_ptr<IFunction> func;</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  std::string func_name;</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="keywordflow">if</span>(conv_algorithm == <a class="code" href="namespacearm__compute_1_1graph.xhtml#a683661ae75dcb7aef16b9c9bde31517da09db1dd1078ec6bdbe2722b4558e578f">ConvolutionMethod::Winograd</a>)</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>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(num_groups != 1, <span class="stringliteral">"WinogradConvolutionLayer does not support grouping!"</span>);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  std::tie(func, func_name) = create_named_memory_managed_function<typename ConvolutionLayerFunctions::WinogradConvolutionLayer>(</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  std::string(<span class="stringliteral">"WinogradConvolutionLayer"</span>), mm,</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  input, weights, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(), fast_math);</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  }</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(conv_algorithm == <a class="code" href="namespacearm__compute_1_1graph.xhtml#a683661ae75dcb7aef16b9c9bde31517dafd1dd0c603be8170f9eae0be9f2f6afb">ConvolutionMethod::Direct</a>)</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  {</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(num_groups != 1, <span class="stringliteral">"DirectConvolutionLayer does not support grouping!"</span>);</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  std::tie(func, func_name) = create_named_function<typename ConvolutionLayerFunctions::DirectConvolutionLayer>(</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  std::string(<span class="stringliteral">"DirectConvolutionLayer"</span>),</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  input, weights, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>);</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  }</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(conv_algorithm == <a class="code" href="namespacearm__compute_1_1graph.xhtml#a683661ae75dcb7aef16b9c9bde31517da5174aac3927faa9ee34befb7fc87a9e3">ConvolutionMethod::GEMM</a>)</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  {</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  std::tie(func, func_name) = create_named_memory_managed_function<typename ConvolutionLayerFunctions::GEMMConvolutionLayer>(</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  std::string(<span class="stringliteral">"GEMMConvolutionLayer"</span>), mm,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  input, weights, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>,</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <a class="code" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>(), <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(), num_groups);</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  {</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  std::tie(func, func_name) = create_named_memory_managed_function<typename ConvolutionLayerFunctions::GenericConvolutionLayer>(</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  std::string(<span class="stringliteral">"GenericConvolutionLayer"</span>), mm,</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  input, weights, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>,</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <a class="code" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>(), <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(), fast_math, num_groups);</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  }</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span> </div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span> << func_name</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  << <span class="stringliteral">" Groups: "</span> << num_groups</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  << <span class="stringliteral">" Input QuantInfo: "</span> << input->info()->quantization_info()</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  << <span class="stringliteral">" Weights QuantInfo: "</span> << weights->info()->quantization_info()</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  << <span class="stringliteral">" Weights shape: "</span> << weights->info()->tensor_shape()</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  << std::endl);</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keywordflow">return</span> func;</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> </div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> <span class="keyword">template</span> <<span class="keyword">typename</span> DeconvolutionLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00331"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ac77fa3bf0d7d7c3fde6243192f93f380"> 331</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ac77fa3bf0d7d7c3fde6243192f93f380">create_deconvolution_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_deconvolution_layer_node.xhtml">DeconvolutionLayerNode</a> &node, <a class="code" href="classarm__compute_1_1graph_1_1_graph_context.xhtml">GraphContext</a> &ctx)</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span> {</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  validate_node<TargetInfo>(node, 3 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span> </div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="keyword">typename</span> TargetInfo::TensorType *weights = get_backing_tensor<TargetInfo>(node.input(1));</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keyword">typename</span> TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.input(2));</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</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> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> deconv_info = node.deconvolution_info();</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> inner_border = node.inner_border();</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span> </div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <span class="comment">// Create and configure function (we assume that functions have been validated before creation)</span></div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  std::shared_ptr<IMemoryManager> mm = <a class="code" href="namespacearm__compute_1_1graph_1_1backends.xhtml#a8919c520c1cb9086dd1116de509bd481">get_memory_manager</a>(ctx, TargetInfo::TargetType);</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  std::unique_ptr<IFunction> func;</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span> </div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  std::tie(func, std::ignore) = create_named_memory_managed_function<DeconvolutionLayerFunction>(</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  std::string(), mm,</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  input, weights, biases, output, deconv_info, inner_border.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#aa19402aa7cd5346df67c0142c75d36c0">x</a>(), inner_border.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#afe5ea0266a8c16e45fe5f5d42235f3f5">y</a>());</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span> </div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span> << node.type()</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  << <span class="stringliteral">" Weights shape: "</span> << weights->info()->tensor_shape()</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  << std::endl);</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <span class="keywordflow">return</span> func;</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span> }</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span> </div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span> <span class="keyword">template</span> <<span class="keyword">typename</span> DepthwiseConvolutionLayerFunctions, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00373"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ab8ff2a40f95b76ec10ac2a98d1a8d594"> 373</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ab8ff2a40f95b76ec10ac2a98d1a8d594">create_depthwise_convolution_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_depthwise_convolution_layer_node.xhtml">DepthwiseConvolutionLayerNode</a> &node)</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span> {</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  validate_node<TargetInfo>(node, 3 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span> </div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <span class="keyword">typename</span> TargetInfo::TensorType *weights = get_backing_tensor<TargetInfo>(node.input(1));</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="keyword">typename</span> TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.input(2));</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span> </div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</a>(input->info()->data_type()))</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  {</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  biases->info()->set_data_type(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  }</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> </div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a> = node.convolution_info();</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute_1_1graph.xhtml#a75ce9b1baad4303a53124d6f0795821f">DepthwiseConvolutionMethod</a> dwc_algorithm = node.depthwise_convolution_method();</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> </div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="comment">// Create and configure function (we assume that functions have been validated before creation)</span></div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  std::unique_ptr<IFunction> func;</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  std::string func_name;</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keywordflow">if</span>(dwc_algorithm == <a class="code" href="namespacearm__compute_1_1graph.xhtml#a75ce9b1baad4303a53124d6f0795821fa3bb7b7f3f021a006e65111fc1d226938">DepthwiseConvolutionMethod::Optimized3x3</a>)</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  {</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  std::tie(func, func_name) = create_named_function<typename DepthwiseConvolutionLayerFunctions::DepthwiseConvolutionLayer3x3>(</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  std::string(<span class="stringliteral">"DepthwiseConvolutionLayer3x3"</span>),</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  input, weights, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>);</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  {</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  std::tie(func, func_name) = create_named_function<typename DepthwiseConvolutionLayerFunctions::GenericDepthwiseConvolutionLayer>(</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  std::string(<span class="stringliteral">"DepthwiseConvolutionLayer"</span>),</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  input, weights, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>);</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> </div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span> << func_name</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  << <span class="stringliteral">" Input QuantInfo: "</span> << input->info()->quantization_info()</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  << <span class="stringliteral">" Weights QuantInfo: "</span> << weights->info()->quantization_info()</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  << <span class="stringliteral">" Weights shape: "</span> << weights->info()->tensor_shape()</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  << std::endl);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <span class="keywordflow">return</span> func;</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> }</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> </div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span> <span class="keyword">template</span> <<span class="keyword">typename</span> EltwiseFunctions, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00430"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aa37faf92f78c0f5cefe2d43c8bf07f18"> 430</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aa37faf92f78c0f5cefe2d43c8bf07f18">create_eltwise_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_eltwise_layer_node.xhtml">EltwiseLayerNode</a> &node)</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span> {</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  validate_node<TargetInfo>(node, 2 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span> </div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input1 = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input2 = get_backing_tensor<TargetInfo>(node.input(1));</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute_1_1graph.xhtml#aa12973876c037bddff8e9ece94aca0e4">EltwiseOperation</a> eltwise_op = node.eltwise_operation();</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6f">ConvertPolicy</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5d58c32bff63e4c34b3234f884a4da58">convert_policy</a> = node.convert_policy();</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input1 == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input2 == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(output == <span class="keyword">nullptr</span>);</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>  std::unique_ptr<IFunction> func = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  std::string func_name;</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <span class="keywordflow">if</span>(eltwise_op == <a class="code" href="namespacearm__compute_1_1graph.xhtml#aa12973876c037bddff8e9ece94aca0e4aec211f7c20af43e742bf2570c3cb84f9">EltwiseOperation::Add</a>)</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  {</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Addition>(</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  std::string(<span class="stringliteral">"ArithmeticAddition"</span>),</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  input1, input2, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5d58c32bff63e4c34b3234f884a4da58">convert_policy</a>);</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  }</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(eltwise_op == <a class="code" href="namespacearm__compute_1_1graph.xhtml#aa12973876c037bddff8e9ece94aca0e4ae80155eceb940c89e2de63ad05868db2">EltwiseOperation::Sub</a>)</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  {</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Subtraction>(</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  std::string(<span class="stringliteral">"ArithmeticSubtraction"</span>),</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  input1, input2, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5d58c32bff63e4c34b3234f884a4da58">convert_policy</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">else</span> <span class="keywordflow">if</span>(eltwise_op == <a class="code" href="namespacearm__compute_1_1graph.xhtml#aa12973876c037bddff8e9ece94aca0e4a62b6d55816cf737bfc6f42e60df1a3f2">EltwiseOperation::Mul</a>)</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>  std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Multiplication>(</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  std::string(<span class="stringliteral">"PixelWiseMultiplication"</span>),</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  input1, input2, output, 1.f, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5d58c32bff63e4c34b3234f884a4da58">convert_policy</a>, node.rounding_policy());</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  }</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  {</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Unsupported element-wise operation!"</span>);</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  }</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span> </div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span> << node.type()</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  << <span class="stringliteral">" Operation "</span> << func_name</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  << <span class="stringliteral">" Data Type: "</span> << input1->info()->data_type()</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  << <span class="stringliteral">" Shape : "</span> << input1->info()->tensor_shape()</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  << std::endl);</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span> </div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <span class="keywordflow">return</span> func;</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span> }</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span> </div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span> <span class="keyword">template</span> <<span class="keyword">typename</span> FlattenLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00490"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a7880c3b249a6dad40da0ebcf6600b0e1"> 490</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a7880c3b249a6dad40da0ebcf6600b0e1">create_flatten_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_flatten_layer_node.xhtml">FlattenLayerNode</a> &node)</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span> {</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  validate_node<TargetInfo>(node, 1 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span> </div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span> </div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(output == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span> </div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<FlattenLayerFunction>();</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  func->configure(input, output);</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span> </div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span> << node.type()</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  << std::endl);</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span> </div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span> }</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span> </div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span> <span class="keyword">template</span> <<span class="keyword">typename</span> FullyConnectedLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00527"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ac64bbd0df74207f9ab59953e21311178"> 527</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ac64bbd0df74207f9ab59953e21311178">create_fully_connected_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer_node.xhtml">FullyConnectedLayerNode</a> &node, <a class="code" href="classarm__compute_1_1graph_1_1_graph_context.xhtml">GraphContext</a> &ctx)</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span> {</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  validate_node<TargetInfo>(node, 3 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</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="comment">// Extract IO and info</span></div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="keyword">typename</span> TargetInfo::TensorType *weights = get_backing_tensor<TargetInfo>(node.input(1));</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="keyword">typename</span> TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.input(2));</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml">FullyConnectedLayerInfo</a> fc_info = node.info();</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span> </div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(weights == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(output == <span class="keyword">nullptr</span>);</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="comment">// Create and configure function</span></div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<FullyConnectedLayerFunction>(<a class="code" href="namespacearm__compute_1_1graph_1_1backends.xhtml#a8919c520c1cb9086dd1116de509bd481">get_memory_manager</a>(ctx, TargetInfo::TargetType));</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  func->configure(input, weights, biases, output, fc_info);</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span> </div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span> << node.type()</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  << <span class="stringliteral">" Input QuantInfo: "</span> << input->info()->quantization_info()</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  << <span class="stringliteral">" Weights QuantInfo: "</span> << weights->info()->quantization_info()</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  << <span class="stringliteral">" Weights shape: "</span> << weights->info()->tensor_shape()</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  << std::endl);</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="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span> }</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span> </div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span> <span class="keyword">template</span> <<span class="keyword">typename</span> NormalizationLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00571"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a2dfc97df083b68f8409ba21d8a0110d8"> 571</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a2dfc97df083b68f8409ba21d8a0110d8">create_normalization_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_normalization_layer_node.xhtml">NormalizationLayerNode</a> &node, <a class="code" href="classarm__compute_1_1graph_1_1_graph_context.xhtml">GraphContext</a> &ctx)</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span> {</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(ctx);</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>  validate_node<TargetInfo>(node, 1 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span> </div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml">NormalizationLayerInfo</a> norm_info = node.normalization_info();</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(output == <span class="keyword">nullptr</span>);</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="comment">// Create and configure function</span></div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<NormalizationLayerFunction>();</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  func->configure(input, output, norm_info);</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span> </div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span> << node.type()</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  << <span class="stringliteral">" Normalization info: "</span> << norm_info.type()</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  << std::endl);</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="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span> }</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span> </div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span> <span class="keyword">template</span> <<span class="keyword">typename</span> PermuteLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00610"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aa65a1becdfa5fc3533d79bba0cd4095c"> 610</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aa65a1becdfa5fc3533d79bba0cd4095c">create_permute_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_permute_layer_node.xhtml">PermuteLayerNode</a> &node)</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span> {</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  validate_node<TargetInfo>(node, 1 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span> </div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">PermutationVector</a> &perm = node.permutation_vector();</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(output == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span> </div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<PermuteLayerFunction>();</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  func->configure(input, output, perm);</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span> </div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span> << node.type()</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  << <span class="stringliteral">" Permutation vector: "</span> << perm</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  << std::endl);</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span> </div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span> }</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span> </div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span> <span class="keyword">template</span> <<span class="keyword">typename</span> PoolingLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00647"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a0e51b62035e79b0f12964cae17ce0480"> 647</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a0e51b62035e79b0f12964cae17ce0480">create_pooling_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_pooling_layer_node.xhtml">PoolingLayerNode</a> &node)</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span> {</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  validate_node<TargetInfo>(node, 1 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span> </div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a> pool_info = node.pooling_info();</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(output == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span> </div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<PoolingLayerFunction>();</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  func->configure(input, output, pool_info);</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>  <span class="comment">// Log info</span></div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span> << node.type()</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  << <span class="stringliteral">" Pooling info: "</span> << pool_info.pool_type()</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  << std::endl);</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span> </div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  <span class="keywordflow">return</span> std::move(func);</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="l00683"></a><span class="lineno"> 683</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ReshapeLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00684"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ad1922deea021647290d0c206723e6c73"> 684</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ad1922deea021647290d0c206723e6c73">create_reshape_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_reshape_layer_node.xhtml">ReshapeLayerNode</a> &node)</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>  validate_node<TargetInfo>(node, 1 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span> </div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(output == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span> </div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ReshapeLayerFunction>();</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  func->configure(input, output);</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span> </div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span> << node.type()</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  << std::endl);</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">return</span> std::move(func);</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> </div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ResizeLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00719"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#adb3a9be16de941b0f601e16c8ac76533"> 719</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#adb3a9be16de941b0f601e16c8ac76533">create_resize_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_resize_layer_node.xhtml">ResizeLayerNode</a> &node)</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>  validate_node<TargetInfo>(node, 1 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span> </div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(output == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a966a9c417ce5e94dca08d9b5e745c0c9">InterpolationPolicy</a> policy = node.policy();</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span> </div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ResizeLayerFunction>();</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  func->configure(input, output, policy, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a8d6b5cada83510220f59e00ce86d4d92">BorderMode::CONSTANT</a>);</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span> </div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span> << node.type()</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  << <span class="stringliteral">" Interpolation: "</span> << policy</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  << std::endl);</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span> </div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  <span class="keywordflow">return</span> std::move(func);</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> </div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span> <span class="keyword">template</span> <<span class="keyword">typename</span> SoftmaxLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00757"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a5271c97b6bef5972c5e259307d52a4da"> 757</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a5271c97b6bef5972c5e259307d52a4da">create_softmax_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_softmax_layer_node.xhtml">SoftmaxLayerNode</a> &node, <a class="code" href="classarm__compute_1_1graph_1_1_graph_context.xhtml">GraphContext</a> &ctx)</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span> {</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  validate_node<TargetInfo>(node, 1 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</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="comment">// Extract IO and info</span></div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab146b9cbab6e73e7588b240dc709fe01">beta</a> = node.beta();</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(output == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span> </div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<SoftmaxLayerFunction>(<a class="code" href="namespacearm__compute_1_1graph_1_1backends.xhtml#a8919c520c1cb9086dd1116de509bd481">get_memory_manager</a>(ctx, TargetInfo::TargetType));</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  func->configure(input, output, beta);</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span> </div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span> << node.type()</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  << std::endl);</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span> </div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span> }</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span> } <span class="comment">// namespace detail</span></div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span> } <span class="comment">// namespace backends</span></div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span> } <span class="comment">// namespace graph</span></div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span> } <span class="comment">// namespace arm_compute</span></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="preprocessor">#endif </span><span class="comment">/* __ARM_COMPUTE_GRAPH_BACKENDS_DETAIL_FUNCTION_HELPERS_H__ */</span><span class="preprocessor"></span></div><div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a2dfc97df083b68f8409ba21d8a0110d8"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a2dfc97df083b68f8409ba21d8a0110d8">arm_compute::graph::backends::detail::create_normalization_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_normalization_layer(NormalizationLayerNode &node, GraphContext &ctx)</div><div class="ttdoc">Create a backend normalization layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00571">FunctionHelpers.h:571</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a05b19c75afe9c24200a62b9724734bbd"><div class="ttname"><a href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR(...)</div><div class="ttdoc">Print the given message then throw an std::runtime_error. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00261">Error.h:261</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab75d8ff29ba9b398d5740b3efd156e71"><div class="ttname"><a href="namespacearm__compute.xhtml#ab75d8ff29ba9b398d5740b3efd156e71">arm_compute::to_string</a></div><div class="ttdeci">std::string to_string(const arm_compute::GradientDimension &type)</div><div class="ttdoc">Formatted output of the GradientDimension type. </div><div class="ttdef"><b>Definition:</b> <a href="tests_2_type_printer_8h_source.xhtml#l00064">TypePrinter.h:64</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_concatenate_layer_node_xhtml_a37c261b90ba4503127a918cec483d859"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_concatenate_layer_node.xhtml#a37c261b90ba4503127a918cec483d859">arm_compute::graph::ConcatenateLayerNode::is_enabled</a></div><div class="ttdeci">bool is_enabled() const </div><div class="ttdoc">Enabled parameter accessor. </div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a966a9c417ce5e94dca08d9b5e745c0c9"><div class="ttname"><a href="namespacearm__compute.xhtml#a966a9c417ce5e94dca08d9b5e745c0c9">arm_compute::InterpolationPolicy</a></div><div class="ttdeci">InterpolationPolicy</div><div class="ttdoc">Interpolation method. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00386">Types.h:386</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_eltwise_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_eltwise_layer_node.xhtml">arm_compute::graph::EltwiseLayerNode</a></div><div class="ttdoc">Eltwise Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_eltwise_layer_node_8h_source.xhtml#l00034">EltwiseLayerNode.h:34</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ab146b9cbab6e73e7588b240dc709fe01"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ab146b9cbab6e73e7588b240dc709fe01">arm_compute::test::validation::beta</a></div><div class="ttdeci">beta</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00087">GEMM.cpp:87</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_xhtml_aa12973876c037bddff8e9ece94aca0e4"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#aa12973876c037bddff8e9ece94aca0e4">arm_compute::graph::EltwiseOperation</a></div><div class="ttdeci">EltwiseOperation</div><div class="ttdoc">Supported Element-wise operations. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00097">Types.h:97</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a683661ae75dcb7aef16b9c9bde31517dafd1dd0c603be8170f9eae0be9f2f6afb"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a683661ae75dcb7aef16b9c9bde31517dafd1dd0c603be8170f9eae0be9f2f6afb">arm_compute::graph::ConvolutionMethod::Direct</a></div><div class="ttdoc">Deep direct convolution. </div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_batch_normalization_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_batch_normalization_layer_node.xhtml">arm_compute::graph::BatchNormalizationLayerNode</a></div><div class="ttdoc">Batch Normalization Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_layer_node_8h_source.xhtml#l00034">BatchNormalizationLayerNode.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_normalization_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_normalization_layer_node.xhtml">arm_compute::graph::NormalizationLayerNode</a></div><div class="ttdoc">Normalization Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_normalization_layer_node_8h_source.xhtml#l00034">NormalizationLayerNode.h:34</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_aa37faf92f78c0f5cefe2d43c8bf07f18"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aa37faf92f78c0f5cefe2d43c8bf07f18">arm_compute::graph::backends::detail::create_eltwise_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_eltwise_layer(EltwiseLayerNode &node)</div><div class="ttdoc">Create a backend element-wise operation layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00430">FunctionHelpers.h:430</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a5d1a73ab4a0b267033a569c46813b9d5"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a5d1a73ab4a0b267033a569c46813b9d5">arm_compute::graph::backends::detail::create_batch_normalization_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_batch_normalization_layer(BatchNormalizationLayerNode &node)</div><div class="ttdoc">Create a backend batch normalization layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00131">FunctionHelpers.h:131</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02">arm_compute::DataLayoutDimension</a></div><div class="ttdeci">DataLayoutDimension</div><div class="ttdoc">Supported tensor data layout dimensions. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00115">Types.h:115</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_ab8ff2a40f95b76ec10ac2a98d1a8d594"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ab8ff2a40f95b76ec10ac2a98d1a8d594">arm_compute::graph::backends::detail::create_depthwise_convolution_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_depthwise_convolution_layer(DepthwiseConvolutionLayerNode &node)</div><div class="ttdoc">Create a backend layer depth-wise convolution function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00373">FunctionHelpers.h:373</a></div></div> |
| <div class="ttc" id="arm__compute_2graph_2_types_8h_xhtml"><div class="ttname"><a href="arm__compute_2graph_2_types_8h.xhtml">Types.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_adb3a9be16de941b0f601e16c8ac76533"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#adb3a9be16de941b0f601e16c8ac76533">arm_compute::graph::backends::detail::create_resize_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_resize_layer(ResizeLayerNode &node)</div><div class="ttdoc">Create a backend resize layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00719">FunctionHelpers.h:719</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_normalization_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_normalization_layer_info.xhtml">arm_compute::NormalizationLayerInfo</a></div><div class="ttdoc">Normalization Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00899">Types.h:899</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_flatten_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_flatten_layer_node.xhtml">arm_compute::graph::FlattenLayerNode</a></div><div class="ttdoc">Flatten Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_flatten_layer_node_8h_source.xhtml#l00034">FlattenLayerNode.h:34</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a33e65be485104e2e9e69fca551d6f492"><div class="ttname"><a href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">arm_compute::PermutationVector</a></div><div class="ttdeci">Strides PermutationVector</div><div class="ttdoc">Permutation vector. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00047">Types.h:47</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_aa65a1becdfa5fc3533d79bba0cd4095c"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aa65a1becdfa5fc3533d79bba0cd4095c">arm_compute::graph::backends::detail::create_permute_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_permute_layer(PermuteLayerNode &node)</div><div class="ttdoc">Create a backend permute layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00610">FunctionHelpers.h:610</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a63e05ce4946dd9807c005c1619fa337a"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a63e05ce4946dd9807c005c1619fa337a">arm_compute::ActivationLayerInfo::activation</a></div><div class="ttdeci">ActivationFunction activation() const </div><div class="ttdoc">Get the type of activation function. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00871">Types.h:871</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a5d58c32bff63e4c34b3234f884a4da58"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a5d58c32bff63e4c34b3234f884a4da58">arm_compute::test::validation::convert_policy</a></div><div class="ttdeci">convert_policy</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_pixel_wise_multiplication_8cpp_source.xhtml#l00168">PixelWiseMultiplication.cpp:168</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00337">Error.h:337</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_fully_connected_layer_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_fully_connected_layer_info.xhtml">arm_compute::FullyConnectedLayerInfo</a></div><div class="ttdoc">Fully connected layer info. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00686">Types.h:686</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_xhtml_ac85a46f3ebd3ab09f576a994ac2dce11a00d23a76e43b46dae9ec7aa9dcbebb32"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#ac85a46f3ebd3ab09f576a994ac2dce11a00d23a76e43b46dae9ec7aa9dcbebb32">arm_compute::graph::FastMathHint::Enabled</a></div><div class="ttdoc">Fast math enabled for Convolution layer. </div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a7880c3b249a6dad40da0ebcf6600b0e1"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a7880c3b249a6dad40da0ebcf6600b0e1">arm_compute::graph::backends::detail::create_flatten_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_flatten_layer(FlattenLayerNode &node)</div><div class="ttdoc">Create a backend flatten layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00490">FunctionHelpers.h:490</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_graph_context_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_graph_context.xhtml">arm_compute::graph::GraphContext</a></div><div class="ttdoc">Graph context. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_context_8h_source.xhtml#l00048">GraphContext.h:48</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml">arm_compute::ActivationLayerInfo</a></div><div class="ttdoc">Activation Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00839">Types.h:839</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_permute_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_permute_layer_node.xhtml">arm_compute::graph::PermuteLayerNode</a></div><div class="ttdoc">Permute Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_permute_layer_node_8h_source.xhtml#l00034">PermuteLayerNode.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_pooling_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_pooling_layer_node.xhtml">arm_compute::graph::PoolingLayerNode</a></div><div class="ttdoc">Pooling Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_pooling_layer_node_8h_source.xhtml#l00034">PoolingLayerNode.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml_a7db9a1c221b414ff11bd4a5b7b97ec8d"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#a7db9a1c221b414ff11bd4a5b7b97ec8d">arm_compute::graph::INode::num_inputs</a></div><div class="ttdeci">size_t num_inputs() const </div><div class="ttdoc">Returns number of inputs of the node. </div></div> |
| <div class="ttc" id="graph_2_logger_8h_xhtml_ab2d8baa35618bdad1d2814942355311e"><div class="ttname"><a href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a></div><div class="ttdeci">#define ARM_COMPUTE_LOG_GRAPH_INFO(x)</div><div class="ttdef"><b>Definition:</b> <a href="graph_2_logger_8h_source.xhtml#l00050">Logger.h:50</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">This file contains all available output stages for GEMMLowp on OpenCL. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00001">00_introduction.dox:1</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_size2_d_xhtml_afe5ea0266a8c16e45fe5f5d42235f3f5"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml#afe5ea0266a8c16e45fe5f5d42235f3f5">arm_compute::Size2D::y</a></div><div class="ttdeci">size_t y() const </div><div class="ttdoc">Semantic accessor for height as y. </div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00086">Size2D.h:86</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_xhtml_aa12973876c037bddff8e9ece94aca0e4ae80155eceb940c89e2de63ad05868db2"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#aa12973876c037bddff8e9ece94aca0e4ae80155eceb940c89e2de63ad05868db2">arm_compute::graph::EltwiseOperation::Sub</a></div><div class="ttdoc">Arithmetic subtraction. </div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_tensor_xhtml_a1a4fd35b8e2e8a2c7ea38b6d37508673"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_tensor.xhtml#a1a4fd35b8e2e8a2c7ea38b6d37508673">arm_compute::graph::Tensor::handle</a></div><div class="ttdeci">ITensorHandle * handle()</div><div class="ttdoc">Backend tensor handle accessor. </div></div> |
| <div class="ttc" id="_cast_8h_xhtml"><div class="ttname"><a href="_cast_8h.xhtml">Cast.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_weights_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_weights_info.xhtml">arm_compute::WeightsInfo</a></div><div class="ttdoc">Convolution Layer Weights Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00974">Types.h:974</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a5271c97b6bef5972c5e259307d52a4da"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a5271c97b6bef5972c5e259307d52a4da">arm_compute::graph::backends::detail::create_softmax_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_softmax_layer(SoftmaxLayerNode &node, GraphContext &ctx)</div><div class="ttdoc">Create a backend softmax layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00757">FunctionHelpers.h:757</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::Format::S32</a></div><div class="ttdoc">1 channel, 1 S32 per channel </div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_convolution_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_convolution_layer_node.xhtml">arm_compute::graph::ConvolutionLayerNode</a></div><div class="ttdoc">Convolution Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_convolution_layer_node_8h_source.xhtml#l00034">ConvolutionLayerNode.h:34</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a683661ae75dcb7aef16b9c9bde31517da5174aac3927faa9ee34befb7fc87a9e3"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a683661ae75dcb7aef16b9c9bde31517da5174aac3927faa9ee34befb7fc87a9e3">arm_compute::graph::ConvolutionMethod::GEMM</a></div><div class="ttdoc">GEMM based convolution. </div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml">arm_compute::graph::INode</a></div><div class="ttdoc">Node interface. </div><div class="ttdef"><b>Definition:</b> <a href="_i_node_8h_source.xhtml#l00044">INode.h:44</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a9e38014fa1e7e08dcbf3b5f8c3bdb81e"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a9e38014fa1e7e08dcbf3b5f8c3bdb81e">arm_compute::graph::backends::detail::create_convolution_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_convolution_layer(ConvolutionLayerNode &node, GraphContext &ctx)</div><div class="ttdoc">Create a backend convolution layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00252">FunctionHelpers.h:252</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml_a72123a16315dde55dade52690642f56c"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#a72123a16315dde55dade52690642f56c">arm_compute::graph::INode::assigned_target</a></div><div class="ttdeci">Target assigned_target() const </div><div class="ttdoc">Returns assigned target for this node. </div></div> |
| <div class="ttc" id="_error_8h_xhtml_a6dc630a6ae9cc063b3924bcea8dee9d6"><div class="ttname"><a href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(...)</div><div class="ttdoc">To avoid unused variables warnings. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00160">Error.h:160</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">arm_compute::Channel::U</a></div><div class="ttdoc">Cb/U channel. </div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml_a37627d5d5bba7f4a8690c71c2ab3cb07"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#a37627d5d5bba7f4a8690c71c2ab3cb07">arm_compute::graph::INode::name</a></div><div class="ttdeci">std::string name() const </div><div class="ttdoc">Returns node&#39;s name. </div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a75ce9b1baad4303a53124d6f0795821f"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a75ce9b1baad4303a53124d6f0795821f">arm_compute::graph::DepthwiseConvolutionMethod</a></div><div class="ttdeci">DepthwiseConvolutionMethod</div><div class="ttdoc">Supported Depthwise Convolution layer methods. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00114">Types.h:114</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a1d79980f25d38aba3d6777d0afe544f3"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a1d79980f25d38aba3d6777d0afe544f3">arm_compute::ActivationLayerInfo::enabled</a></div><div class="ttdeci">bool enabled() const </div><div class="ttdoc">Check if initialised. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00886">Types.h:886</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_aa6fabefcb8c4bd308219565ddcf00928"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aa6fabefcb8c4bd308219565ddcf00928">arm_compute::graph::backends::detail::validate_node</a></div><div class="ttdeci">void validate_node(const INode &node, size_t num_expected_inputs, size_t num_expected_outputs)</div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00072">FunctionHelpers.h:72</a></div></div> |
| <div class="ttc" id="graph_2_tensor_8h_xhtml"><div class="ttname"><a href="graph_2_tensor_8h.xhtml">Tensor.h</a></div></div> |
| <div class="ttc" id="_i_tensor_info_8h_xhtml"><div class="ttname"><a href="_i_tensor_info_8h.xhtml">ITensorInfo.h</a></div></div> |
| <div class="ttc" id="graph_2_logger_8h_xhtml"><div class="ttname"><a href="graph_2_logger_8h.xhtml">Logger.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_channel_shuffle_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_channel_shuffle_layer_node.xhtml">arm_compute::graph::ChannelShuffleLayerNode</a></div><div class="ttdoc">Channel Shuffle Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_channel_shuffle_layer_node_8h_source.xhtml#l00034">ChannelShuffleLayerNode.h:34</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_xhtml_aa12973876c037bddff8e9ece94aca0e4aec211f7c20af43e742bf2570c3cb84f9"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#aa12973876c037bddff8e9ece94aca0e4aec211f7c20af43e742bf2570c3cb84f9">arm_compute::graph::EltwiseOperation::Add</a></div><div class="ttdoc">Arithmetic addition. </div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_reshape_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_reshape_layer_node.xhtml">arm_compute::graph::ReshapeLayerNode</a></div><div class="ttdoc">Reshape Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_reshape_layer_node_8h_source.xhtml#l00034">ReshapeLayerNode.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00569">Types.h:569</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_concatenate_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_concatenate_layer_node.xhtml">arm_compute::graph::ConcatenateLayerNode</a></div><div class="ttdoc">Concatenation Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_concatenate_layer_node_8h_source.xhtml#l00034">ConcatenateLayerNode.h:34</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml"><div class="ttname"><a href="_error_8h.xhtml">Error.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_softmax_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_softmax_layer_node.xhtml">arm_compute::graph::SoftmaxLayerNode</a></div><div class="ttdoc">Softmax Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_softmax_layer_node_8h_source.xhtml#l00034">SoftmaxLayerNode.h:34</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acbf8f8a6dd185de04c1981c57a8963cf"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">arm_compute::test::validation::conv_info</a></div><div class="ttdeci">conv_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00572">Winograd.cpp:572</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_i_tensor_handle_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_tensor_handle.xhtml">arm_compute::graph::ITensorHandle</a></div><div class="ttdoc">Tensor handle interface object. </div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_handle_8h_source.xhtml#l00038">ITensorHandle.h:38</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_ac77fa3bf0d7d7c3fde6243192f93f380"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ac77fa3bf0d7d7c3fde6243192f93f380">arm_compute::graph::backends::detail::create_deconvolution_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_deconvolution_layer(DeconvolutionLayerNode &node, GraphContext &ctx)</div><div class="ttdoc">Create a backend deconvolution layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00331">FunctionHelpers.h:331</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_tensor_xhtml_ace1b6c0005a4c373e54bcfdc6d5b7d68"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_tensor.xhtml#ace1b6c0005a4c373e54bcfdc6d5b7d68">arm_compute::graph::Tensor::desc</a></div><div class="ttdeci">TensorDescriptor & desc()</div><div class="ttdoc">TensorInfo metadata accessor. </div></div> |
| <div class="ttc" id="structarm__compute_1_1graph_1_1_tensor_descriptor_xhtml_a2a7ca82c5e74421cb45f17e936abf964"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a2a7ca82c5e74421cb45f17e936abf964">arm_compute::graph::TensorDescriptor::target</a></div><div class="ttdeci">Target target</div><div class="ttdoc">Target. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_descriptor_8h_source.xhtml#l00113">TensorDescriptor.h:113</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_concatenate_layer_node_xhtml_ac3ff7bc54b572572d6cbc4141b9f5db6"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_concatenate_layer_node.xhtml#ac3ff7bc54b572572d6cbc4141b9f5db6">arm_compute::graph::ConcatenateLayerNode::concatenation_axis</a></div><div class="ttdeci">DataLayoutDimension concatenation_axis() const </div><div class="ttdoc">Concatenation axis parameter accessor. </div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a14f46283f316e7f0fad301d5c1507e9f"><div class="ttname"><a href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a></div><div class="ttdeci">bool is_data_type_quantized_asymmetric(DataType dt)</div><div class="ttdoc">Check if a given data type is of asymmetric quantized type. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l00992">Utils.h:992</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_xhtml_a4403f766b0d02eb3882a9521d0390986"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends.xhtml#a4403f766b0d02eb3882a9521d0390986">arm_compute::graph::backends::is_in_place_operation</a></div><div class="ttdeci">bool is_in_place_operation(void *input, void *output)</div><div class="ttdoc">Checks if an operation is in place. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2backends_2_utils_8h_source.xhtml#l00076">Utils.h:76</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml_ac2073f3ae2a49f98a91315ed035f8669"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#ac2073f3ae2a49f98a91315ed035f8669">arm_compute::graph::INode::id</a></div><div class="ttdeci">NodeID id() const </div><div class="ttdoc">Returns node&#39;s ID. </div></div> |
| <div class="ttc" id="arm__compute_2graph_2_type_printer_8h_xhtml"><div class="ttname"><a href="arm__compute_2graph_2_type_printer_8h.xhtml">TypePrinter.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_activation_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_activation_layer_node.xhtml">arm_compute::graph::ActivationLayerNode</a></div><div class="ttdoc">Activation Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_activation_layer_node_8h_source.xhtml#l00034">ActivationLayerNode.h:34</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_ac64bbd0df74207f9ab59953e21311178"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ac64bbd0df74207f9ab59953e21311178">arm_compute::graph::backends::detail::create_fully_connected_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_fully_connected_layer(FullyConnectedLayerNode &node, GraphContext &ctx)</div><div class="ttdoc">Create a backend fully connected layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00527">FunctionHelpers.h:527</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a0f5afb0ddd5aec3a8e4df3c56d7d91f4"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a0f5afb0ddd5aec3a8e4df3c56d7d91f4">arm_compute::graph::backends::detail::create_activation_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_activation_layer(ActivationLayerNode &node)</div><div class="ttdoc">Creates a backend activation layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00095">FunctionHelpers.h:95</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_size2_d_xhtml"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml">arm_compute::Size2D</a></div><div class="ttdoc">Class for specifying the size of an image or rectangle. </div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00034">Size2D.h:34</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_xhtml_a8919c520c1cb9086dd1116de509bd481"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends.xhtml#a8919c520c1cb9086dd1116de509bd481">arm_compute::graph::backends::get_memory_manager</a></div><div class="ttdeci">std::shared_ptr< IMemoryManager > get_memory_manager(GraphContext &ctx, Target target)</div><div class="ttdoc">Returns the memory manager for a given target. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2backends_2_utils_8h_source.xhtml#l00088">Utils.h:88</a></div></div> |
| <div class="ttc" id="graph_2_logger_8h_xhtml_a300d153929a99c7b571d4cda3f7987a5"><div class="ttname"><a href="graph_2_logger_8h.xhtml#a300d153929a99c7b571d4cda3f7987a5">ARM_COMPUTE_LOG_GRAPH_VERBOSE</a></div><div class="ttdeci">#define ARM_COMPUTE_LOG_GRAPH_VERBOSE(x)</div><div class="ttdef"><b>Definition:</b> <a href="graph_2_logger_8h_source.xhtml#l00046">Logger.h:46</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a683661ae75dcb7aef16b9c9bde31517d"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a683661ae75dcb7aef16b9c9bde31517d">arm_compute::graph::ConvolutionMethod</a></div><div class="ttdeci">ConvolutionMethod</div><div class="ttdoc">Supported Convolution layer methods. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00105">Types.h:105</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a15a05537a472ee742404821851529327a8d6b5cada83510220f59e00ce86d4d92"><div class="ttname"><a href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a8d6b5cada83510220f59e00ce86d4d92">arm_compute::BorderMode::CONSTANT</a></div><div class="ttdoc">Pixels outside the image are assumed to have a constant value. </div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml_a427ac30d0f5274436afbf5c78bc4f644"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#a427ac30d0f5274436afbf5c78bc4f644">arm_compute::graph::INode::output</a></div><div class="ttdeci">Tensor * output(size_t idx) const </div><div class="ttdoc">Returns the tensor of a given output of the node. </div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a69dd1fc17c7a15f4125873be182c8c76"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a69dd1fc17c7a15f4125873be182c8c76">arm_compute::graph::backends::detail::get_backing_tensor</a></div><div class="ttdeci">TargetInfo::TensorType * get_backing_tensor(arm_compute::graph::Tensor *tensor)</div><div class="ttdoc">Returns backing tensor of a given tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00056">FunctionHelpers.h:56</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_deconvolution_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_deconvolution_layer_node.xhtml">arm_compute::graph::DeconvolutionLayerNode</a></div><div class="ttdoc">Deconvolution Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_deconvolution_layer_node_8h_source.xhtml#l00034">DeconvolutionLayerNode.h:34</a></div></div> |
| <div class="ttc" id="arm__compute_2core_2_helpers_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_helpers_8h.xhtml">Helpers.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_fully_connected_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fully_connected_layer_node.xhtml">arm_compute::graph::FullyConnectedLayerNode</a></div><div class="ttdoc">Fully Connected Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_fully_connected_layer_node_8h_source.xhtml#l00034">FullyConnectedLayerNode.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_depthwise_convolution_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_depthwise_convolution_layer_node.xhtml">arm_compute::graph::DepthwiseConvolutionLayerNode</a></div><div class="ttdoc">Depthwise Convolution Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_depthwise_convolution_layer_node_8h_source.xhtml#l00034">DepthwiseConvolutionLayerNode.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml_ac6dfcf4c1c7d4cb129fda6393e8c0b21"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#ac6dfcf4c1c7d4cb129fda6393e8c0b21">arm_compute::graph::INode::input</a></div><div class="ttdeci">Tensor * input(size_t idx) const </div><div class="ttdoc">Returns the tensor of a given input of the node. </div></div> |
| <div class="ttc" id="_nodes_8h_xhtml"><div class="ttname"><a href="_nodes_8h.xhtml">Nodes.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a683661ae75dcb7aef16b9c9bde31517da09db1dd1078ec6bdbe2722b4558e578f"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a683661ae75dcb7aef16b9c9bde31517da09db1dd1078ec6bdbe2722b4558e578f">arm_compute::graph::ConvolutionMethod::Winograd</a></div><div class="ttdoc">Winograd based convolution. </div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_resize_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_resize_layer_node.xhtml">arm_compute::graph::ResizeLayerNode</a></div><div class="ttdoc">Resize Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_resize_layer_node_8h_source.xhtml#l00034">ResizeLayerNode.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml_a6b2d83e561886647467f86c20ce39bec"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#a6b2d83e561886647467f86c20ce39bec">arm_compute::graph::INode::type</a></div><div class="ttdeci">virtual NodeType type() const =0</div><div class="ttdoc">Returns node&#39;s type. </div></div> |
| <div class="ttc" id="arm__compute_2graph_2backends_2_utils_8h_xhtml"><div class="ttname"><a href="arm__compute_2graph_2backends_2_utils_8h.xhtml">Utils.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_xhtml_aa12973876c037bddff8e9ece94aca0e4a62b6d55816cf737bfc6f42e60df1a3f2"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#aa12973876c037bddff8e9ece94aca0e4a62b6d55816cf737bfc6f42e60df1a3f2">arm_compute::graph::EltwiseOperation::Mul</a></div><div class="ttdoc">Arithmetic multiplication. </div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a75ce9b1baad4303a53124d6f0795821fa3bb7b7f3f021a006e65111fc1d226938"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a75ce9b1baad4303a53124d6f0795821fa3bb7b7f3f021a006e65111fc1d226938">arm_compute::graph::DepthwiseConvolutionMethod::Optimized3x3</a></div><div class="ttdoc">Optimized 3x3 direct depthwise convolution. </div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_concatenate_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_concatenate_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::ConcatenateLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node&#39;s type. </div></div> |
| <div class="ttc" id="classarm__compute_1_1_size2_d_xhtml_aa19402aa7cd5346df67c0142c75d36c0"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml#aa19402aa7cd5346df67c0142c75d36c0">arm_compute::Size2D::x</a></div><div class="ttdeci">size_t x() const </div><div class="ttdoc">Semantic accessor for width as x. </div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00077">Size2D.h:77</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_pooling_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pooling_layer_info.xhtml">arm_compute::PoolingLayerInfo</a></div><div class="ttdoc">Pooling Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00718">Types.h:718</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a0e51b62035e79b0f12964cae17ce0480"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a0e51b62035e79b0f12964cae17ce0480">arm_compute::graph::backends::detail::create_pooling_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_pooling_layer(PoolingLayerNode &node)</div><div class="ttdoc">Create a backend pooling layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00647">FunctionHelpers.h:647</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a627f6bdc4a7de6dbb03acb3d8b3a4d6d"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a627f6bdc4a7de6dbb03acb3d8b3a4d6d">arm_compute::graph::backends::detail::create_concatenate_layer</a></div><div class="ttdeci">std::unique_ptr< arm_compute::IFunction > create_concatenate_layer(ConcatenateLayerNode &node)</div><div class="ttdoc">Create a backend layer concatenate function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00205">FunctionHelpers.h:205</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a82b8ac759c804bc1fb4e2d21e178fb6f"><div class="ttname"><a href="namespacearm__compute.xhtml#a82b8ac759c804bc1fb4e2d21e178fb6f">arm_compute::ConvertPolicy</a></div><div class="ttdeci">ConvertPolicy</div><div class="ttdoc">Policy to handle overflow. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00379">Types.h:379</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_ad1922deea021647290d0c206723e6c73"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ad1922deea021647290d0c206723e6c73">arm_compute::graph::backends::detail::create_reshape_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_reshape_layer(ReshapeLayerNode &node)</div><div class="ttdoc">Create a backend reshape layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00684">FunctionHelpers.h:684</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml_a20ee33c4a581d8d3507dbb898d47d733"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#a20ee33c4a581d8d3507dbb898d47d733">arm_compute::graph::INode::num_outputs</a></div><div class="ttdeci">size_t num_outputs() const </div><div class="ttdoc">Returns number of outputs of the node. </div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_tensor.xhtml">arm_compute::graph::Tensor</a></div><div class="ttdoc">Tensor object. </div><div class="ttdef"><b>Definition:</b> <a href="graph_2_tensor_8h_source.xhtml#l00041">Tensor.h:41</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a3f5c14020836599056281fe52d7e9dd3"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a3f5c14020836599056281fe52d7e9dd3">arm_compute::graph::backends::detail::create_channel_shuffle_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_channel_shuffle_layer(ChannelShuffleLayerNode &node)</div><div class="ttdoc">Create a backend channel shuffle layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00172">FunctionHelpers.h:172</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond,...)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00328">Error.h:328</a></div></div> |
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