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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#a0d3608f94078b90ab1ff9e9465d4ed3b">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#a45c1ef0023ce430d009ec79c97761544">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#a5ea90a25ef8059df2d2e51b82991ebf3">id</a>()</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">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#aa26cd423e8fc9233bd2bff46f82f46ab">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#abe0cb3e4411a1c289e3e40e3f9d79fec">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#a75d7c8bdf0059bb235880f8ef8d190bc">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></div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  << node.name()</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  << <span class="stringliteral">" Shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  << <span class="stringliteral">" Activation function: "</span> << act_info.activation()</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  << <span class="stringliteral">" a: "</span> << act_info.a()</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  << <span class="stringliteral">" b: "</span> << act_info.b()</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</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="l00118"></a><span class="lineno"> 118</span>  << std::endl);</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>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> }</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> </div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> <span class="keyword">template</span> <<span class="keyword">typename</span> BatchNormalizationLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00133"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a5d1a73ab4a0b267033a569c46813b9d5"> 133</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="l00134"></a><span class="lineno"> 134</span> {</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</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="l00136"></a><span class="lineno"> 136</span> </div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keyword">typename</span> TargetInfo::TensorType *mean = get_backing_tensor<TargetInfo>(node.input(1));</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keyword">typename</span> TargetInfo::TensorType *var = get_backing_tensor<TargetInfo>(node.input(2));</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</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="l00142"></a><span class="lineno"> 142</span>  <span class="keyword">typename</span> TargetInfo::TensorType *gamma = get_backing_tensor<TargetInfo>(node.input(4));</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> <a class="code" href="_asymm_helpers_8cpp.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a> = node.epsilon();</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</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="l00146"></a><span class="lineno"> 146</span> </div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<BatchNormalizationLayerFunction>();</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  func->configure(input, output, mean, var, beta, gamma, epsilon, fused_act);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> </div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  << node.name()</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  << <span class="stringliteral">" Shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  << <span class="stringliteral">" Epsilon: "</span> << epsilon << <span class="stringliteral">" "</span></div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  << (fused_act.<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#af5a8385102f8f8dd6c5957eac08b04c2">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#a9e0fb1d1462557f28966ae19988532c2">activation</a>()) : <span class="stringliteral">""</span>)</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</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="l00161"></a><span class="lineno"> 161</span>  << std::endl);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> </div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> }</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> </div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> <span class="keyword">template</span> <<span class="keyword">typename</span> BoundingBoxTransformLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00176"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ab3787ac008a709edda8d347370bbad61"> 176</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ab3787ac008a709edda8d347370bbad61">create_bounding_box_transform_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_bounding_box_transform_layer_node.xhtml">BoundingBoxTransformLayerNode</a> &node)</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> {</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  validate_node<TargetInfo>(node, 2 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> </div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keyword">typename</span> TargetInfo::TensorType *deltas = get_backing_tensor<TargetInfo>(node.input(1));</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_bounding_box_transform_info.xhtml">BoundingBoxTransformInfo</a> bbox_info = node.info();</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<BoundingBoxTransformLayerFunction>();</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  func->configure(input, output, deltas, bbox_info);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> </div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00191"></a><span class="lineno"> 191</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="l00192"></a><span class="lineno"> 192</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  << <span class="stringliteral">" Shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  << <span class="stringliteral">" BoundingBox Info img W: "</span> << bbox_info.img_width() << <span class="stringliteral">" "</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  << <span class="stringliteral">" BoundingBox Info img H: "</span> << bbox_info.img_height() << <span class="stringliteral">" "</span></div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  << std::endl);</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> </div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> }</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> </div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ChannelShuffleLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00212"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a3f5c14020836599056281fe52d7e9dd3"> 212</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="l00213"></a><span class="lineno"> 213</span> {</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</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="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>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</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="l00220"></a><span class="lineno"> 220</span> </div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="comment">// Create function</span></div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ChannelShuffleLayerFunction>();</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  func->configure(input, output, num_groups);</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>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  << node.name()</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  << <span class="stringliteral">" Shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  << <span class="stringliteral">" Num groups: "</span> << num_groups</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  << std::endl);</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> </div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> }</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> </div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ConcatenateLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00247"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a627f6bdc4a7de6dbb03acb3d8b3a4d6d"> 247</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="l00248"></a><span class="lineno"> 248</span> {</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</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#a5ea90a25ef8059df2d2e51b82991ebf3">id</a>() << <span class="stringliteral">" and Name: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>() << std::endl);</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</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#a75d7c8bdf0059bb235880f8ef8d190bc">num_outputs</a>() != 1);</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> </div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="comment">// Return nullptr if depth concatenate is switched off</span></div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keywordflow">if</span>(!node.<a class="code" href="classarm__compute_1_1graph_1_1_concatenate_layer_node.xhtml#a6507f40ddf408e1f124cb84fa5cbfd1e">is_enabled</a>())</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  {</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  }</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> </div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  std::vector<typename TargetInfo::TensorType *> inputs;</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</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#abe0cb3e4411a1c289e3e40e3f9d79fec">num_inputs</a>(); ++i)</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>  inputs.push_back(get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(i)));</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>  <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#a27369471d9b2ba47746e1e923f585b9f">output</a>(0));</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</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#a051cd2a8d15cf783e9ab9a00451c77f6">concatenation_axis</a>();</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="comment">// Create and configure function</span></div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ConcatenateLayerFunction>();</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  func->configure(inputs, output, concat_axis);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> </div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  << <span class="stringliteral">" Type: "</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="l00275"></a><span class="lineno"> 275</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  << <span class="stringliteral">" Data Type: "</span> << output->info()->data_type()</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  << <span class="stringliteral">" Shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  << <span class="stringliteral">" Num Inputs: "</span> << inputs.size()</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  << <span class="stringliteral">" Axis: "</span> << concat_axis</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  << std::endl);</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> </div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <span class="keywordflow">return</span> std::move(func);</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> </div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ConvolutionLayerFunctions, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00296"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a9e38014fa1e7e08dcbf3b5f8c3bdb81e"> 296</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="l00297"></a><span class="lineno"> 297</span> {</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</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="l00299"></a><span class="lineno"> 299</span> </div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="keyword">typename</span> TargetInfo::TensorType *weights = get_backing_tensor<TargetInfo>(node.input(1));</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keyword">typename</span> TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.input(2));</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</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="keyword">const</span> <span class="keywordtype">bool</span> is_quantized = <a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</a>(input->info()->data_type());</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span> </div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keywordflow">if</span>(is_quantized)</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  {</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  biases->info()->set_data_type(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  }</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span> </div><div class="line"><a name="l00313"></a><span class="lineno"> 313</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="l00314"></a><span class="lineno"> 314</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="l00315"></a><span class="lineno"> 315</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="l00316"></a><span class="lineno"> 316</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="l00317"></a><span class="lineno"> 317</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="l00318"></a><span class="lineno"> 318</span> </div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="comment">// Create and configure function (we assume that functions have been validated before creation)</span></div><div class="line"><a name="l00320"></a><span class="lineno"> 320</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="l00321"></a><span class="lineno"> 321</span>  std::unique_ptr<IFunction> func;</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  std::string func_name;</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> </div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <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="l00325"></a><span class="lineno"> 325</span>  {</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</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="l00327"></a><span class="lineno"> 327</span>  std::tie(func, func_name) = create_named_memory_managed_function<typename ConvolutionLayerFunctions::WinogradConvolutionLayer>(</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  std::string(<span class="stringliteral">"WinogradConvolutionLayer"</span>), mm,</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  input, weights, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>, fused_act, fast_math);</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  }</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <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="l00332"></a><span class="lineno"> 332</span>  {</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</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="l00334"></a><span class="lineno"> 334</span>  std::tie(func, func_name) = create_named_function<typename ConvolutionLayerFunctions::DirectConvolutionLayer>(</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  std::string(<span class="stringliteral">"DirectConvolutionLayer"</span>),</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  input, weights, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>, fused_act);</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  }</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <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="l00339"></a><span class="lineno"> 339</span>  {</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  std::tie(func, func_name) = create_named_memory_managed_function<typename ConvolutionLayerFunctions::GEMMConvolutionLayer>(</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  std::string(<span class="stringliteral">"GEMMConvolutionLayer"</span>), mm,</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</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="l00343"></a><span class="lineno"> 343</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>), fused_act, num_groups);</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  }</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  {</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  std::tie(func, func_name) = create_named_memory_managed_function<typename ConvolutionLayerFunctions::GenericConvolutionLayer>(</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  std::string(<span class="stringliteral">"GenericConvolutionLayer"</span>), mm,</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</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="l00350"></a><span class="lineno"> 350</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>), fused_act, fast_math, num_groups);</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> </div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  std::ostringstream qss;</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keywordflow">if</span>(is_quantized)</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  {</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  qss << <span class="stringliteral">" Input QuantInfo: "</span> << input->info()->quantization_info()</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  << <span class="stringliteral">" Weights QuantInfo: "</span> << weights->info()->quantization_info()</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  << <span class="stringliteral">" Output QuantInfo: "</span> << output->info()->quantization_info();</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  }</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  << node.name()</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  << <span class="stringliteral">" Type: "</span> << func_name</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  << <span class="stringliteral">" Groups: "</span> << num_groups</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  << qss.str()</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  << <span class="stringliteral">" Weights shape: "</span> << weights->info()->tensor_shape()</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  << (fused_act.<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#af5a8385102f8f8dd6c5957eac08b04c2">enabled</a>() ? <span class="stringliteral">" "</span> + <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#a9e0fb1d1462557f28966ae19988532c2">activation</a>()) : <span class="stringliteral">""</span>)</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  << std::endl);</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="keywordflow">return</span> func;</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> </div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span> <span class="keyword">template</span> <<span class="keyword">typename</span> DeconvolutionLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00387"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ac77fa3bf0d7d7c3fde6243192f93f380"> 387</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="l00388"></a><span class="lineno"> 388</span> {</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</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="l00390"></a><span class="lineno"> 390</span> </div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="keyword">typename</span> TargetInfo::TensorType *weights = get_backing_tensor<TargetInfo>(node.input(1));</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keyword">typename</span> TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.input(2));</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span> </div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="keyword">const</span> <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="l00398"></a><span class="lineno"> 398</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="l00399"></a><span class="lineno"> 399</span> </div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="comment">// Create and configure function (we assume that functions have been validated before creation)</span></div><div class="line"><a name="l00401"></a><span class="lineno"> 401</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="l00402"></a><span class="lineno"> 402</span>  std::unique_ptr<IFunction> func;</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span> </div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  std::tie(func, std::ignore) = create_named_memory_managed_function<DeconvolutionLayerFunction>(</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  std::string(), mm,</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  input, weights, biases, output, deconv_info, inner_border.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a94b8468af876f5ab54020d5e9787a4f0">x</a>(), inner_border.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#aaaeb4853150b7d0e8b685fd08052924f">y</a>());</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span> </div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  << node.name()</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  << <span class="stringliteral">" Weights shape: "</span> << weights->info()->tensor_shape()</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  << std::endl);</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="keywordflow">return</span> func;</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> }</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span> </div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span> <span class="keyword">template</span> <<span class="keyword">typename</span> DepthwiseConvolutionLayerFunctions, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00431"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ab8ff2a40f95b76ec10ac2a98d1a8d594"> 431</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="l00432"></a><span class="lineno"> 432</span> {</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</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="l00434"></a><span class="lineno"> 434</span> </div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <span class="keyword">typename</span> TargetInfo::TensorType *weights = get_backing_tensor<TargetInfo>(node.input(1));</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  <span class="keyword">typename</span> TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.input(2));</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span> </div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_quantized = <a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</a>(input->info()->data_type());</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span> </div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="keywordflow">if</span>(is_quantized)</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  {</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  biases->info()->set_data_type(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  }</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span> </div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <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="l00449"></a><span class="lineno"> 449</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="l00450"></a><span class="lineno"> 450</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth_multiplier = 1;</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</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="l00452"></a><span class="lineno"> 452</span> </div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <span class="comment">// Create and configure function (we assume that functions have been validated before creation)</span></div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  std::unique_ptr<IFunction> func;</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  std::string func_name;</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</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="l00457"></a><span class="lineno"> 457</span>  {</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  std::tie(func, func_name) = create_named_function<typename DepthwiseConvolutionLayerFunctions::DepthwiseConvolutionLayer3x3>(</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  std::string(<span class="stringliteral">"DepthwiseConvolutionLayer3x3"</span>),</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  input, weights, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>, depth_multiplier, fused_act);</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  }</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keywordflow">else</span></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>  std::tie(func, func_name) = create_named_function<typename DepthwiseConvolutionLayerFunctions::GenericDepthwiseConvolutionLayer>(</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  std::string(<span class="stringliteral">"DepthwiseConvolutionLayer"</span>),</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  input, weights, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>, depth_multiplier, fused_act);</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>  std::ostringstream qss;</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <span class="keywordflow">if</span>(is_quantized)</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  {</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  qss << <span class="stringliteral">" Input QuantInfo: "</span> << input->info()->quantization_info()</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  << <span class="stringliteral">" Weights QuantInfo: "</span> << weights->info()->quantization_info()</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  << <span class="stringliteral">" Output QuantInfo: "</span> << output->info()->quantization_info();</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>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  << node.name()</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  << <span class="stringliteral">" Type: "</span> << func_name</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  << qss.str()</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  << <span class="stringliteral">" Weights shape: "</span> << weights->info()->tensor_shape()</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  << (fused_act.<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#af5a8385102f8f8dd6c5957eac08b04c2">enabled</a>() ? <span class="stringliteral">" "</span> + <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#a9e0fb1d1462557f28966ae19988532c2">activation</a>()) : <span class="stringliteral">""</span>)</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  << std::endl);</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="keywordflow">return</span> func;</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span> }</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span> </div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span> <span class="keyword">template</span> <<span class="keyword">typename</span> EltwiseFunctions, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00501"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aa37faf92f78c0f5cefe2d43c8bf07f18"> 501</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="l00502"></a><span class="lineno"> 502</span> {</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</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="l00504"></a><span class="lineno"> 504</span> </div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input1 = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input2 = get_backing_tensor<TargetInfo>(node.input(1));</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</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="l00510"></a><span class="lineno"> 510</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="l00511"></a><span class="lineno"> 511</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="l00512"></a><span class="lineno"> 512</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="l00513"></a><span class="lineno"> 513</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="l00514"></a><span class="lineno"> 514</span> </div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  std::unique_ptr<IFunction> func = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  std::string func_name;</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</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="l00518"></a><span class="lineno"> 518</span>  {</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Addition>(</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  std::string(<span class="stringliteral">"ArithmeticAddition"</span>),</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</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="l00522"></a><span class="lineno"> 522</span>  }</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</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="l00524"></a><span class="lineno"> 524</span>  {</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Subtraction>(</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  std::string(<span class="stringliteral">"ArithmeticSubtraction"</span>),</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</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="l00528"></a><span class="lineno"> 528</span>  }</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</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="l00530"></a><span class="lineno"> 530</span>  {</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Multiplication>(</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  std::string(<span class="stringliteral">"PixelWiseMultiplication"</span>),</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</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="l00534"></a><span class="lineno"> 534</span>  }</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  {</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <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="l00538"></a><span class="lineno"> 538</span>  }</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span> </div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  << node.name()</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  << <span class="stringliteral">" Operation: "</span> << func_name</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  << <span class="stringliteral">" Data Type: "</span> << input1->info()->data_type()</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  << <span class="stringliteral">" Shape: "</span> << input1->info()->tensor_shape()</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  << std::endl);</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span> </div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <span class="keywordflow">return</span> func;</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span> }</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span> </div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span> <span class="keyword">template</span> <<span class="keyword">typename</span> FlattenLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00563"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a7880c3b249a6dad40da0ebcf6600b0e1"> 563</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="l00564"></a><span class="lineno"> 564</span> {</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</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="l00566"></a><span class="lineno"> 566</span> </div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span> </div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <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="l00572"></a><span class="lineno"> 572</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="l00573"></a><span class="lineno"> 573</span> </div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<FlattenLayerFunction>();</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  func->configure(input, output);</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span> </div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  << node.name()</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  << std::endl);</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="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span> }</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span> </div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span> <span class="keyword">template</span> <<span class="keyword">typename</span> FullyConnectedLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00602"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ac64bbd0df74207f9ab59953e21311178"> 602</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="l00603"></a><span class="lineno"> 603</span> {</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</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="l00605"></a><span class="lineno"> 605</span> </div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  <span class="keyword">typename</span> TargetInfo::TensorType *weights = get_backing_tensor<TargetInfo>(node.input(1));</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  <span class="keyword">typename</span> TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.input(2));</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</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="l00612"></a><span class="lineno"> 612</span> </div><div class="line"><a name="l00613"></a><span class="lineno"> 613</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="l00614"></a><span class="lineno"> 614</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="l00615"></a><span class="lineno"> 615</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="l00616"></a><span class="lineno"> 616</span> </div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00618"></a><span class="lineno"> 618</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="l00619"></a><span class="lineno"> 619</span>  func->configure(input, weights, biases, output, fc_info);</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="keyword">const</span> <span class="keywordtype">bool</span> is_quantized = <a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</a>(input->info()->data_type());</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span> </div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  std::ostringstream qss;</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <span class="keywordflow">if</span>(is_quantized)</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  {</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  qss << <span class="stringliteral">" Input QuantInfo: "</span> << input->info()->quantization_info()</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  << <span class="stringliteral">" Weights QuantInfo: "</span> << weights->info()->quantization_info()</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  << <span class="stringliteral">" Output QuantInfo: "</span> << output->info()->quantization_info();</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  }</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  << node.name()</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  << qss.str()</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  << <span class="stringliteral">" Weights shape: "</span> << weights->info()->tensor_shape()</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  << std::endl);</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span> </div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span> }</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span> </div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span> <span class="keyword">template</span> <<span class="keyword">typename</span> GenerateProposalsLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00656"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a1a18725e5da4a7ae62c9a3b731ab8fe1"> 656</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a1a18725e5da4a7ae62c9a3b731ab8fe1">create_generate_proposals_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_generate_proposals_layer_node.xhtml">GenerateProposalsLayerNode</a> &node, <a class="code" href="classarm__compute_1_1graph_1_1_graph_context.xhtml">GraphContext</a> &ctx)</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>  validate_node<TargetInfo>(node, 3 <span class="comment">/* expected inputs */</span>, 3 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span> </div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <span class="keyword">typename</span> TargetInfo::TensorType *scores = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  <span class="keyword">typename</span> TargetInfo::TensorType *deltas = get_backing_tensor<TargetInfo>(node.input(1));</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <span class="keyword">typename</span> TargetInfo::TensorType *anchors = get_backing_tensor<TargetInfo>(node.input(2));</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  <span class="keyword">typename</span> TargetInfo::TensorType *proposals = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <span class="keyword">typename</span> TargetInfo::TensorType *scores_out = get_backing_tensor<TargetInfo>(node.output(1));</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <span class="keyword">typename</span> TargetInfo::TensorType *num_valid_proposals = get_backing_tensor<TargetInfo>(node.output(2));</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_generate_proposals_info.xhtml">GenerateProposalsInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a> = node.info();</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span> </div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(scores == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(deltas == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(anchors == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(proposals == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(scores_out == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span> </div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<GenerateProposalsLayerFunction>(<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="l00677"></a><span class="lineno"> 677</span>  func->configure(scores, deltas, anchors, proposals, scores_out, num_valid_proposals, info);</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span> </div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00680"></a><span class="lineno"> 680</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="l00681"></a><span class="lineno"> 681</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  << <span class="stringliteral">" Data Type: "</span> << scores->info()->data_type()</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  << <span class="stringliteral">" Scores shape: "</span> << scores->info()->tensor_shape()</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  << <span class="stringliteral">" Deltas shape: "</span> << deltas->info()->tensor_shape()</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  << <span class="stringliteral">" Anchors shape: "</span> << anchors->info()->tensor_shape()</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  << <span class="stringliteral">" Proposals shape: "</span> << proposals->info()->tensor_shape()</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  << <span class="stringliteral">" Num valid proposals shape: "</span> << num_valid_proposals->info()->tensor_shape()</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  << <span class="stringliteral">" Scores Out shape: "</span> << scores_out->info()->tensor_shape()</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  << std::endl);</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span> </div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span> }</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span> </div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span> <span class="keyword">template</span> <<span class="keyword">typename</span> NormalizationLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00705"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a2dfc97df083b68f8409ba21d8a0110d8"> 705</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="l00706"></a><span class="lineno"> 706</span> {</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(ctx);</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span> </div><div class="line"><a name="l00709"></a><span class="lineno"> 709</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="l00710"></a><span class="lineno"> 710</span> </div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</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="l00715"></a><span class="lineno"> 715</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="l00716"></a><span class="lineno"> 716</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="l00717"></a><span class="lineno"> 717</span> </div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<NormalizationLayerFunction>();</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  func->configure(input, output, norm_info);</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span> </div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  << node.name()</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  << <span class="stringliteral">" Normalization info: "</span> << norm_info.type()</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  << std::endl);</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span> </div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span> }</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span> </div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span> <span class="keyword">template</span> <<span class="keyword">typename</span> NormalizePlanarYUVLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00746"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a92b35a365f58606a13baaf4501d78d9e"> 746</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a92b35a365f58606a13baaf4501d78d9e">create_normalize_planar_yuv_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_normalize_planar_y_u_v_layer_node.xhtml">NormalizePlanarYUVLayerNode</a> &node)</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span> {</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</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="l00749"></a><span class="lineno"> 749</span> </div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  <span class="keyword">typename</span> TargetInfo::TensorType *mean = get_backing_tensor<TargetInfo>(node.input(1));</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacestd.xhtml">std</a> = get_backing_tensor<TargetInfo>(node.input(2));</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</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="l00756"></a><span class="lineno"> 756</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(mean == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(std == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</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="l00759"></a><span class="lineno"> 759</span> </div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<NormalizePlanarYUVLayerFunction>();</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  func->configure(input, output, mean, std);</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span> </div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  << node.name()</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  << <span class="stringliteral">" Shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  << std::endl);</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span> </div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span> }</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span> </div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span> <span class="keyword">template</span> <<span class="keyword">typename</span> PadLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00786"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a127d0cce11ed3d411eaa5bd25c7a3ac1"> 786</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a127d0cce11ed3d411eaa5bd25c7a3ac1">create_pad_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_pad_layer_node.xhtml">PadLayerNode</a> &node)</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span> {</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</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="l00789"></a><span class="lineno"> 789</span> </div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ac1a1b012674e0f1de071a611391828ad">PaddingList</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">padding</a> = node.padding();</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</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="l00795"></a><span class="lineno"> 795</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="l00796"></a><span class="lineno"> 796</span> </div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<PadLayerFunction>();</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  func->configure(input, output, padding);</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span> </div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  << node.name()</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  << std::endl);</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span> </div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span> }</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span> </div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span> <span class="keyword">template</span> <<span class="keyword">typename</span> PermuteLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00824"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aa65a1becdfa5fc3533d79bba0cd4095c"> 824</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="l00825"></a><span class="lineno"> 825</span> {</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</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="l00827"></a><span class="lineno"> 827</span> </div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_strides.xhtml">PermutationVector</a> &perm = node.permutation_vector();</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</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="l00833"></a><span class="lineno"> 833</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="l00834"></a><span class="lineno"> 834</span> </div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<PermuteLayerFunction>();</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  func->configure(input, output, perm);</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span> </div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  << node.name()</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  << <span class="stringliteral">" Permutation vector: "</span> << perm</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>  << std::endl);</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span> </div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span> }</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span> </div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span> <span class="keyword">template</span> <<span class="keyword">typename</span> PoolingLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00863"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a0e51b62035e79b0f12964cae17ce0480"> 863</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="l00864"></a><span class="lineno"> 864</span> {</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</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="l00866"></a><span class="lineno"> 866</span> </div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</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="l00871"></a><span class="lineno"> 871</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="l00872"></a><span class="lineno"> 872</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="l00873"></a><span class="lineno"> 873</span> </div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<PoolingLayerFunction>();</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  func->configure(input, output, pool_info);</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span> </div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  << node.name()</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  << <span class="stringliteral">" Pooling info: "</span> << pool_info.pool_type()</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  << std::endl);</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span> </div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span> }</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span> </div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span> <span class="keyword">template</span> <<span class="keyword">typename</span> PriorBoxLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00902"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aaf28fee838c38cc4da407a4dbc62f7b0"> 902</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aaf28fee838c38cc4da407a4dbc62f7b0">create_priorbox_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_prior_box_layer_node.xhtml">PriorBoxLayerNode</a> &node)</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span> {</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</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="l00905"></a><span class="lineno"> 905</span> </div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input0 = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input1 = get_backing_tensor<TargetInfo>(node.input(1));</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_prior_box_layer_info.xhtml">PriorBoxLayerInfo</a> prior_info = node.priorbox_info();</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input0 == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</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="l00913"></a><span class="lineno"> 913</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="l00914"></a><span class="lineno"> 914</span> </div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<PriorBoxLayerFunction>();</div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  func->configure(input0, input1, output, prior_info);</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span> </div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  << node.name()</div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  << <span class="stringliteral">" Data Type: "</span> << input0->info()->data_type()</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  << <span class="stringliteral">" Input0 shape: "</span> << input0->info()->tensor_shape()</div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  << <span class="stringliteral">" Input1 shape: "</span> << input1->info()->tensor_shape()</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  << <span class="stringliteral">" PriorBoxLayer info: "</span> << prior_info</div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  << std::endl);</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span> </div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span> }</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span> </div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ReorgLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00944"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a0331e6b2b68ea76e9415f7f148d92601"> 944</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a0331e6b2b68ea76e9415f7f148d92601">create_reorg_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_reorg_layer_node.xhtml">ReorgLayerNode</a> &node)</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span> {</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</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="l00947"></a><span class="lineno"> 947</span> </div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</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="l00952"></a><span class="lineno"> 952</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="l00953"></a><span class="lineno"> 953</span> </div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ReorgLayerFunction>();</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  func->configure(input, output, node.stride());</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span> </div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  << node.name()</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  << std::endl);</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span> </div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span> }</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span> </div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ReshapeLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00981"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ad1922deea021647290d0c206723e6c73"> 981</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="l00982"></a><span class="lineno"> 982</span> {</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</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="l00984"></a><span class="lineno"> 984</span> </div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</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="l00989"></a><span class="lineno"> 989</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="l00990"></a><span class="lineno"> 990</span> </div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ReshapeLayerFunction>();</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  func->configure(input, output);</div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span> </div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  << node.name()</div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>  << std::endl);</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span> </div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span> }</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span> </div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ResizeLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01018"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#adb3a9be16de941b0f601e16c8ac76533"> 1018</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="l01019"></a><span class="lineno"> 1019</span> {</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</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="l01021"></a><span class="lineno"> 1021</span> </div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</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="l01026"></a><span class="lineno"> 1026</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="l01027"></a><span class="lineno"> 1027</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a966a9c417ce5e94dca08d9b5e745c0c9">InterpolationPolicy</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad986b73e9d5f47a623a9b6d773c25e34">policy</a> = node.policy();</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span> </div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ResizeLayerFunction>();</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  func->configure(input, output, policy, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a8d6b5cada83510220f59e00ce86d4d92">BorderMode::CONSTANT</a>);</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span> </div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  << node.name()</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>  << <span class="stringliteral">" Interpolation: "</span> << policy</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  << std::endl);</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span> </div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span> }</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span> </div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ROIAlignLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01057"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#adef77d1f64203fe2828b3f992c87f5df"> 1057</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#adef77d1f64203fe2828b3f992c87f5df">create_roi_align_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_r_o_i_align_layer_node.xhtml">ROIAlignLayerNode</a> &node)</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span> {</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</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="l01060"></a><span class="lineno"> 1060</span> </div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>  <span class="keyword">typename</span> TargetInfo::TensorType *rois = get_backing_tensor<TargetInfo>(node.input(1));</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</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="l01066"></a><span class="lineno"> 1066</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="l01067"></a><span class="lineno"> 1067</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(rois == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span> </div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_r_o_i_pooling_layer_info.xhtml">ROIPoolingLayerInfo</a> pool_info = node.pooling_info();</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span> </div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ROIAlignLayerFunction>();</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span> </div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  func->configure(input, rois, output, pool_info);</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span> </div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</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="l01078"></a><span class="lineno"> 1078</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>  << <span class="stringliteral">" ROIs shape: "</span> << rois->info()->tensor_shape()</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  << <span class="stringliteral">" ROIPooling width: "</span> << pool_info.<a class="code" href="classarm__compute_1_1_r_o_i_pooling_layer_info.xhtml#a5a00bf3cb11be124771cf7e3958e218e">pooled_width</a>()</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>  << <span class="stringliteral">" ROIPooling height: "</span> << pool_info.<a class="code" href="classarm__compute_1_1_r_o_i_pooling_layer_info.xhtml#aee6a0c75a44f00d3306101e14ffc3ebb">pooled_height</a>()</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>  << std::endl);</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span> </div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span> }</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span> </div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span> <span class="keyword">template</span> <<span class="keyword">typename</span> SliceLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01100"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a69a819dc92f559ed0b788392391da602"> 1100</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a69a819dc92f559ed0b788392391da602">create_slice_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_slice_layer_node.xhtml">SliceLayerNode</a> &node)</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span> {</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</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="l01103"></a><span class="lineno"> 1103</span> </div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</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="l01108"></a><span class="lineno"> 1108</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="l01109"></a><span class="lineno"> 1109</span> </div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<SliceLayerFunction>();</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  func->configure(input, output, node.starts(), node.ends());</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span> </div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  << node.name()</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>  << std::endl);</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span> </div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span> }</div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span> </div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span> <span class="keyword">template</span> <<span class="keyword">typename</span> SoftmaxLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01138"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a5271c97b6bef5972c5e259307d52a4da"> 1138</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="l01139"></a><span class="lineno"> 1139</span> {</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</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="l01141"></a><span class="lineno"> 1141</span> </div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</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="l01146"></a><span class="lineno"> 1146</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="l01147"></a><span class="lineno"> 1147</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="l01148"></a><span class="lineno"> 1148</span> </div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</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="l01151"></a><span class="lineno"> 1151</span>  func->configure(input, output, beta);</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span> </div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  << node.name()</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  << std::endl);</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span> </div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span> }</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span> <span class="keyword">template</span> <<span class="keyword">typename</span> UpsampleLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01176"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#acd9d23be81ad915ff875876c6606f576"> 1176</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#acd9d23be81ad915ff875876c6606f576">create_upsample_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_upsample_layer_node.xhtml">UpsampleLayerNode</a> &node, <a class="code" href="classarm__compute_1_1graph_1_1_graph_context.xhtml">GraphContext</a> &ctx)</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span> {</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  validate_node<TargetInfo>(node, 1 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span> </div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a> = node.info();</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a966a9c417ce5e94dca08d9b5e745c0c9">InterpolationPolicy</a> upsampling_policy = node.upsampling_policy();</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(upsampling_policy != <a class="code" href="namespacearm__compute.xhtml#a966a9c417ce5e94dca08d9b5e745c0c9a7f5ccbc3d30c2cd3fd04d567946cbde2">InterpolationPolicy::NEAREST_NEIGHBOR</a>);</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(info.x() != 2 || info.y() != 2);</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</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="l01188"></a><span class="lineno"> 1188</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="l01189"></a><span class="lineno"> 1189</span> </div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<UpsampleLayerFunction>();</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  func->configure(input, output, info, upsampling_policy);</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span> </div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>  << node.name()</div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  << <span class="stringliteral">" Strides: "</span> << info</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  << <span class="stringliteral">" Upsampling policy: "</span> << upsampling_policy</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  << std::endl);</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span> </div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span> }</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span> <span class="keyword">template</span> <<span class="keyword">typename</span> YOLOlayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01219"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a7db148217bc0f1f5a4adf6194c858d24"> 1219</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a7db148217bc0f1f5a4adf6194c858d24">create_yolo_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_y_o_l_o_layer_node.xhtml">YOLOLayerNode</a> &node, <a class="code" href="classarm__compute_1_1graph_1_1_graph_context.xhtml">GraphContext</a> &ctx)</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span> {</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</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="l01222"></a><span class="lineno"> 1222</span> </div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</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="l01227"></a><span class="lineno"> 1227</span>  <span class="keyword">const</span> int32_t num_classes = node.num_classes();</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(num_classes <= 0);</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</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="l01230"></a><span class="lineno"> 1230</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="l01231"></a><span class="lineno"> 1231</span> </div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<YOLOlayerFunction>();</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  func->configure(input, output, act_info, num_classes);</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span> </div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>  << node.name()</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>  << <span class="stringliteral">" Type: "</span> << node.type()</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>  << <span class="stringliteral">" Data Type: "</span> << input->info()->data_type()</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  << <span class="stringliteral">" Input shape: "</span> << input->info()->tensor_shape()</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  << <span class="stringliteral">" Activation function: "</span> << act_info.activation()</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>  << <span class="stringliteral">" Num classes: "</span> << num_classes</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>  << std::endl);</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span> </div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span> }</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span> } <span class="comment">// namespace detail</span></div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span> } <span class="comment">// namespace backends</span></div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span> } <span class="comment">// namespace graph</span></div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span> } <span class="comment">// namespace arm_compute</span></div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span> </div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span> <span class="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#l00705">FunctionHelpers.h:705</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml_a1d89c28bd42ba9a52da008bb69367171"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">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 class="ttdef"><b>Definition:</b> <a href="_i_node_8cpp_source.xhtml#l00107">INode.cpp:107</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_upsample_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_upsample_layer_node.xhtml">arm_compute::graph::UpsampleLayerNode</a></div><div class="ttdoc">Upsample Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_upsample_layer_node_8h_source.xhtml#l00034">UpsampleLayerNode.h:34</a></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#l00391">Types.h:391</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="classarm__compute_1_1_generate_proposals_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_generate_proposals_info.xhtml">arm_compute::GenerateProposalsInfo</a></div><div class="ttdoc">Generate Proposals Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01085">Types.h:1085</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_1test_1_1validation_xhtml_ad986b73e9d5f47a623a9b6d773c25e34"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ad986b73e9d5f47a623a9b6d773c25e34">arm_compute::test::validation::policy</a></div><div class="ttdeci">policy</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_arithmetic_subtraction_8cpp_source.xhtml#l00175">ArithmeticSubtraction.cpp:175</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#l00094">Types.h:94</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="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a69a819dc92f559ed0b788392391da602"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a69a819dc92f559ed0b788392391da602">arm_compute::graph::backends::detail::create_slice_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_slice_layer(SliceLayerNode &node)</div><div class="ttdoc">Create a backend slice layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l01100">FunctionHelpers.h:1100</a></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#l00501">FunctionHelpers.h:501</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_af5a8385102f8f8dd6c5957eac08b04c2"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#af5a8385102f8f8dd6c5957eac08b04c2">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#l01330">Types.h:1330</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ac1a1b012674e0f1de071a611391828ad"><div class="ttname"><a href="namespacearm__compute.xhtml#ac1a1b012674e0f1de071a611391828ad">arm_compute::PaddingList</a></div><div class="ttdeci">std::vector< PaddingInfo > PaddingList</div><div class="ttdoc">List of padding information. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00480">Types.h:480</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_concatenate_layer_node_xhtml_a051cd2a8d15cf783e9ab9a00451c77f6"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_concatenate_layer_node.xhtml#a051cd2a8d15cf783e9ab9a00451c77f6">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 class="ttdef"><b>Definition:</b> <a href="_concatenate_layer_node_8cpp_source.xhtml#l00054">ConcatenateLayerNode.cpp:54</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_tensor_xhtml_a45c1ef0023ce430d009ec79c97761544"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_tensor.xhtml#a45c1ef0023ce430d009ec79c97761544">arm_compute::graph::Tensor::handle</a></div><div class="ttdeci">ITensorHandle * handle()</div><div class="ttdoc">Backend tensor handle accessor. </div><div class="ttdef"><b>Definition:</b> <a href="graph_2_tensor_8cpp_source.xhtml#l00055">Tensor.cpp:55</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#l00133">FunctionHelpers.h:133</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">[DataLayout enum definition] </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00120">Types.h:120</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml_aa26cd423e8fc9233bd2bff46f82f46ab"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#aa26cd423e8fc9233bd2bff46f82f46ab">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 class="ttdef"><b>Definition:</b> <a href="_i_node_8cpp_source.xhtml#l00198">INode.cpp:198</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#l00431">FunctionHelpers.h:431</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="classarm__compute_1_1graph_1_1_i_node_xhtml_a75d7c8bdf0059bb235880f8ef8d190bc"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#a75d7c8bdf0059bb235880f8ef8d190bc">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 class="ttdef"><b>Definition:</b> <a href="_i_node_8cpp_source.xhtml#l00183">INode.cpp:183</a></div></div> |
| <div class="ttc" id="_asymm_helpers_8cpp_xhtml_a552dc3787d7ea1675f3e4e8993501d58"><div class="ttname"><a href="_asymm_helpers_8cpp.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a></div><div class="ttdeci">constexpr float epsilon</div><div class="ttdef"><b>Definition:</b> <a href="_asymm_helpers_8cpp_source.xhtml#l00033">AsymmHelpers.cpp:33</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#l01018">FunctionHelpers.h:1018</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#l01343">Types.h:1343</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_normalize_planar_y_u_v_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_normalize_planar_y_u_v_layer_node.xhtml">arm_compute::graph::NormalizePlanarYUVLayerNode</a></div><div class="ttdoc">Batch Normalization Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_normalize_planar_y_u_v_layer_node_8h_source.xhtml#l00034">NormalizePlanarYUVLayerNode.h:34</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_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#l00824">FunctionHelpers.h:824</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="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a1a18725e5da4a7ae62c9a3b731ab8fe1"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a1a18725e5da4a7ae62c9a3b731ab8fe1">arm_compute::graph::backends::detail::create_generate_proposals_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_generate_proposals_layer(GenerateProposalsLayerNode &node, GraphContext &ctx)</div><div class="ttdoc">Create a backend generate proposals layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00656">FunctionHelpers.h:656</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#l00802">Types.h:802</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="classarm__compute_1_1graph_1_1_reorg_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_reorg_layer_node.xhtml">arm_compute::graph::ReorgLayerNode</a></div><div class="ttdoc">Reorg Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_reorg_layer_node_8h_source.xhtml#l00034">ReorgLayerNode.h:34</a></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#l00563">FunctionHelpers.h:563</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_size2_d_xhtml_a94b8468af876f5ab54020d5e9787a4f0"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml#a94b8468af876f5ab54020d5e9787a4f0">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_r_o_i_pooling_layer_info_xhtml_a5a00bf3cb11be124771cf7e3958e218e"><div class="ttname"><a href="classarm__compute_1_1_r_o_i_pooling_layer_info.xhtml#a5a00bf3cb11be124771cf7e3958e218e">arm_compute::ROIPoolingLayerInfo::pooled_width</a></div><div class="ttdeci">unsigned int pooled_width() const</div><div class="ttdoc">Get the pooled width of the layer. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01057">Types.h:1057</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a127d0cce11ed3d411eaa5bd25c7a3ac1"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a127d0cce11ed3d411eaa5bd25c7a3ac1">arm_compute::graph::backends::detail::create_pad_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_pad_layer(PadLayerNode &node)</div><div class="ttdoc">Create a backend pad layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00786">FunctionHelpers.h:786</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a966a9c417ce5e94dca08d9b5e745c0c9a7f5ccbc3d30c2cd3fd04d567946cbde2"><div class="ttname"><a href="namespacearm__compute.xhtml#a966a9c417ce5e94dca08d9b5e745c0c9a7f5ccbc3d30c2cd3fd04d567946cbde2">arm_compute::InterpolationPolicy::NEAREST_NEIGHBOR</a></div><div class="ttdoc">Output values are defined to match the source pixel whose center is nearest to the sample position...</div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_slice_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_slice_layer_node.xhtml">arm_compute::graph::SliceLayerNode</a></div><div class="ttdoc">Slice Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_slice_layer_node_8h_source.xhtml#l00036">SliceLayerNode.h:36</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#l00049">GraphContext.h:49</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#l01283">Types.h:1283</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="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_1_1test_1_1validation_xhtml_a096668313a9a819d54a2e65ec21ff0cc"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">arm_compute::test::validation::info</a></div><div class="ttdeci">src info() -> set_format(Format::S16)</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2018 ARM Limited. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</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_bounding_box_transform_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_bounding_box_transform_layer_node.xhtml">arm_compute::graph::BoundingBoxTransformLayerNode</a></div><div class="ttdoc">Bounding Box Transform Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_bounding_box_transform_layer_node_8h_source.xhtml#l00035">BoundingBoxTransformLayerNode.h:35</a></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_1graph_1_1_pad_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_pad_layer_node.xhtml">arm_compute::graph::PadLayerNode</a></div><div class="ttdoc">Pad Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_pad_layer_node_8h_source.xhtml#l00034">PadLayerNode.h:34</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#l01418">Types.h:1418</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#l01138">FunctionHelpers.h:1138</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_tensor_xhtml_a0d3608f94078b90ab1ff9e9465d4ed3b"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_tensor.xhtml#a0d3608f94078b90ab1ff9e9465d4ed3b">arm_compute::graph::Tensor::desc</a></div><div class="ttdeci">TensorDescriptor & desc()</div><div class="ttdoc">TensorInfo metadata accessor. </div><div class="ttdef"><b>Definition:</b> <a href="graph_2_tensor_8cpp_source.xhtml#l00040">Tensor.cpp:40</a></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#l00296">FunctionHelpers.h:296</a></div></div> |
| <div class="ttc" id="namespacestd_xhtml"><div class="ttname"><a href="namespacestd.xhtml">std</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_acd9d23be81ad915ff875876c6606f576"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#acd9d23be81ad915ff875876c6606f576">arm_compute::graph::backends::detail::create_upsample_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_upsample_layer(UpsampleLayerNode &node, GraphContext &ctx)</div><div class="ttdoc">Create a backend Upsample layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l01176">FunctionHelpers.h:1176</a></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_1_1graph_1_1backends_1_1detail_xhtml_adef77d1f64203fe2828b3f992c87f5df"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#adef77d1f64203fe2828b3f992c87f5df">arm_compute::graph::backends::detail::create_roi_align_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_roi_align_layer(ROIAlignLayerNode &node)</div><div class="ttdoc">Create a backend ROI align layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l01057">FunctionHelpers.h:1057</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_concatenate_layer_node_xhtml_a6507f40ddf408e1f124cb84fa5cbfd1e"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_concatenate_layer_node.xhtml#a6507f40ddf408e1f124cb84fa5cbfd1e">arm_compute::graph::ConcatenateLayerNode::is_enabled</a></div><div class="ttdeci">bool is_enabled() const</div><div class="ttdoc">Enabled parameter accessor. </div><div class="ttdef"><b>Definition:</b> <a href="_concatenate_layer_node_8cpp_source.xhtml#l00049">ConcatenateLayerNode.cpp:49</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_a27369471d9b2ba47746e1e923f585b9f"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#a27369471d9b2ba47746e1e923f585b9f">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 class="ttdef"><b>Definition:</b> <a href="_i_node_8cpp_source.xhtml#l00158">INode.cpp:158</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_prior_box_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_prior_box_layer_node.xhtml">arm_compute::graph::PriorBoxLayerNode</a></div><div class="ttdoc">PriorBox Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_prior_box_layer_node_8h_source.xhtml#l00034">PriorBoxLayerNode.h:34</a></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#l00111">Types.h:111</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="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_aaf28fee838c38cc4da407a4dbc62f7b0"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aaf28fee838c38cc4da407a4dbc62f7b0">arm_compute::graph::backends::detail::create_priorbox_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_priorbox_layer(PriorBoxLayerNode &node)</div><div class="ttdoc">Create a backend priorbox layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00902">FunctionHelpers.h:902</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml_a5ea90a25ef8059df2d2e51b82991ebf3"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#a5ea90a25ef8059df2d2e51b82991ebf3">arm_compute::graph::INode::id</a></div><div class="ttdeci">NodeID id() const</div><div class="ttdoc">Returns node&#39;s ID. </div><div class="ttdef"><b>Definition:</b> <a href="_i_node_8cpp_source.xhtml#l00102">INode.cpp:102</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="classarm__compute_1_1_prior_box_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_prior_box_layer_info.xhtml">arm_compute::PriorBoxLayerInfo</a></div><div class="ttdoc">PriorBox layer info. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00834">Types.h:834</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#l00685">Types.h:685</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_1_bounding_box_transform_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_bounding_box_transform_info.xhtml">arm_compute::BoundingBoxTransformInfo</a></div><div class="ttdoc">Bounding Box Transform information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01216">Types.h:1216</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#l00694">Winograd.cpp:694</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="classarm__compute_1_1_size2_d_xhtml_aaaeb4853150b7d0e8b685fd08052924f"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml#aaaeb4853150b7d0e8b685fd08052924f">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_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#l00387">FunctionHelpers.h:387</a></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="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#l00996">Utils.h:996</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_1_strides_xhtml"><div class="ttname"><a href="classarm__compute_1_1_strides.xhtml">arm_compute::Strides</a></div><div class="ttdoc">Strides of an item in bytes. </div><div class="ttdef"><b>Definition:</b> <a href="_strides_8h_source.xhtml#l00037">Strides.h:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_r_o_i_align_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_r_o_i_align_layer_node.xhtml">arm_compute::graph::ROIAlignLayerNode</a></div><div class="ttdoc">ROI Align node. </div><div class="ttdef"><b>Definition:</b> <a href="_r_o_i_align_layer_node_8h_source.xhtml#l00036">ROIAlignLayerNode.h:36</a></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#l00602">FunctionHelpers.h:602</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_r_o_i_pooling_layer_info_xhtml_aee6a0c75a44f00d3306101e14ffc3ebb"><div class="ttname"><a href="classarm__compute_1_1_r_o_i_pooling_layer_info.xhtml#aee6a0c75a44f00d3306101e14ffc3ebb">arm_compute::ROIPoolingLayerInfo::pooled_height</a></div><div class="ttdeci">unsigned int pooled_height() const</div><div class="ttdoc">Get the pooled height of the layer. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01062">Types.h:1062</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_y_o_l_o_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_y_o_l_o_layer_node.xhtml">arm_compute::graph::YOLOLayerNode</a></div><div class="ttdoc">YOLO Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_y_o_l_o_layer_node_8h_source.xhtml#l00034">YOLOLayerNode.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_r_o_i_pooling_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_r_o_i_pooling_layer_info.xhtml">arm_compute::ROIPoolingLayerInfo</a></div><div class="ttdoc">ROI Pooling Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01042">Types.h:1042</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="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a92b35a365f58606a13baaf4501d78d9e"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a92b35a365f58606a13baaf4501d78d9e">arm_compute::graph::backends::detail::create_normalize_planar_yuv_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_normalize_planar_yuv_layer(NormalizePlanarYUVLayerNode &node)</div><div class="ttdoc">Create a backend normalize planar YUV layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00746">FunctionHelpers.h:746</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#l00102">Types.h:102</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="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_ab3787ac008a709edda8d347370bbad61"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ab3787ac008a709edda8d347370bbad61">arm_compute::graph::backends::detail::create_bounding_box_transform_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_bounding_box_transform_layer(BoundingBoxTransformLayerNode &node)</div><div class="ttdoc">Create a backend bounding box transform layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00176">FunctionHelpers.h:176</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_i_node_xhtml_abe0cb3e4411a1c289e3e40e3f9d79fec"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#abe0cb3e4411a1c289e3e40e3f9d79fec">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 class="ttdef"><b>Definition:</b> <a href="_i_node_8cpp_source.xhtml#l00178">INode.cpp:178</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_generate_proposals_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_generate_proposals_layer_node.xhtml">arm_compute::graph::GenerateProposalsLayerNode</a></div><div class="ttdoc">Generate Proposals Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_generate_proposals_layer_node_8h_source.xhtml#l00035">GenerateProposalsLayerNode.h:35</a></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="_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_1_activation_layer_info_xhtml_a9e0fb1d1462557f28966ae19988532c2"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a9e0fb1d1462557f28966ae19988532c2">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#l01315">Types.h:1315</a></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_i_node_xhtml_ad7c09b0faaf3c808b0489012204852a9"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">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 class="ttdef"><b>Definition:</b> <a href="_i_node_8cpp_source.xhtml#l00150">INode.cpp:150</a></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 class="ttdef"><b>Definition:</b> <a href="_concatenate_layer_node_8cpp_source.xhtml#l00131">ConcatenateLayerNode.cpp:131</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a735a025fce26c1ef147b54426df18181"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">arm_compute::test::validation::padding</a></div><div class="ttdeci">const PaddingSize padding</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_arithmetic_division_8cpp_source.xhtml#l00111">ArithmeticDivision.cpp:111</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#l00957">Types.h:957</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#l00863">FunctionHelpers.h:863</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#l00247">FunctionHelpers.h:247</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#l00384">Types.h:384</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#l00981">FunctionHelpers.h:981</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a7db148217bc0f1f5a4adf6194c858d24"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a7db148217bc0f1f5a4adf6194c858d24">arm_compute::graph::backends::detail::create_yolo_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_yolo_layer(YOLOLayerNode &node, GraphContext &ctx)</div><div class="ttdoc">Create a backend YOLO layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l01219">FunctionHelpers.h:1219</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a0331e6b2b68ea76e9415f7f148d92601"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a0331e6b2b68ea76e9415f7f148d92601">arm_compute::graph::backends::detail::create_reorg_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_reorg_layer(ReorgLayerNode &node)</div><div class="ttdoc">Create a backend reorg layer function. </div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00944">FunctionHelpers.h:944</a></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#l00212">FunctionHelpers.h:212</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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