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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) 2018-2020 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_2_utils_8h.xhtml">arm_compute/graph/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="_fused_convolution_batch_normalization_function_8h.xhtml">arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h</a>"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include "<a class="code" href="_fused_depthwise_convolution_batch_normalization_function_8h.xhtml">arm_compute/graph/backends/FusedDepthwiseConvolutionBatchNormalizationFunction.h</a>"</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</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="l00035"></a><span class="lineno"> 35</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="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00037"></a><span class="lineno"> 37</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="l00038"></a><span class="lineno"> 38</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="l00039"></a><span class="lineno"> 39</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="l00040"></a><span class="lineno"> 40</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="l00041"></a><span class="lineno"> 41</span> </div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> {</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="keyword">namespace </span>graph</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> {</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="keyword">namespace </span>backends</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> {</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="keyword">namespace </span>detail</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> {<span class="comment"></span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="comment">/** Returns backing tensor of a given tensor</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="comment"> *</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="comment"> * @tparam TargetInfo Target information</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> <span class="comment"> *</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> <span class="comment"> * @param[in] tensor Tensor to extract the backing tensor from</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="comment"> *</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> <span class="comment"> * @return Backing tensor if present else nullptr</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> <span class="comment"> */</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> <span class="keyword">template</span> <<span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00059"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a69dd1fc17c7a15f4125873be182c8c76"> 59</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="l00060"></a><span class="lineno"> 60</span> {</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keyword">typename</span> TargetInfo::TensorType *backing_tensor = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">if</span>(tensor != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  {</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <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="l00065"></a><span class="lineno"> 65</span>  <span class="comment">// Get backing tensor handle</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</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="l00067"></a><span class="lineno"> 67</span>  <span class="comment">// Get backing tensor</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</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="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="keywordflow">return</span> backing_tensor;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> }</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> <span class="keyword">template</span> <<span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00075"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aa6fabefcb8c4bd308219565ddcf00928"> 75</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="l00076"></a><span class="lineno"> 76</span> {</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</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="l00078"></a><span class="lineno"> 78</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</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="l00080"></a><span class="lineno"> 80</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  << std::endl);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> </div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <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="l00084"></a><span class="lineno"> 84</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="l00085"></a><span class="lineno"> 85</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="l00086"></a><span class="lineno"> 86</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(node, num_expected_inputs, num_expected_outputs);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> }</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> <span class="comment"></span></div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> <span class="comment">/** Creates a backend activation layer function</span></div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> <span class="comment"> *</span></div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> <span class="comment"> * @tparam ActivationLayerFunction Backend activation function</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> <span class="comment"> *</span></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> <span class="comment"> *</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> <span class="comment"> * @return Backend activation layer function</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> <span class="comment"> */</span></div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ActivationLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00099"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a0f5afb0ddd5aec3a8e4df3c56d7d91f4"> 99</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="l00100"></a><span class="lineno"> 100</span> {</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</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="l00102"></a><span class="lineno"> 102</span> </div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</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="l00106"></a><span class="lineno"> 106</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a> = node.<a class="code" href="classarm__compute_1_1graph_1_1_activation_layer_node.xhtml#a39a8dd296461705ee5cb54eacb4b2818">activation_info</a>();</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="comment">// Create function</span></div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ActivationLayerFunction>();</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> </div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <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="l00113"></a><span class="lineno"> 113</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_activation_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  << <span class="stringliteral">" Shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  << <span class="stringliteral">" Activation function: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.activation()</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  << <span class="stringliteral">" a: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.a()</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  << <span class="stringliteral">" b: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.b()</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  << <span class="stringliteral">" InPlace : "</span> << <a class="code" href="namespacearm__compute_1_1graph_1_1backends.xhtml#a4403f766b0d02eb3882a9521d0390986">is_in_place_operation</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output)</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  << std::endl);</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> </div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> }</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> <span class="comment"></span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> <span class="comment">/** Create a backend batch normalization layer function</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> <span class="comment"> *</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> <span class="comment"> * @tparam BatchNormalizationLayerFunction Backend batch normalization function</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> <span class="comment"> *</span></div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> <span class="comment"> *</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> <span class="comment"> * @return Backend batch normalization layer function</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> <span class="comment"> */</span></div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> <span class="keyword">template</span> <<span class="keyword">typename</span> BatchNormalizationLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00137"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a5d1a73ab4a0b267033a569c46813b9d5"> 137</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="l00138"></a><span class="lineno"> 138</span> {</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</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="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keyword">typename</span> TargetInfo::TensorType *mean = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(1));</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keyword">typename</span> TargetInfo::TensorType *var = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(2));</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keyword">typename</span> TargetInfo::TensorType *beta = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(3));</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keyword">typename</span> TargetInfo::TensorType *gamma = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(4));</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> </div><div class="line"><a name="l00148"></a><span class="lineno"> 148</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="l00149"></a><span class="lineno"> 149</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> <a class="code" href="namespacearm__compute_1_1quantization.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a> = node.<a class="code" href="classarm__compute_1_1graph_1_1_batch_normalization_layer_node.xhtml#acd39aa81617eab4d3482fa904d5dee87">epsilon</a>();</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> fused_act = node.<a class="code" href="classarm__compute_1_1graph_1_1_batch_normalization_layer_node.xhtml#a32186582e0a6e02ed7ac3944f60b9c62">fused_activation</a>();</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> </div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<BatchNormalizationLayerFunction>();</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output, mean, var, beta, gamma, <a class="code" href="namespacearm__compute_1_1quantization.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a>, fused_act);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> </div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00157"></a><span class="lineno"> 157</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="l00158"></a><span class="lineno"> 158</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_batch_normalization_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  << <span class="stringliteral">" Shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  << <span class="stringliteral">" Epsilon: "</span> << <a class="code" href="namespacearm__compute_1_1quantization.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a> << <span class="stringliteral">" "</span></div><div class="line"><a name="l00164"></a><span class="lineno"> 164</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="l00165"></a><span class="lineno"> 165</span>  << <span class="stringliteral">" InPlace: "</span> << <a class="code" href="namespacearm__compute_1_1graph_1_1backends.xhtml#a4403f766b0d02eb3882a9521d0390986">is_in_place_operation</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output)</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  << std::endl);</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> </div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> }</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> <span class="comment"></span></div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> <span class="comment">/** Create a backend batch normalization layer function</span></div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> <span class="comment"> *</span></div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> <span class="comment"> * @tparam BatchNormalizationLayerFunction Backend batch normalization function</span></div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> <span class="comment"> *</span></div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> <span class="comment"> * @param[in] ctx Graph context</span></div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> <span class="comment"> *</span></div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> <span class="comment"> * @return Backend batch normalization layer function</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> <span class="comment"> */</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> <span class="keyword">template</span> <<span class="keyword">typename</span> FusedLayerTypes, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00182"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a7f44d10197128d3f478626b5c68b3c35"> 182</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a7f44d10197128d3f478626b5c68b3c35">create_fused_convolution_batch_normalization_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node.xhtml">FusedConvolutionBatchNormalizationNode</a> &node, <a class="code" href="classarm__compute_1_1graph_1_1_graph_context.xhtml">GraphContext</a> &ctx)</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> {</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  validate_node<TargetInfo>(node, 7 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(1));</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keyword">typename</span> TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(2));</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keyword">typename</span> TargetInfo::TensorType *mean = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(3));</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keyword">typename</span> TargetInfo::TensorType *var = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(4));</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keyword">typename</span> TargetInfo::TensorType *beta = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(5));</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <span class="keyword">typename</span> TargetInfo::TensorType *gamma = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(6));</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> </div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="keyword">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="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keyword">const</span> <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#a00525ff582f16038a1d3819aa44a23a3">conv_info</a> = node.<a class="code" href="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node.xhtml#ad087f1f9aa1e444236911adf6c57df04">convolution_info</a>();</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> = node.<a class="code" href="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node.xhtml#a984881c2c9e6de259af8fcd4ecbc4d80">num_groups</a>();</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> fast_math = node.<a class="code" href="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node.xhtml#a807d0a897f65b2fa1f8ea92892fa2c2a">fast_math_hint</a>() == <a class="code" href="namespacearm__compute_1_1graph.xhtml#ac85a46f3ebd3ab09f576a994ac2dce11a00d23a76e43b46dae9ec7aa9dcbebb32">FastMathHint::Enabled</a>;</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> fused_act = node.<a class="code" href="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node.xhtml#a32186582e0a6e02ed7ac3944f60b9c62">fused_activation</a>();</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> <a class="code" href="namespacearm__compute_1_1quantization.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a> = node.<a class="code" href="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node.xhtml#acd39aa81617eab4d3482fa904d5dee87">epsilon</a>();</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> </div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="comment">// Create and configure function (we assume that functions have been validated before creation)</span></div><div class="line"><a name="l00204"></a><span class="lineno"> 204</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="l00205"></a><span class="lineno"> 205</span>  std::unique_ptr<IFunction> func;</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  std::string func_name;</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> </div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keyword">using</span> FType = <a class="code" href="classarm__compute_1_1graph_1_1backends_1_1_fused_convolution_batch_normalization_function.xhtml">FusedConvolutionBatchNormalizationFunction<TargetInfo, FusedLayerTypes></a>;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> </div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  std::tie(func, func_name) = create_named_memory_managed_function<FType>(</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  std::string(<span class="stringliteral">"FusedConvolutionBatchNormalizationLayer"</span>), mm, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, mean, var, beta, gamma, <a class="code" href="namespacearm__compute_1_1quantization.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>, fast_math, fused_act);</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span> </div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</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="l00216"></a><span class="lineno"> 216</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  << <span class="stringliteral">" Weights shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>()</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</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="l00224"></a><span class="lineno"> 224</span>  << std::endl);</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keywordflow">return</span> func;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> }</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> <span class="comment"></span></div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span> <span class="comment">/** Create a backend fused depthwise convolution batch normalization layer function</span></div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> <span class="comment"> *</span></div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> <span class="comment"> * @tparam FusedLayerTypes Fused layer types</span></div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span> <span class="comment"> *</span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span> <span class="comment"> * @param[in] ctx Graph context</span></div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> <span class="comment"> *</span></div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> <span class="comment"> * @return Backend fused depthwise convolution batch normalization layer function</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> <span class="comment"> */</span></div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span> <span class="keyword">template</span> <<span class="keyword">typename</span> FusedLayerTypes, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00239"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a7ec865e1ee296647ec995b501e5ceb8b"> 239</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a7ec865e1ee296647ec995b501e5ceb8b">create_fused_depthwise_convolution_batch_normalization_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_fused_depthwise_convolution_batch_normalization_node.xhtml">FusedDepthwiseConvolutionBatchNormalizationNode</a> &node, <a class="code" href="classarm__compute_1_1graph_1_1_graph_context.xhtml">GraphContext</a> &ctx)</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> {</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  validate_node<TargetInfo>(node, 7 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span> </div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(1));</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keyword">typename</span> TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(2));</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="keyword">typename</span> TargetInfo::TensorType *mean = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(3));</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="keyword">typename</span> TargetInfo::TensorType *var = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(4));</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="keyword">typename</span> TargetInfo::TensorType *beta = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(5));</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keyword">typename</span> TargetInfo::TensorType *gamma = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(6));</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="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="l00253"></a><span class="lineno"> 253</span> </div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keyword">const</span> <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#a00525ff582f16038a1d3819aa44a23a3">conv_info</a> = node.<a class="code" href="classarm__compute_1_1graph_1_1_fused_depthwise_convolution_batch_normalization_node.xhtml#ad087f1f9aa1e444236911adf6c57df04">convolution_info</a>();</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth_multiplier = node.<a class="code" href="classarm__compute_1_1graph_1_1_fused_depthwise_convolution_batch_normalization_node.xhtml#ac8cef0874f04203401b5d7f5a6fa2a34">depth_multiplier</a>();</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> fused_act = node.<a class="code" href="classarm__compute_1_1graph_1_1_fused_depthwise_convolution_batch_normalization_node.xhtml#a32186582e0a6e02ed7ac3944f60b9c62">fused_activation</a>();</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> <a class="code" href="namespacearm__compute_1_1quantization.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a> = node.<a class="code" href="classarm__compute_1_1graph_1_1_fused_depthwise_convolution_batch_normalization_node.xhtml#acd39aa81617eab4d3482fa904d5dee87">epsilon</a>();</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> </div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="comment">// Create and configure function (we assume that functions have been validated before creation)</span></div><div class="line"><a name="l00260"></a><span class="lineno"> 260</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="l00261"></a><span class="lineno"> 261</span>  std::unique_ptr<IFunction> func;</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  std::string func_name;</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">using</span> FType = <a class="code" href="classarm__compute_1_1graph_1_1backends_1_1_fused_depthwise_convolution_batch_normalization_function.xhtml">FusedDepthwiseConvolutionBatchNormalizationFunction<TargetInfo, FusedLayerTypes></a>;</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> </div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  std::tie(func, func_name) = create_named_memory_managed_function<FType>(</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  std::string(<span class="stringliteral">"FusedDepthwiseConvolutionBatchNormalizationLayer"</span>), mm, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, mean, var, beta, gamma, <a class="code" href="namespacearm__compute_1_1quantization.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, depth_multiplier, fused_act);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> </div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</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="l00272"></a><span class="lineno"> 272</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_fused_depthwise_convolution_batch_normalization_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  << <span class="stringliteral">" Weights shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>()</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</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="l00280"></a><span class="lineno"> 280</span>  << std::endl);</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <span class="keywordflow">return</span> func;</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> }</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> <span class="comment"></span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span> <span class="comment">/** Create a backend bounding box transform layer function</span></div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> <span class="comment"> *</span></div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span> <span class="comment"> * @tparam BoundingBoxTransformLayerFunction Backend bounding box transform function</span></div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span> <span class="comment"> *</span></div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> <span class="comment"> *</span></div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span> <span class="comment"> * @return Backend bounding box transform layer function</span></div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span> <span class="comment"> */</span></div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span> <span class="keyword">template</span> <<span class="keyword">typename</span> BoundingBoxTransformLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00294"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ab3787ac008a709edda8d347370bbad61"> 294</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="l00295"></a><span class="lineno"> 295</span> {</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</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="l00297"></a><span class="lineno"> 297</span> </div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keyword">typename</span> TargetInfo::TensorType *deltas = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(1));</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</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="l00302"></a><span class="lineno"> 302</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_bounding_box_transform_info.xhtml">BoundingBoxTransformInfo</a> bbox_info = node.<a class="code" href="classarm__compute_1_1graph_1_1_bounding_box_transform_layer_node.xhtml#a7a5bf7cea9e9cf19a6cf3e5240c5fff7">info</a>();</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> </div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<BoundingBoxTransformLayerFunction>();</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output, deltas, bbox_info);</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="comment">// Log info</span></div><div class="line"><a name="l00309"></a><span class="lineno"> 309</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="l00310"></a><span class="lineno"> 310</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_bounding_box_transform_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  << <span class="stringliteral">" Shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  << <span class="stringliteral">" BoundingBox Info img W: "</span> << bbox_info.img_width() << <span class="stringliteral">" "</span></div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  << <span class="stringliteral">" BoundingBox Info img H: "</span> << bbox_info.img_height() << <span class="stringliteral">" "</span></div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  << std::endl);</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="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> }</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span> <span class="comment"></span></div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span> <span class="comment">/** Create a backend channel shuffle layer function</span></div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> <span class="comment"> *</span></div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span> <span class="comment"> * @tparam ChannelShuffleLayerFunction Backend channel shuffle function</span></div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> <span class="comment"> *</span></div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span> <span class="comment"> *</span></div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span> <span class="comment"> * @return Backend channel shuffle layer function</span></div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> <span class="comment"> */</span></div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ChannelShuffleLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00332"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a3f5c14020836599056281fe52d7e9dd3"> 332</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="l00333"></a><span class="lineno"> 333</span> {</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</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="l00335"></a><span class="lineno"> 335</span> </div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</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="l00339"></a><span class="lineno"> 339</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> = node.<a class="code" href="classarm__compute_1_1graph_1_1_channel_shuffle_layer_node.xhtml#a984881c2c9e6de259af8fcd4ecbc4d80">num_groups</a>();</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span> </div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="comment">// Create function</span></div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ChannelShuffleLayerFunction>();</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>);</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>  <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="l00346"></a><span class="lineno"> 346</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_channel_shuffle_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  << <span class="stringliteral">" Shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  << <span class="stringliteral">" Num groups: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a></div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  << std::endl);</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span> </div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span> }</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span> <span class="comment"></span></div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> <span class="comment">/** Create a backend layer concatenate function</span></div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span> <span class="comment"> *</span></div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span> <span class="comment"> * @tparam ConcatenateLayerFunction Backend concatenate function</span></div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span> <span class="comment"> *</span></div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span> <span class="comment"> *</span></div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> <span class="comment"> * @return Backend concatenate layer function</span></div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span> <span class="comment"> */</span></div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ConcatenateLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00367"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a627f6bdc4a7de6dbb03acb3d8b3a4d6d"> 367</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="l00368"></a><span class="lineno"> 368</span> {</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</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="l00370"></a><span class="lineno"> 370</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="l00371"></a><span class="lineno"> 371</span> </div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="comment">// Return nullptr if depth concatenate is switched off</span></div><div class="line"><a name="l00373"></a><span class="lineno"> 373</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="l00374"></a><span class="lineno"> 374</span>  {</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  }</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span> </div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  std::vector<typename TargetInfo::TensorType *> inputs;</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</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="l00381"></a><span class="lineno"> 381</span>  {</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</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="l00383"></a><span class="lineno"> 383</span>  }</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</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="l00385"></a><span class="lineno"> 385</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a27369471d9b2ba47746e1e923f585b9f">output</a>(0) != <span class="keyword">nullptr</span> ? node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a27369471d9b2ba47746e1e923f585b9f">output</a>(0)-><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#a4b52bb397c7296e8efe864967b44f97e">layout</a> : <a class="code" href="namespacearm__compute.xhtml#a3a440b3893fa10608d4428958be1c52ea696b031073e74bf2cb98e5ef201d4aa3">DataLayout::UNKNOWN</a>;</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> concat_axis = <a class="code" href="namespacearm__compute_1_1graph.xhtml#a1df15aed3ed531f442ecea2a131d65a4">get_dimension_idx</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, 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="l00387"></a><span class="lineno"> 387</span> </div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ConcatenateLayerFunction>();</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  func->configure(inputs, output, concat_axis);</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span> </div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00393"></a><span class="lineno"> 393</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>(output->info()->data_type());</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  std::ostringstream qss;</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="keywordflow">if</span>(is_quantized)</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>  qss << <span class="stringliteral">" Output QuantInfo: "</span> << output->info()->quantization_info();</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  }</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <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="l00400"></a><span class="lineno"> 400</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</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="l00402"></a><span class="lineno"> 402</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  << <span class="stringliteral">" Data Type: "</span> << output->info()->data_type()</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  << <span class="stringliteral">" Shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  << <span class="stringliteral">" Num Inputs: "</span> << inputs.size()</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  << <span class="stringliteral">" Axis: "</span> << concat_axis</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  << qss.str()</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  << std::endl);</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span> </div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span> }</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span> <span class="comment"></span></div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span> <span class="comment">/** Create a backend convolution layer function</span></div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span> <span class="comment"> *</span></div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span> <span class="comment"> * @tparam ConvolutionLayerFunctions Backend convolution functions</span></div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span> <span class="comment"> *</span></div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> <span class="comment"> * @param[in] ctx Graph context</span></div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span> <span class="comment"> *</span></div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span> <span class="comment"> * @return Backend convolution layer function</span></div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span> <span class="comment"> */</span></div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ConvolutionLayerFunctions, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00424"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a9e38014fa1e7e08dcbf3b5f8c3bdb81e"> 424</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="l00425"></a><span class="lineno"> 425</span> {</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</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="l00427"></a><span class="lineno"> 427</span> </div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(1));</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="keyword">typename</span> TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(2));</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</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="l00433"></a><span class="lineno"> 433</span> </div><div class="line"><a name="l00434"></a><span class="lineno"> 434</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>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type());</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span> </div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <span class="keywordflow">if</span>(is_quantized)</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  {</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  biases->info()->set_data_type(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  }</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span> </div><div class="line"><a name="l00441"></a><span class="lineno"> 441</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#a00525ff582f16038a1d3819aa44a23a3">conv_info</a> = node.<a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer_node.xhtml#ad087f1f9aa1e444236911adf6c57df04">convolution_info</a>();</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> = node.<a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer_node.xhtml#a984881c2c9e6de259af8fcd4ecbc4d80">num_groups</a>();</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute_1_1graph.xhtml#a683661ae75dcb7aef16b9c9bde31517d">ConvolutionMethod</a> conv_algorithm = node.<a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer_node.xhtml#a16b2c6652c4cee5b566daf018f768a42">convolution_method</a>();</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> fast_math = node.<a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer_node.xhtml#a807d0a897f65b2fa1f8ea92892fa2c2a">fast_math_hint</a>() == <a class="code" href="namespacearm__compute_1_1graph.xhtml#ac85a46f3ebd3ab09f576a994ac2dce11a00d23a76e43b46dae9ec7aa9dcbebb32">FastMathHint::Enabled</a>;</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> fused_act = node.<a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer_node.xhtml#a32186582e0a6e02ed7ac3944f60b9c62">fused_activation</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>  <span class="comment">// Create and configure function (we assume that functions have been validated before creation)</span></div><div class="line"><a name="l00448"></a><span class="lineno"> 448</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="l00449"></a><span class="lineno"> 449</span>  std::unique_ptr<IFunction> func;</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  std::string func_name;</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span> </div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="keywordflow">if</span>(conv_algorithm == <a class="code" href="namespacearm__compute_1_1graph.xhtml#a683661ae75dcb7aef16b9c9bde31517da09db1dd1078ec6bdbe2722b4558e578f">ConvolutionMethod::Winograd</a>)</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  {</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <a class="code" href="_error_8h.xhtml#a0b0eb3235749a2909dc5a101afe59a1b">ARM_COMPUTE_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> != 1, <span class="stringliteral">"WinogradConvolutionLayer does not support grouping!"</span>);</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  std::tie(func, func_name) = create_named_memory_managed_function<typename ConvolutionLayerFunctions::WinogradConvolutionLayer>(</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  std::string(<span class="stringliteral">"WinogradConvolutionLayer"</span>), mm,</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, fused_act, fast_math);</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  }</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <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="l00460"></a><span class="lineno"> 460</span>  {</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <a class="code" href="_error_8h.xhtml#a0b0eb3235749a2909dc5a101afe59a1b">ARM_COMPUTE_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> != 1, <span class="stringliteral">"DirectConvolutionLayer does not support grouping!"</span>);</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  std::tie(func, func_name) = create_named_function<typename ConvolutionLayerFunctions::DirectConvolutionLayer>(</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  std::string(<span class="stringliteral">"DirectConvolutionLayer"</span>),</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, fused_act);</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  }</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <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="l00467"></a><span class="lineno"> 467</span>  {</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  std::tie(func, func_name) = create_named_memory_managed_function<typename ConvolutionLayerFunctions::GEMMConvolutionLayer>(</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  std::string(<span class="stringliteral">"GEMMConvolutionLayer"</span>), mm,</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</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_1_1utils_1_1cast.xhtml#a2ea3d1fc01a3a442900249ca182ffa5e">U</a>, 1<a class="code" href="namespacearm__compute_1_1utils_1_1cast.xhtml#a2ea3d1fc01a3a442900249ca182ffa5e">U</a>), fused_act, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>);</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  }</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  {</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  std::tie(func, func_name) = create_named_memory_managed_function<typename ConvolutionLayerFunctions::GenericConvolutionLayer>(</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  std::string(<span class="stringliteral">"GenericConvolutionLayer"</span>), mm,</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</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_1_1utils_1_1cast.xhtml#a2ea3d1fc01a3a442900249ca182ffa5e">U</a>, 1<a class="code" href="namespacearm__compute_1_1utils_1_1cast.xhtml#a2ea3d1fc01a3a442900249ca182ffa5e">U</a>), fused_act, fast_math, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>);</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  }</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> </div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  std::ostringstream qss;</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <span class="keywordflow">if</span>(is_quantized)</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  {</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  qss << <span class="stringliteral">" Input QuantInfo: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->quantization_info()</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  << <span class="stringliteral">" Weights QuantInfo: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ac74736e3863207232a23b7181c1d0f44">quantization_info</a>()</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  << <span class="stringliteral">" Output QuantInfo: "</span> << output->info()->quantization_info();</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  }</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span></div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  << <span class="stringliteral">" Type: "</span> << func_name</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  << <span class="stringliteral">" Groups: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a></div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  << <span class="stringliteral">" Weights shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>()</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  << qss.str()</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</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="l00500"></a><span class="lineno"> 500</span>  << std::endl);</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <span class="keywordflow">return</span> func;</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> <span class="comment"></span></div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span> <span class="comment">/** Create a backend deconvolution layer function</span></div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span> <span class="comment"> *</span></div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span> <span class="comment"> * @tparam DeconvolutionLayerFunction Backend deconvolution function</span></div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span> <span class="comment"> *</span></div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span> <span class="comment"> * @param[in] ctx Graph context</span></div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span> <span class="comment"> *</span></div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span> <span class="comment"> * @return Backend deconvolution layer function</span></div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span> <span class="comment"> */</span></div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span> <span class="keyword">template</span> <<span class="keyword">typename</span> DeconvolutionLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00515"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ac77fa3bf0d7d7c3fde6243192f93f380"> 515</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="l00516"></a><span class="lineno"> 516</span> {</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</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="l00518"></a><span class="lineno"> 518</span> </div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(1));</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <span class="keyword">typename</span> TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(2));</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</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="l00524"></a><span class="lineno"> 524</span> </div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> deconv_info = node.<a class="code" href="classarm__compute_1_1graph_1_1_deconvolution_layer_node.xhtml#ae304796bd723ec2b2d50b88236498bd1">deconvolution_info</a>();</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span> </div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <span class="comment">// Create and configure function (we assume that functions have been validated before creation)</span></div><div class="line"><a name="l00528"></a><span class="lineno"> 528</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="l00529"></a><span class="lineno"> 529</span>  std::unique_ptr<IFunction> func;</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, std::ignore) = create_named_memory_managed_function<DeconvolutionLayerFunction>(</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  std::string(), mm,</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, deconv_info);</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="comment">// Log info</span></div><div class="line"><a name="l00536"></a><span class="lineno"> 536</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="l00537"></a><span class="lineno"> 537</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_deconvolution_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  << <span class="stringliteral">" Weights shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>()</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  << std::endl);</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <span class="keywordflow">return</span> func;</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span> }</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span> <span class="comment"></span></div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span> <span class="comment">/** Create a backend layer depth-wise convolution function</span></div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span> <span class="comment"> *</span></div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span> <span class="comment"> * @tparam DepthwiseConvolutionLayerFunctions Backend depthwise convolution function</span></div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span> <span class="comment"> *</span></div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span> <span class="comment"> *</span></div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span> <span class="comment"> * @return Backend depth-wise convolution layer function</span></div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span> <span class="comment"> */</span></div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span> <span class="keyword">template</span> <<span class="keyword">typename</span> DepthwiseConvolutionLayer, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00558"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ab8ff2a40f95b76ec10ac2a98d1a8d594"> 558</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="l00559"></a><span class="lineno"> 559</span> {</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</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="l00561"></a><span class="lineno"> 561</span> </div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(1));</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <span class="keyword">typename</span> TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(2));</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</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="l00567"></a><span class="lineno"> 567</span> </div><div class="line"><a name="l00568"></a><span class="lineno"> 568</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>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type());</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span> </div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  <span class="keywordflow">if</span>(is_quantized)</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  {</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  biases->info()->set_data_type(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</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> </div><div class="line"><a name="l00575"></a><span class="lineno"> 575</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#a00525ff582f16038a1d3819aa44a23a3">conv_info</a> = node.<a class="code" href="classarm__compute_1_1graph_1_1_depthwise_convolution_layer_node.xhtml#ad087f1f9aa1e444236911adf6c57df04">convolution_info</a>();</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth_multiplier = node.<a class="code" href="classarm__compute_1_1graph_1_1_depthwise_convolution_layer_node.xhtml#a88e38a50a2e964b19521fe8f2e9a144f">depth_multiplier</a>();</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> fused_act = node.<a class="code" href="classarm__compute_1_1graph_1_1_depthwise_convolution_layer_node.xhtml#a32186582e0a6e02ed7ac3944f60b9c62">fused_activation</a>();</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span> </div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  <span class="comment">// Create and configure function (we assume that functions have been validated before creation)</span></div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  std::unique_ptr<IFunction> func;</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  std::string func_name;</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span> </div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  std::tie(func, func_name) = create_named_function<DepthwiseConvolutionLayer>(</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  std::string(<span class="stringliteral">"DepthwiseConvolutionLayer"</span>),</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, depth_multiplier, fused_act);</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span> </div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  std::ostringstream qss;</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  <span class="keywordflow">if</span>(is_quantized)</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  {</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  qss << <span class="stringliteral">" Input QuantInfo: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->quantization_info()</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  << <span class="stringliteral">" Weights QuantInfo: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ac74736e3863207232a23b7181c1d0f44">quantization_info</a>()</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  << <span class="stringliteral">" Output QuantInfo: "</span> << output->info()->quantization_info();</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  }</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <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="l00596"></a><span class="lineno"> 596</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  << <span class="stringliteral">" Type: "</span> << func_name</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  << <span class="stringliteral">" Weights shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>()</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  << <span class="stringliteral">" Depth multiplier: "</span> << depth_multiplier</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  << qss.str()</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</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="l00606"></a><span class="lineno"> 606</span>  << std::endl);</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <span class="keywordflow">return</span> func;</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span> }</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span> <span class="comment"></span></div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span> <span class="comment">/** Create a backend dequantize layer function</span></div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span> <span class="comment"> *</span></div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span> <span class="comment"> * @tparam DequantizationLayer Function Backend dequantize function</span></div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span> <span class="comment"> *</span></div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span> <span class="comment"> *</span></div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span> <span class="comment"> * @return Backend dequantize layer function</span></div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span> <span class="comment"> */</span></div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span> <span class="keyword">template</span> <<span class="keyword">typename</span> DequantizationLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00620"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a751e39ebd690d1cd1027d165cdbe143d"> 620</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a751e39ebd690d1cd1027d165cdbe143d">create_dequantization_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_dequantization_layer_node.xhtml">DequantizationLayerNode</a> &node)</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span> {</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</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="l00623"></a><span class="lineno"> 623</span> </div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</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="l00627"></a><span class="lineno"> 627</span> </div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</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="l00630"></a><span class="lineno"> 630</span> </div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<DequantizationLayerFunction>();</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output);</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span> </div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00636"></a><span class="lineno"> 636</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="l00637"></a><span class="lineno"> 637</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_dequantization_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  << <span class="stringliteral">" Input quantization info: "</span> << output->info()->quantization_info()</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  << std::endl);</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span> </div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span> }<span class="comment"></span></div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span> <span class="comment">/** Create a backend detection output layer function</span></div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span> <span class="comment"> *</span></div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span> <span class="comment"> * @tparam DetectionOutputLayer Function Backend detection output function</span></div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span> <span class="comment"> *</span></div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span> <span class="comment"> *</span></div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span> <span class="comment"> * @return Backend detection output layer function</span></div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span> <span class="comment"> */</span></div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span> <span class="keyword">template</span> <<span class="keyword">typename</span> DetectionOutputLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00658"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#afce1d2d783bb97a3a8c3c406c8cf6b9c"> 658</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#afce1d2d783bb97a3a8c3c406c8cf6b9c">create_detection_output_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_detection_output_layer_node.xhtml">DetectionOutputLayerNode</a> &node)</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>  validate_node<TargetInfo>(node, 3 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span> </div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input0 = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input1 = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(1));</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input2 = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(2));</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</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="l00667"></a><span class="lineno"> 667</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml">DetectionOutputLayerInfo</a> detect_info = node.<a class="code" href="classarm__compute_1_1graph_1_1_detection_output_layer_node.xhtml#a4491336dccd18464fbbf617c981736cf">detection_output_info</a>();</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>(input0 == <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>(input1 == <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>(input2 == <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>(output == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span> </div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<DetectionOutputLayerFunction>();</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  func->configure(input0, input1, input2, output, detect_info);</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span> </div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00679"></a><span class="lineno"> 679</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="l00680"></a><span class="lineno"> 680</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_detection_output_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  << <span class="stringliteral">" Data Type: "</span> << input0->info()->data_type()</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  << <span class="stringliteral">" Input0 shape: "</span> << input0->info()->tensor_shape()</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  << <span class="stringliteral">" Input1 shape: "</span> << input1->info()->tensor_shape()</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  << <span class="stringliteral">" Input2 shape: "</span> << input2->info()->tensor_shape()</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  << <span class="stringliteral">" DetectionOutputLayer info: "</span> << detect_info</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> <span class="comment"></span></div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span> <span class="comment">/** Create a backend detection post process layer function</span></div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span> <span class="comment"> *</span></div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span> <span class="comment"> * @tparam DetectionPostProcessLayerFunction Backend detection output function</span></div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span> <span class="comment"> *</span></div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span> <span class="comment"> *</span></div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span> <span class="comment"> * @return Backend detection post process layer function</span></div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span> <span class="comment"> */</span></div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span> <span class="keyword">template</span> <<span class="keyword">typename</span> DetectionPostProcessLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00704"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a2dbe319a9ac9b6820b2ef5eff8c46ddc"> 704</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a2dbe319a9ac9b6820b2ef5eff8c46ddc">create_detection_post_process_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_detection_post_process_layer_node.xhtml">DetectionPostProcessLayerNode</a> &node)</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span> {</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  validate_node<TargetInfo>(node, 3 <span class="comment">/* expected inputs */</span>, 4 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span> </div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input0 = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input1 = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(1));</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input2 = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(2));</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output0 = 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="l00713"></a><span class="lineno"> 713</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output1 = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a27369471d9b2ba47746e1e923f585b9f">output</a>(1));</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output2 = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a27369471d9b2ba47746e1e923f585b9f">output</a>(2));</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  <span class="keyword">typename</span> TargetInfo::TensorType *output3 = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a27369471d9b2ba47746e1e923f585b9f">output</a>(3));</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_detection_post_process_layer_info.xhtml">DetectionPostProcessLayerInfo</a> detect_info = node.<a class="code" href="classarm__compute_1_1graph_1_1_detection_post_process_layer_node.xhtml#a23ab280af362e61b91763038fc3194f4">detection_post_process_info</a>();</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>  <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="l00719"></a><span class="lineno"> 719</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="l00720"></a><span class="lineno"> 720</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="l00721"></a><span class="lineno"> 721</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(output0 == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(output1 == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(output2 == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(output3 == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span> </div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<DetectionPostProcessLayerFunction>();</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  func->configure(input0, input1, input2, output0, output1, output2, output3, detect_info);</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span> </div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00731"></a><span class="lineno"> 731</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="l00732"></a><span class="lineno"> 732</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_detection_post_process_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  << <span class="stringliteral">" Data Type: "</span> << input0->info()->data_type()</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  << <span class="stringliteral">" Input0 shape: "</span> << input0->info()->tensor_shape()</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  << <span class="stringliteral">" Input1 shape: "</span> << input1->info()->tensor_shape()</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  << <span class="stringliteral">" Input2 shape: "</span> << input2->info()->tensor_shape()</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  << <span class="stringliteral">" Output0 shape: "</span> << output0->info()->tensor_shape()</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  << <span class="stringliteral">" Output1 shape: "</span> << output1->info()->tensor_shape()</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  << <span class="stringliteral">" Output2 shape: "</span> << output2->info()->tensor_shape()</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  << <span class="stringliteral">" Output3 shape: "</span> << output3->info()->tensor_shape()</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  << <span class="stringliteral">" DetectionPostProcessLayer info: "</span> << detect_info</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  << std::endl);</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span> </div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  <span class="keywordflow">return</span> std::move(func);</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> <span class="comment"></span></div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span> <span class="comment">/** Create a backend element-wise operation layer function</span></div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span> <span class="comment"> *</span></div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span> <span class="comment"> * @tparam EltwiseFunctions Backend element-wise function</span></div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span> <span class="comment"> *</span></div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span> <span class="comment"> *</span></div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span> <span class="comment"> * @return Backend element-wise operation layer function</span></div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span> <span class="comment"> */</span></div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span> <span class="keyword">template</span> <<span class="keyword">typename</span> EltwiseFunctions, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00759"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aa37faf92f78c0f5cefe2d43c8bf07f18"> 759</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="l00760"></a><span class="lineno"> 760</span> {</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</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="l00762"></a><span class="lineno"> 762</span> </div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input1 = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input2 = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(1));</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</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="l00767"></a><span class="lineno"> 767</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute_1_1graph.xhtml#aa12973876c037bddff8e9ece94aca0e4">EltwiseOperation</a> eltwise_op = node.<a class="code" href="classarm__compute_1_1graph_1_1_eltwise_layer_node.xhtml#acda6687f669fe87581d7bff8fcd82ebc">eltwise_operation</a>();</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</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.<a class="code" href="classarm__compute_1_1graph_1_1_eltwise_layer_node.xhtml#aa7b3781f10fc0ac73a9a4f748e22d3d4">convert_policy</a>();</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</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="l00770"></a><span class="lineno"> 770</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="l00771"></a><span class="lineno"> 771</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="l00772"></a><span class="lineno"> 772</span> </div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  std::unique_ptr<IFunction> func = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  std::string func_name;</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</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="l00776"></a><span class="lineno"> 776</span>  {</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Addition>(</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  std::string(<span class="stringliteral">"ArithmeticAddition"</span>),</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</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="l00780"></a><span class="lineno"> 780</span>  }</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</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="l00782"></a><span class="lineno"> 782</span>  {</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Subtraction>(</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  std::string(<span class="stringliteral">"ArithmeticSubtraction"</span>),</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</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="l00786"></a><span class="lineno"> 786</span>  }</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</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="l00788"></a><span class="lineno"> 788</span>  {</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Multiplication>(</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  std::string(<span class="stringliteral">"PixelWiseMultiplication"</span>),</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  input1, input2, output, 1.f, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5d58c32bff63e4c34b3234f884a4da58">convert_policy</a>, node.<a class="code" href="classarm__compute_1_1graph_1_1_eltwise_layer_node.xhtml#a0f09377db195c78de49f1d2be26ee649">rounding_policy</a>());</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  }</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  {</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  <a class="code" href="_error_8h.xhtml#a7cf8d8b669b8f7b05680230be30d60f4">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Unsupported element-wise operation!"</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> </div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00799"></a><span class="lineno"> 799</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="l00800"></a><span class="lineno"> 800</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_eltwise_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  << <span class="stringliteral">" Operation: "</span> << func_name</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  << <span class="stringliteral">" Data Type: "</span> << input1->info()->data_type()</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  << <span class="stringliteral">" Shape: "</span> << input1->info()->tensor_shape()</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  << std::endl);</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span> </div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <span class="keywordflow">return</span> func;</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span> }</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span> <span class="comment"></span></div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span> <span class="comment">/** Create a backend flatten layer function</span></div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span> <span class="comment"> *</span></div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span> <span class="comment"> * @tparam FlattenLayerFunction Backend flatten function</span></div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span> <span class="comment"> *</span></div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span> <span class="comment"> *</span></div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span> <span class="comment"> * @return Backend flatten layer function</span></div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span> <span class="comment"> */</span></div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span> <span class="keyword">template</span> <<span class="keyword">typename</span> FlattenLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00821"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a7880c3b249a6dad40da0ebcf6600b0e1"> 821</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="l00822"></a><span class="lineno"> 822</span> {</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</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="l00824"></a><span class="lineno"> 824</span> </div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</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="l00828"></a><span class="lineno"> 828</span> </div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</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="l00831"></a><span class="lineno"> 831</span> </div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<FlattenLayerFunction>();</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output);</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span> </div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00837"></a><span class="lineno"> 837</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="l00838"></a><span class="lineno"> 838</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_flatten_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  << std::endl);</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span> </div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span> }</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span> <span class="comment"></span></div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span> <span class="comment">/** Create a backend fully connected layer function</span></div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span> <span class="comment"> *</span></div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span> <span class="comment"> * @tparam FullyConnectedLayerFunction Backend fully-connected function</span></div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span> <span class="comment"> *</span></div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span> <span class="comment"> * @param[in] ctx Graph context</span></div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span> <span class="comment"> *</span></div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span> <span class="comment"> * @return Backend fully connected layer function</span></div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span> <span class="comment"> */</span></div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span> <span class="keyword">template</span> <<span class="keyword">typename</span> FullyConnectedLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00860"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ac64bbd0df74207f9ab59953e21311178"> 860</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="l00861"></a><span class="lineno"> 861</span> {</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</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="l00863"></a><span class="lineno"> 863</span> </div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(1));</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  <span class="keyword">typename</span> TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(2));</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</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="l00869"></a><span class="lineno"> 869</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml">FullyConnectedLayerInfo</a> fc_info = node.<a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer_node.xhtml#acadd42ba204d72f78bfef07cc4c864ab">info</a>();</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span> </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>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <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>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</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="l00874"></a><span class="lineno"> 874</span> </div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  <span class="keyword">auto</span> wm = <a class="code" href="namespacearm__compute_1_1graph_1_1backends.xhtml#a32d8fea34ca818386a078939a03e3cb8">get_weights_manager</a>(ctx, TargetInfo::TargetType);</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  <span class="keyword">auto</span> 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="l00878"></a><span class="lineno"> 878</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<FullyConnectedLayerFunction>(mm, wm.get());</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, fc_info);</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span> </div><div class="line"><a name="l00881"></a><span class="lineno"> 881</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>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type());</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span> </div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  std::ostringstream qss;</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  <span class="keywordflow">if</span>(is_quantized)</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  {</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  qss << <span class="stringliteral">" Input QuantInfo: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->quantization_info()</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  << <span class="stringliteral">" Weights QuantInfo: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ac74736e3863207232a23b7181c1d0f44">quantization_info</a>()</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  << <span class="stringliteral">" Output QuantInfo: "</span> << output->info()->quantization_info();</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>  <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="l00892"></a><span class="lineno"> 892</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  << qss.str()</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  << <span class="stringliteral">" Weights shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-><a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-><a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>()</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  << std::endl);</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span> </div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  <span class="keywordflow">return</span> std::move(func);</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> <span class="comment"></span></div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span> <span class="comment">/** Create a backend generate proposals layer function</span></div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span> <span class="comment"> *</span></div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span> <span class="comment"> * @tparam GenerateProposalsLayerFunction Backend generate proposals function</span></div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span> <span class="comment"> *</span></div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span> <span class="comment"> * @param[in] ctx Graph context</span></div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span> <span class="comment"> *</span></div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span> <span class="comment"> * @return Backend generate proposals layer function</span></div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span> <span class="comment"> */</span></div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span> <span class="keyword">template</span> <<span class="keyword">typename</span> GenerateProposalsLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00916"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a1a18725e5da4a7ae62c9a3b731ab8fe1"> 916</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="l00917"></a><span class="lineno"> 917</span> {</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</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="l00919"></a><span class="lineno"> 919</span> </div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  <span class="keyword">typename</span> TargetInfo::TensorType *scores = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  <span class="keyword">typename</span> TargetInfo::TensorType *deltas = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(1));</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  <span class="keyword">typename</span> TargetInfo::TensorType *anchors = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(2));</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  <span class="keyword">typename</span> TargetInfo::TensorType *proposals = 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="l00925"></a><span class="lineno"> 925</span>  <span class="keyword">typename</span> TargetInfo::TensorType *scores_out = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a27369471d9b2ba47746e1e923f585b9f">output</a>(1));</div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  <span class="keyword">typename</span> TargetInfo::TensorType *num_valid_proposals = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a27369471d9b2ba47746e1e923f585b9f">output</a>(2));</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</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#a4f4125dba5283887b34f889b1c615c0c">info</a> = node.<a class="code" href="classarm__compute_1_1graph_1_1_generate_proposals_layer_node.xhtml#acfa649555ddb4df4cc5ae52b8205ee5f">info</a>();</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span> </div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  <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="l00930"></a><span class="lineno"> 930</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="l00931"></a><span class="lineno"> 931</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="l00932"></a><span class="lineno"> 932</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="l00933"></a><span class="lineno"> 933</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="l00934"></a><span class="lineno"> 934</span> </div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00936"></a><span class="lineno"> 936</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="l00937"></a><span class="lineno"> 937</span>  func->configure(scores, deltas, anchors, proposals, scores_out, num_valid_proposals, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>);</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span> </div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  <a class="code" href="graph_2_logger_8h.xhtml#ab2d8baa35618bdad1d2814942355311e">ARM_COMPUTE_LOG_GRAPH_INFO</a>(<span class="stringliteral">"Instantiated "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_generate_proposals_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  << <span class="stringliteral">" Target "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  << <span class="stringliteral">" Data Type: "</span> << scores->info()->data_type()</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  << <span class="stringliteral">" Scores shape: "</span> << scores->info()->tensor_shape()</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  << <span class="stringliteral">" Deltas shape: "</span> << deltas->info()->tensor_shape()</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  << <span class="stringliteral">" Anchors shape: "</span> << anchors->info()->tensor_shape()</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  << <span class="stringliteral">" Proposals shape: "</span> << proposals->info()->tensor_shape()</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  << <span class="stringliteral">" Num valid proposals shape: "</span> << num_valid_proposals->info()->tensor_shape()</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  << <span class="stringliteral">" Scores Out shape: "</span> << scores_out->info()->tensor_shape()</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  << std::endl);</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span> </div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span> }</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span> <span class="comment"></span></div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span> <span class="comment">/** Create a backend normalization layer function</span></div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span> <span class="comment"> *</span></div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span> <span class="comment"> * @tparam NormalizationLayerFunction Backend normalization function</span></div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span> <span class="comment"> *</span></div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span> <span class="comment"> * @param[in] ctx Graph context</span></div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span> <span class="comment"> *</span></div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span> <span class="comment"> * @return Backend normalization layer function</span></div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span> <span class="comment"> */</span></div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span> <span class="keyword">template</span> <<span class="keyword">typename</span> NormalizationLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l00965"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a2dfc97df083b68f8409ba21d8a0110d8"> 965</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="l00966"></a><span class="lineno"> 966</span> {</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(ctx);</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span> </div><div class="line"><a name="l00969"></a><span class="lineno"> 969</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="l00970"></a><span class="lineno"> 970</span> </div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</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="l00974"></a><span class="lineno"> 974</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml">NormalizationLayerInfo</a> norm_info = node.<a class="code" href="classarm__compute_1_1graph_1_1_normalization_layer_node.xhtml#a3bfea94983e45ff8d1a3061206593349">normalization_info</a>();</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</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="l00977"></a><span class="lineno"> 977</span> </div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<NormalizationLayerFunction>();</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output, norm_info);</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span> </div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l00983"></a><span class="lineno"> 983</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="l00984"></a><span class="lineno"> 984</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_normalization_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  << <span class="stringliteral">" Normalization info: "</span> << norm_info.type()</div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  << std::endl);</div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span> </div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  <span class="keywordflow">return</span> std::move(func);</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"></span></div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span> <span class="comment">/** Create a backend normalize planar YUV layer function</span></div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span> <span class="comment"> *</span></div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span> <span class="comment"> * @tparam NormalizePlanarYUVLayerFunction Backend normalize planar YUV function</span></div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span> <span class="comment"> *</span></div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span> <span class="comment"> *</span></div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span> <span class="comment"> * @return Backend normalize plnar YUV layer function</span></div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span> <span class="comment"> */</span></div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span> <span class="keyword">template</span> <<span class="keyword">typename</span> NormalizePlanarYUVLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01006"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a92b35a365f58606a13baaf4501d78d9e"> 1006</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="l01007"></a><span class="lineno"> 1007</span> {</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</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="l01009"></a><span class="lineno"> 1009</span> </div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  <span class="keyword">typename</span> TargetInfo::TensorType *mean = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(1));</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  <span class="keyword">typename</span> TargetInfo::TensorType *std = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(2));</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</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="l01015"></a><span class="lineno"> 1015</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</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="l01017"></a><span class="lineno"> 1017</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="l01018"></a><span class="lineno"> 1018</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="l01019"></a><span class="lineno"> 1019</span> </div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<NormalizePlanarYUVLayerFunction>();</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output, mean, std);</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span> </div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</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="l01026"></a><span class="lineno"> 1026</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_normalize_planar_y_u_v_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  << <span class="stringliteral">" Shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  << std::endl);</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="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span> }</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span> <span class="comment"></span></div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span> <span class="comment">/** Create a backend pad layer function</span></div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span> <span class="comment"> *</span></div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span> <span class="comment"> * @tparam PadLayerFunction Backend pad function</span></div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span> <span class="comment"> *</span></div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span> <span class="comment"> *</span></div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span> <span class="comment"> * @return Backend pad layer function</span></div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span> <span class="comment"> */</span></div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span> <span class="keyword">template</span> <<span class="keyword">typename</span> PadLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01046"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a127d0cce11ed3d411eaa5bd25c7a3ac1"> 1046</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="l01047"></a><span class="lineno"> 1047</span> {</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</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="l01049"></a><span class="lineno"> 1049</span> </div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</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="l01053"></a><span class="lineno"> 1053</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.<a class="code" href="classarm__compute_1_1graph_1_1_pad_layer_node.xhtml#af98c64901f2fef6b6e26563bbb358f7e">padding</a>();</div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</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="l01056"></a><span class="lineno"> 1056</span> </div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<PadLayerFunction>();</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">padding</a>);</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">// Log info</span></div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</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="l01063"></a><span class="lineno"> 1063</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_pad_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>  << std::endl);</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="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span> }</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span> <span class="comment"></span></div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span> <span class="comment">/** Create a backend permute layer function</span></div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span> <span class="comment"> *</span></div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span> <span class="comment"> * @tparam PermuteLayerFunction Backend permute function</span></div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span> <span class="comment"> *</span></div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span> <span class="comment"> *</span></div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span> <span class="comment"> * @return Backend permute layer function</span></div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span> <span class="comment"> */</span></div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span> <span class="keyword">template</span> <<span class="keyword">typename</span> PermuteLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01084"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aa65a1becdfa5fc3533d79bba0cd4095c"> 1084</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="l01085"></a><span class="lineno"> 1085</span> {</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</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="l01087"></a><span class="lineno"> 1087</span> </div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</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="l01091"></a><span class="lineno"> 1091</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_strides.xhtml">PermutationVector</a> &perm = node.<a class="code" href="classarm__compute_1_1graph_1_1_permute_layer_node.xhtml#a509cfef89595612c50bce4ef1eae181b">permutation_vector</a>();</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</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="l01094"></a><span class="lineno"> 1094</span> </div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<PermuteLayerFunction>();</div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output, perm);</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span> </div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</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="l01101"></a><span class="lineno"> 1101</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_permute_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>  << <span class="stringliteral">" Permutation vector: "</span> << perm</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>  << std::endl);</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="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span> }</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span> <span class="comment"></span></div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span> <span class="comment">/** Create a backend pooling layer function</span></div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span> <span class="comment"> *</span></div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span> <span class="comment"> * @tparam PoolingLayerFunction Backend pooling function</span></div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span> <span class="comment"> *</span></div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span> <span class="comment"> *</span></div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span> <span class="comment"> * @return Backend pooling layer function</span></div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span> <span class="comment"> */</span></div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span> <span class="keyword">template</span> <<span class="keyword">typename</span> PoolingLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01123"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a0e51b62035e79b0f12964cae17ce0480"> 1123</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="l01124"></a><span class="lineno"> 1124</span> {</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</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="l01126"></a><span class="lineno"> 1126</span> </div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</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="l01130"></a><span class="lineno"> 1130</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a> pool_info = node.<a class="code" href="classarm__compute_1_1graph_1_1_pooling_layer_node.xhtml#a27ad0a381c4ccbc80999d452c4dfe18b">pooling_info</a>();</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</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="l01133"></a><span class="lineno"> 1133</span> </div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<PoolingLayerFunction>();</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output, pool_info);</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span> </div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</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="l01140"></a><span class="lineno"> 1140</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_pooling_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  << <span class="stringliteral">" Pooling info: "</span> << pool_info.pool_type</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  << std::endl);</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="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span> }</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span> <span class="comment"></span></div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span> <span class="comment">/** Create a backend PRelu layer function</span></div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span> <span class="comment"> *</span></div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span> <span class="comment"> * @tparam PReluFunction Backend PRelu function</span></div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span> <span class="comment"> *</span></div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span> <span class="comment"> *</span></div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span> <span class="comment"> * @return Backend PRelu layer function</span></div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span> <span class="comment"> */</span></div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span> <span class="keyword">template</span> <<span class="keyword">typename</span> PReluFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01162"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a6806f347d8b4c0986cdfe4c45918972b"> 1162</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a6806f347d8b4c0986cdfe4c45918972b">create_prelu_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_p_relu_layer_node.xhtml">PReluLayerNode</a> &node)</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span> {</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</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="l01165"></a><span class="lineno"> 1165</span> </div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3161c2c93c655dd30953372064ec627b">alpha</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(1));</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</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="l01170"></a><span class="lineno"> 1170</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span> || <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3161c2c93c655dd30953372064ec627b">alpha</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</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="l01172"></a><span class="lineno"> 1172</span> </div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<PReluFunction>();</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3161c2c93c655dd30953372064ec627b">alpha</a>, output);</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span> </div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</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="l01179"></a><span class="lineno"> 1179</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_p_relu_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  << std::endl);</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span> </div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span> }</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span> <span class="comment"></span></div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span> <span class="comment">/** Create a backend print layer function</span></div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span> <span class="comment"> *</span></div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span> <span class="comment"> *</span></div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span> <span class="comment"> *</span></div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span> <span class="comment"> * @return Backend print layer function</span></div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span> <span class="comment"> */</span></div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span> <span class="keyword">template</span> <<span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01199"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a5567ed5ad9c8fb45d2748bab27163530"> 1199</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a5567ed5ad9c8fb45d2748bab27163530">create_print_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_print_layer_node.xhtml">PrintLayerNode</a> &node)</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span> {</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  validate_node<TargetInfo>(node, 1 <span class="comment">/* expected inputs */</span>, 1 <span class="comment">/* expected outputs */</span>);</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span> </div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>);</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span> </div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</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="l01209"></a><span class="lineno"> 1209</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_print_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>  << std::endl);</div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span> </div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  <span class="keywordflow">return</span> <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span> }</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span> <span class="comment"></span></div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span> <span class="comment">/** Create a backend priorbox layer function</span></div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span> <span class="comment"> *</span></div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span> <span class="comment"> * @tparam PriorBoxLayerFunction Backend priorbox function</span></div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span> <span class="comment"> *</span></div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span> <span class="comment"> *</span></div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span> <span class="comment"> * @return Backend priorbox layer function</span></div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span> <span class="comment"> */</span></div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span> <span class="keyword">template</span> <<span class="keyword">typename</span> PriorBoxLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01229"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#aaf28fee838c38cc4da407a4dbc62f7b0"> 1229</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="l01230"></a><span class="lineno"> 1230</span> {</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</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="l01232"></a><span class="lineno"> 1232</span> </div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input0 = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  <span class="keyword">typename</span> TargetInfo::TensorType *input1 = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(1));</div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</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="l01237"></a><span class="lineno"> 1237</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_prior_box_layer_info.xhtml">PriorBoxLayerInfo</a> prior_info = node.<a class="code" href="classarm__compute_1_1graph_1_1_prior_box_layer_node.xhtml#a0f62f59c57a7cdbdc20f7d850f1dfd8c">priorbox_info</a>();</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</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="l01239"></a><span class="lineno"> 1239</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="l01240"></a><span class="lineno"> 1240</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="l01241"></a><span class="lineno"> 1241</span> </div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<PriorBoxLayerFunction>();</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  func->configure(input0, input1, output, prior_info);</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span> </div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</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="l01248"></a><span class="lineno"> 1248</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_prior_box_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  << <span class="stringliteral">" Data Type: "</span> << input0->info()->data_type()</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>  << <span class="stringliteral">" Input0 shape: "</span> << input0->info()->tensor_shape()</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>  << <span class="stringliteral">" Input1 shape: "</span> << input1->info()->tensor_shape()</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>  << <span class="stringliteral">" PriorBoxLayer info: "</span> << prior_info</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  << std::endl);</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span> </div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span> }</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span> <span class="comment"></span></div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span> <span class="comment">/** Create a backend quantization layer function</span></div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span> <span class="comment"> *</span></div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span> <span class="comment"> * @tparam QuantizationLayerFunction Backend quantization function</span></div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span> <span class="comment"> *</span></div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span> <span class="comment"> *</span></div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span> <span class="comment"> * @return Backend quantization layer function</span></div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span> <span class="comment"> */</span></div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span> <span class="keyword">template</span> <<span class="keyword">typename</span> QuantizationLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01271"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a31be99a5d0f75045fc411e211824baad"> 1271</a></span> std::unique_ptr<IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a31be99a5d0f75045fc411e211824baad">create_quantization_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_quantization_layer_node.xhtml">QuantizationLayerNode</a> &node)</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span> {</div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</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="l01274"></a><span class="lineno"> 1274</span> </div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</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="l01278"></a><span class="lineno"> 1278</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</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="l01280"></a><span class="lineno"> 1280</span> </div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<QuantizationLayerFunction>();</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output);</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span> </div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</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="l01287"></a><span class="lineno"> 1287</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_quantization_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>  << std::endl);</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span> </div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span> }</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span> <span class="comment"></span></div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span> <span class="comment">/** Create a backend reorg layer function</span></div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span> <span class="comment"> *</span></div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span> <span class="comment"> * @tparam ReorgLayerFunction Backend reorg function</span></div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span> <span class="comment"> *</span></div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span> <span class="comment"> *</span></div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span> <span class="comment"> * @return Backend reshape layer function</span></div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span> <span class="comment"> */</span></div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ReorgLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01308"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a0331e6b2b68ea76e9415f7f148d92601"> 1308</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="l01309"></a><span class="lineno"> 1309</span> {</div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</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="l01311"></a><span class="lineno"> 1311</span> </div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</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="l01315"></a><span class="lineno"> 1315</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</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="l01317"></a><span class="lineno"> 1317</span> </div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ReorgLayerFunction>();</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output, node.<a class="code" href="classarm__compute_1_1graph_1_1_reorg_layer_node.xhtml#a47d010db0ab9940009209db7cf529f36">stride</a>());</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span> </div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</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="l01324"></a><span class="lineno"> 1324</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_reorg_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>  << std::endl);</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span> </div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span> }</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span> <span class="comment"></span></div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span> <span class="comment">/** Create a backend reshape layer function</span></div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span> <span class="comment"> *</span></div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span> <span class="comment"> * @tparam ReshapeLayerFunction Backend reshape function</span></div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span> <span class="comment"> *</span></div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span> <span class="comment"> *</span></div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span> <span class="comment"> * @return Backend reshape layer function</span></div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span> <span class="comment"> */</span></div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ReshapeLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01345"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ad1922deea021647290d0c206723e6c73"> 1345</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="l01346"></a><span class="lineno"> 1346</span> {</div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</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="l01348"></a><span class="lineno"> 1348</span> </div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</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="l01352"></a><span class="lineno"> 1352</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</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="l01354"></a><span class="lineno"> 1354</span> </div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ReshapeLayerFunction>();</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output);</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span> </div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</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="l01361"></a><span class="lineno"> 1361</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_reshape_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>  << std::endl);</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span> </div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span> }</div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span> <span class="comment"></span></div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span> <span class="comment">/** Create a backend resize layer function</span></div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span> <span class="comment"> *</span></div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span> <span class="comment"> * @tparam ResizeLayerFunction Backend resize function</span></div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span> <span class="comment"> *</span></div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span> <span class="comment"> *</span></div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span> <span class="comment"> * @return Backend resize layer function</span></div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span> <span class="comment"> */</span></div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ResizeLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01382"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#adb3a9be16de941b0f601e16c8ac76533"> 1382</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="l01383"></a><span class="lineno"> 1383</span> {</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</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="l01385"></a><span class="lineno"> 1385</span> </div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</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="l01389"></a><span class="lineno"> 1389</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</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="l01391"></a><span class="lineno"> 1391</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.<a class="code" href="classarm__compute_1_1graph_1_1_resize_layer_node.xhtml#a718c049decea6397c493df9cb2f581da">policy</a>();</div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span> </div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ResizeLayerFunction>();</div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad986b73e9d5f47a623a9b6d773c25e34">policy</a>, <a class="code" href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92">BorderMode::CONSTANT</a>);</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span> </div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</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="l01399"></a><span class="lineno"> 1399</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_resize_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>  << <span class="stringliteral">" Interpolation: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad986b73e9d5f47a623a9b6d773c25e34">policy</a></div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>  << std::endl);</div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span> </div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span> }</div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span> <span class="comment"></span></div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span> <span class="comment">/** Create a backend ROI align layer function</span></div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span> <span class="comment"> *</span></div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span> <span class="comment"> * @tparam ROIAlignLayerFunction ROI Align function</span></div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span> <span class="comment"> *</span></div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span> <span class="comment"> *</span></div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span> <span class="comment"> * @return ROI Align layer function</span></div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span> <span class="comment"> */</span></div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ROIAlignLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01421"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#adef77d1f64203fe2828b3f992c87f5df"> 1421</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="l01422"></a><span class="lineno"> 1422</span> {</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</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="l01424"></a><span class="lineno"> 1424</span> </div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>  <span class="keyword">typename</span> TargetInfo::TensorType *rois = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(1));</div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</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="l01429"></a><span class="lineno"> 1429</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</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="l01431"></a><span class="lineno"> 1431</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="l01432"></a><span class="lineno"> 1432</span> </div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</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.<a class="code" href="classarm__compute_1_1graph_1_1_r_o_i_align_layer_node.xhtml#a51a2c95a0b98cf92e99d06672db84060">pooling_info</a>();</div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span> </div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<ROIAlignLayerFunction>();</div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span> </div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, rois, output, pool_info);</div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span> </div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</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="l01442"></a><span class="lineno"> 1442</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_r_o_i_align_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span>  << <span class="stringliteral">" ROIs shape: "</span> << rois->info()->tensor_shape()</div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</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="l01450"></a><span class="lineno"> 1450</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="l01451"></a><span class="lineno"> 1451</span>  << std::endl);</div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span> </div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span> }</div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span> <span class="comment"></span></div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span> <span class="comment">/** Create a backend slice layer function</span></div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span> <span class="comment"> *</span></div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span> <span class="comment"> * @tparam SliceLayerFunction Backend slice function</span></div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span> <span class="comment"> *</span></div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span> <span class="comment"> *</span></div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span> <span class="comment"> * @return Backend slice layer function</span></div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span> <span class="comment"> */</span></div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span> <span class="keyword">template</span> <<span class="keyword">typename</span> SliceLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01466"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a69a819dc92f559ed0b788392391da602"> 1466</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="l01467"></a><span class="lineno"> 1467</span> {</div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</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="l01469"></a><span class="lineno"> 1469</span> </div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</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="l01473"></a><span class="lineno"> 1473</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</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="l01475"></a><span class="lineno"> 1475</span> </div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<SliceLayerFunction>();</div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output, node.<a class="code" href="classarm__compute_1_1graph_1_1_slice_layer_node.xhtml#a476f08a0729f8861bec63b7e62c7b514">starts</a>(), node.<a class="code" href="classarm__compute_1_1graph_1_1_slice_layer_node.xhtml#a836dbfcb541878d045cac29f9b35d5cb">ends</a>());</div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span> </div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</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="l01482"></a><span class="lineno"> 1482</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_slice_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l01486"></a><span class="lineno"> 1486</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>  << std::endl);</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span> </div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span> }</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span> <span class="comment"></span></div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span> <span class="comment">/** Create a backend softmax layer function</span></div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span> <span class="comment"> *</span></div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span> <span class="comment"> * @tparam SoftmaxLayerFunction Backend softmax function</span></div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span> <span class="comment"> *</span></div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span> <span class="comment"> * @param[in] ctx Graph context</span></div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span> <span class="comment"> *</span></div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span> <span class="comment"> * @return Backend softmax layer function</span></div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span> <span class="comment"> */</span></div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span> <span class="keyword">template</span> <<span class="keyword">typename</span> SoftmaxLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01504"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a5271c97b6bef5972c5e259307d52a4da"> 1504</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="l01505"></a><span class="lineno"> 1505</span> {</div><div class="line"><a name="l01506"></a><span class="lineno"> 1506</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="l01507"></a><span class="lineno"> 1507</span> </div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</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="l01511"></a><span class="lineno"> 1511</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> beta = node.<a class="code" href="classarm__compute_1_1graph_1_1_softmax_layer_node.xhtml#a01524f49fa1d982d4382e390bef91de7">beta</a>();</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</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="l01514"></a><span class="lineno"> 1514</span> </div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</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="l01517"></a><span class="lineno"> 1517</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output, beta);</div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span> </div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</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="l01521"></a><span class="lineno"> 1521</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_softmax_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01524"></a><span class="lineno"> 1524</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span>  << std::endl);</div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span> </div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span> }</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span> <span class="comment"></span></div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span> <span class="comment">/** Create a backend layer stack function</span></div><div class="line"><a name="l01533"></a><span class="lineno"> 1533</span> <span class="comment"> *</span></div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span> <span class="comment"> * @tparam StackLayerFunction Backend stack function</span></div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span> <span class="comment"> *</span></div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span> <span class="comment"> *</span></div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span> <span class="comment"> * @return Backend stack layer function</span></div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span> <span class="comment"> */</span></div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span> <span class="keyword">template</span> <<span class="keyword">typename</span> StackLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01542"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ae157076aa533f9db0960dea1d5fc5014"> 1542</a></span> std::unique_ptr<arm_compute::IFunction> <a class="code" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ae157076aa533f9db0960dea1d5fc5014">create_stack_layer</a>(<a class="code" href="classarm__compute_1_1graph_1_1_stack_layer_node.xhtml">StackLayerNode</a> &node)</div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span> {</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>  <a class="code" href="graph_2_logger_8h.xhtml#a300d153929a99c7b571d4cda3f7987a5">ARM_COMPUTE_LOG_GRAPH_VERBOSE</a>(<span class="stringliteral">"Creating Stack 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="l01545"></a><span class="lineno"> 1545</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="l01546"></a><span class="lineno"> 1546</span> </div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>  std::vector<typename TargetInfo::TensorType *> inputs;</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</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="l01550"></a><span class="lineno"> 1550</span>  {</div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</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="l01552"></a><span class="lineno"> 1552</span>  }</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</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="l01554"></a><span class="lineno"> 1554</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a> = node.<a class="code" href="classarm__compute_1_1graph_1_1_stack_layer_node.xhtml#a18ffaef3d2889fbba089ffbf7ea2f12d">axis</a>();</div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span> </div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<StackLayerFunction>();</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>  func->configure(inputs, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>, output);</div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span> </div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</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="l01562"></a><span class="lineno"> 1562</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_stack_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>  << <span class="stringliteral">" Data Type: "</span> << output->info()->data_type()</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>  << <span class="stringliteral">" Inputs shape: "</span> << inputs[0]->info()->tensor_shape()</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span>  << <span class="stringliteral">" Num Inputs: "</span> << inputs.size()</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>  << <span class="stringliteral">" Axis: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a></div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>  << std::endl);</div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span> </div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span> }<span class="comment"></span></div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span> <span class="comment">/** Create a backend Upsample layer function</span></div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span> <span class="comment"> *</span></div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span> <span class="comment"> * @tparam UpsampleLayerFunction Backend Upsample function</span></div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span> <span class="comment"> *</span></div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span> <span class="comment"> * @param[in] ctx Graph context</span></div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span> <span class="comment"> *</span></div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</span> <span class="comment"> * @return Backend Upsample layer function</span></div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span> <span class="comment"> */</span></div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span> <span class="keyword">template</span> <<span class="keyword">typename</span> UpsampleLayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01585"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#acd9d23be81ad915ff875876c6606f576"> 1585</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="l01586"></a><span class="lineno"> 1586</span> {</div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(ctx);</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</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="l01589"></a><span class="lineno"> 1589</span> </div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</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="l01593"></a><span class="lineno"> 1593</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#a4f4125dba5283887b34f889b1c615c0c">info</a> = node.<a class="code" href="classarm__compute_1_1graph_1_1_upsample_layer_node.xhtml#aa7260d3e156a747cfa5ea5f2173ef3df">info</a>();</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a966a9c417ce5e94dca08d9b5e745c0c9">InterpolationPolicy</a> upsampling_policy = node.<a class="code" href="classarm__compute_1_1graph_1_1_upsample_layer_node.xhtml#a8e71fc913825230e6e26af0242260ce4">upsampling_policy</a>();</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</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="l01596"></a><span class="lineno"> 1596</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.x() != 2 || <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.y() != 2);</div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01598"></a><span class="lineno"> 1598</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="l01599"></a><span class="lineno"> 1599</span> </div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<UpsampleLayerFunction>();</div><div class="line"><a name="l01602"></a><span class="lineno"> 1602</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>, upsampling_policy);</div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</span> </div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</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="l01606"></a><span class="lineno"> 1606</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_upsample_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01612"></a><span class="lineno"> 1612</span>  << <span class="stringliteral">" Strides: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a></div><div class="line"><a name="l01613"></a><span class="lineno"> 1613</span>  << <span class="stringliteral">" Upsampling policy: "</span> << upsampling_policy</div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>  << std::endl);</div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span> </div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01617"></a><span class="lineno"> 1617</span> }<span class="comment"></span></div><div class="line"><a name="l01618"></a><span class="lineno"> 1618</span> <span class="comment">/** Create a backend YOLO layer function</span></div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span> <span class="comment"> *</span></div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span> <span class="comment"> * @tparam YoloLayerFunction Backend YOLO function</span></div><div class="line"><a name="l01621"></a><span class="lineno"> 1621</span> <span class="comment"> * @tparam TargetInfo Target-specific information</span></div><div class="line"><a name="l01622"></a><span class="lineno"> 1622</span> <span class="comment"> *</span></div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span> <span class="comment"> * @param[in] node Node to create the backend function for</span></div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span> <span class="comment"> * @param[in] ctx Graph context</span></div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span> <span class="comment"> *</span></div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span> <span class="comment"> * @return Backend YOLO layer function</span></div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span> <span class="comment"> */</span></div><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span> <span class="keyword">template</span> <<span class="keyword">typename</span> YOLOlayerFunction, <span class="keyword">typename</span> TargetInfo></div><div class="line"><a name="l01629"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a7db148217bc0f1f5a4adf6194c858d24"> 1629</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="l01630"></a><span class="lineno"> 1630</span> {</div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(ctx);</div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</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="l01633"></a><span class="lineno"> 1633</span> </div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span>  <span class="comment">// Extract IO and info</span></div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>  <span class="keyword">typename</span> TargetInfo::TensorType *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> = get_backing_tensor<TargetInfo>(node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#ad7c09b0faaf3c808b0489012204852a9">input</a>(0));</div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</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="l01637"></a><span class="lineno"> 1637</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a> = node.<a class="code" href="classarm__compute_1_1graph_1_1_y_o_l_o_layer_node.xhtml#a39a8dd296461705ee5cb54eacb4b2818">activation_info</a>();</div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span>  <span class="keyword">const</span> int32_t num_classes = node.<a class="code" href="classarm__compute_1_1graph_1_1_y_o_l_o_layer_node.xhtml#adb962dd0e5ae700a7ab8b64a437aea2a">num_classes</a>();</div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(num_classes <= 0);</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</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="l01642"></a><span class="lineno"> 1642</span> </div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span>  <span class="comment">// Create and configure function</span></div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span>  <span class="keyword">auto</span> func = support::cpp14::make_unique<YOLOlayerFunction>();</div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span>  func->configure(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, num_classes);</div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span> </div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>  <span class="comment">// Log info</span></div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</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="l01649"></a><span class="lineno"> 1649</span>  << node.<a class="code" href="classarm__compute_1_1graph_1_1_i_node.xhtml#a1d89c28bd42ba9a52da008bb69367171">name</a>()</div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>  << <span class="stringliteral">" Type: "</span> << node.<a class="code" href="classarm__compute_1_1graph_1_1_y_o_l_o_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">type</a>()</div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>  << <span class="stringliteral">" Target: "</span> << TargetInfo::TargetType</div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>  << <span class="stringliteral">" Data Type: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type()</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span>  << <span class="stringliteral">" Input shape: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->tensor_shape()</div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>  << <span class="stringliteral">" Output shape: "</span> << output->info()->tensor_shape()</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>  << <span class="stringliteral">" Activation function: "</span> << <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.activation()</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span>  << <span class="stringliteral">" Num classes: "</span> << num_classes</div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>  << std::endl);</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span> </div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>  <span class="keywordflow">return</span> std::move(func);</div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span> }</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span> } <span class="comment">// namespace detail</span></div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span> } <span class="comment">// namespace backends</span></div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</span> } <span class="comment">// namespace graph</span></div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span> } <span class="comment">// namespace arm_compute</span></div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span> </div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</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="classarm__compute_1_1graph_1_1_generate_proposals_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_generate_proposals_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::GenerateProposalsLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_generate_proposals_layer_node_8cpp_source.xhtml#l00092">GenerateProposalsLayerNode.cpp:92</a></div></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#l00965">FunctionHelpers.h:965</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a751e39ebd690d1cd1027d165cdbe143d"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a751e39ebd690d1cd1027d165cdbe143d">arm_compute::graph::backends::detail::create_dequantization_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_dequantization_layer(DequantizationLayerNode &node)</div><div class="ttdoc">Create a backend dequantize layer function.</div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00620">FunctionHelpers.h:620</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'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="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_normalization_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_normalization_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::NormalizationLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_normalization_layer_node_8cpp_source.xhtml#l00069">NormalizationLayerNode.cpp:69</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_eltwise_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_eltwise_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::EltwiseLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_eltwise_layer_node_8cpp_source.xhtml#l00077">EltwiseLayerNode.cpp:77</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#l00366">Types.h:366</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_1graph_1_1_quantization_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_quantization_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::QuantizationLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_layer_node_8cpp_source.xhtml#l00067">QuantizationLayerNode.cpp:67</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::FusedConvolutionBatchNormalizationNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_fused_convolution_batch_normalization_node_8cpp_source.xhtml#l00139">FusedConvolutionBatchNormalizationNode.cpp:139</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#l01417">Types.h:1417</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acf5f12bbab64dd614bd8220c97fe484f"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">arm_compute::test::validation::data_layout</a></div><div class="ttdeci">const DataLayout data_layout</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00146">Im2Col.cpp:146</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_addition_8cpp_source.xhtml#l00139">ArithmeticAddition.cpp:139</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#l00100">Types.h:100</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a683661ae75dcb7aef16b9c9bde31517dafd1dd0c603be8170f9eae0be9f2f6afb"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a683661ae75dcb7aef16b9c9bde31517dafd1dd0c603be8170f9eae0be9f2f6afb">arm_compute::graph::ConvolutionMethod::Direct</a></div><div class="ttdoc">Deep direct convolution.</div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_bounding_box_transform_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_bounding_box_transform_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::BoundingBoxTransformLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_bounding_box_transform_layer_node_8cpp_source.xhtml#l00071">BoundingBoxTransformLayerNode.cpp:71</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_print_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_print_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::PrintLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_print_layer_node_8cpp_source.xhtml#l00081">PrintLayerNode.cpp:81</a></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#l01466">FunctionHelpers.h:1466</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_fused_depthwise_convolution_batch_normalization_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fused_depthwise_convolution_batch_normalization_node.xhtml">arm_compute::graph::FusedDepthwiseConvolutionBatchNormalizationNode</a></div><div class="ttdoc">Fused Depthwise Convolution Batch Normalization node.</div><div class="ttdef"><b>Definition:</b> <a href="_fused_depthwise_convolution_batch_normalization_node_8h_source.xhtml#l00034">FusedDepthwiseConvolutionBatchNormalizationNode.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_reorg_layer_node_xhtml_a47d010db0ab9940009209db7cf529f36"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_reorg_layer_node.xhtml#a47d010db0ab9940009209db7cf529f36">arm_compute::graph::ReorgLayerNode::stride</a></div><div class="ttdeci">int stride() const</div><div class="ttdoc">Stride value to use for reorganizing the values in the output tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_reorg_layer_node_8cpp_source.xhtml#l00041">ReorgLayerNode.cpp:41</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="classarm__compute_1_1_c_l_tensor_xhtml_ad45f0c01a0713dfb6bd7232c7f396fc4"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">arm_compute::CLTensor::info</a></div><div class="ttdeci">TensorInfo * info() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00041">CLTensor.cpp:41</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_softmax_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_softmax_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::SoftmaxLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_softmax_layer_node_8cpp_source.xhtml#l00072">SoftmaxLayerNode.cpp:72</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_eltwise_layer_node_xhtml_a0f09377db195c78de49f1d2be26ee649"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_eltwise_layer_node.xhtml#a0f09377db195c78de49f1d2be26ee649">arm_compute::graph::EltwiseLayerNode::rounding_policy</a></div><div class="ttdeci">RoundingPolicy rounding_policy() const</div><div class="ttdoc">Rounding policy accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_eltwise_layer_node_8cpp_source.xhtml#l00050">EltwiseLayerNode.cpp:50</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_r_o_i_align_layer_node_xhtml_a51a2c95a0b98cf92e99d06672db84060"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_r_o_i_align_layer_node.xhtml#a51a2c95a0b98cf92e99d06672db84060">arm_compute::graph::ROIAlignLayerNode::pooling_info</a></div><div class="ttdeci">const ROIPoolingLayerInfo & pooling_info() const</div><div class="ttdoc">ROIPoolingLayerInfo accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_r_o_i_align_layer_node_8cpp_source.xhtml#l00043">ROIAlignLayerNode.cpp:43</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#l00759">FunctionHelpers.h:759</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#l01664">Types.h:1664</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_depthwise_convolution_layer_node_xhtml_a32186582e0a6e02ed7ac3944f60b9c62"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_depthwise_convolution_layer_node.xhtml#a32186582e0a6e02ed7ac3944f60b9c62">arm_compute::graph::DepthwiseConvolutionLayerNode::fused_activation</a></div><div class="ttdeci">ActivationLayerInfo fused_activation() const</div><div class="ttdoc">Returns fused activation.</div><div class="ttdef"><b>Definition:</b> <a href="_depthwise_convolution_layer_node_8cpp_source.xhtml#l00063">DepthwiseConvolutionLayerNode.cpp:63</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a1f8aca235c095df227e7444f6b237eb1"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">arm_compute::test::validation::act_info</a></div><div class="ttdeci">act_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00183">ConvolutionLayer.cpp:183</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#l00455">Types.h:455</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#l00137">FunctionHelpers.h:137</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_upsample_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_upsample_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::UpsampleLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_upsample_layer_node_8cpp_source.xhtml#l00088">UpsampleLayerNode.cpp:88</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_convolution_layer_node_xhtml_a807d0a897f65b2fa1f8ea92892fa2c2a"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_convolution_layer_node.xhtml#a807d0a897f65b2fa1f8ea92892fa2c2a">arm_compute::graph::ConvolutionLayerNode::fast_math_hint</a></div><div class="ttdeci">FastMathHint fast_math_hint() const</div><div class="ttdoc">Fast math hint accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_convolution_layer_node_8cpp_source.xhtml#l00061">ConvolutionLayerNode.cpp:61</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a7cf8d8b669b8f7b05680230be30d60f4"><div class="ttname"><a href="_error_8h.xhtml#a7cf8d8b669b8f7b05680230be30d60f4">ARM_COMPUTE_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR(msg)</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#l00352">Error.h:352</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node_xhtml_acd39aa81617eab4d3482fa904d5dee87"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node.xhtml#acd39aa81617eab4d3482fa904d5dee87">arm_compute::graph::FusedConvolutionBatchNormalizationNode::epsilon</a></div><div class="ttdeci">float epsilon() const</div><div class="ttdoc">Epsilon parameter accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_fused_convolution_batch_normalization_node_8cpp_source.xhtml#l00051">FusedConvolutionBatchNormalizationNode.cpp:51</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_fused_depthwise_convolution_batch_normalization_node_xhtml_ad087f1f9aa1e444236911adf6c57df04"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fused_depthwise_convolution_batch_normalization_node.xhtml#ad087f1f9aa1e444236911adf6c57df04">arm_compute::graph::FusedDepthwiseConvolutionBatchNormalizationNode::convolution_info</a></div><div class="ttdeci">PadStrideInfo convolution_info() const</div><div class="ttdoc">Convolution metadata accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_fused_depthwise_convolution_batch_normalization_node_8cpp_source.xhtml#l00061">FusedDepthwiseConvolutionBatchNormalizationNode.cpp:61</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a7f44d10197128d3f478626b5c68b3c35"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a7f44d10197128d3f478626b5c68b3c35">arm_compute::graph::backends::detail::create_fused_convolution_batch_normalization_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_fused_convolution_batch_normalization_layer(FusedConvolutionBatchNormalizationNode &node, GraphContext &ctx)</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#l00182">FunctionHelpers.h:182</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_deconvolution_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_deconvolution_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::DeconvolutionLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_deconvolution_layer_node_8cpp_source.xhtml#l00094">DeconvolutionLayerNode.cpp:94</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="classarm__compute_1_1graph_1_1_depthwise_convolution_layer_node_xhtml_a88e38a50a2e964b19521fe8f2e9a144f"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_depthwise_convolution_layer_node.xhtml#a88e38a50a2e964b19521fe8f2e9a144f">arm_compute::graph::DepthwiseConvolutionLayerNode::depth_multiplier</a></div><div class="ttdeci">int depth_multiplier() const</div><div class="ttdoc">Depth multiplier accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_depthwise_convolution_layer_node_8cpp_source.xhtml#l00043">DepthwiseConvolutionLayerNode.cpp:43</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_fused_depthwise_convolution_batch_normalization_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fused_depthwise_convolution_batch_normalization_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::FusedDepthwiseConvolutionBatchNormalizationNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_fused_depthwise_convolution_batch_normalization_node_8cpp_source.xhtml#l00130">FusedDepthwiseConvolutionBatchNormalizationNode.cpp:130</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a6806f347d8b4c0986cdfe4c45918972b"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a6806f347d8b4c0986cdfe4c45918972b">arm_compute::graph::backends::detail::create_prelu_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_prelu_layer(PReluLayerNode &node)</div><div class="ttdoc">Create a backend PRelu layer function.</div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l01162">FunctionHelpers.h:1162</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_fused_depthwise_convolution_batch_normalization_node_xhtml_ac8cef0874f04203401b5d7f5a6fa2a34"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fused_depthwise_convolution_batch_normalization_node.xhtml#ac8cef0874f04203401b5d7f5a6fa2a34">arm_compute::graph::FusedDepthwiseConvolutionBatchNormalizationNode::depth_multiplier</a></div><div class="ttdeci">unsigned int depth_multiplier() const</div><div class="ttdoc">Depth multiplier accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_fused_depthwise_convolution_batch_normalization_node_8cpp_source.xhtml#l00066">FusedDepthwiseConvolutionBatchNormalizationNode.cpp:66</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a00525ff582f16038a1d3819aa44a23a3"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">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#l00597">Winograd.cpp:597</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_channel_shuffle_layer_node_xhtml_a984881c2c9e6de259af8fcd4ecbc4d80"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_channel_shuffle_layer_node.xhtml#a984881c2c9e6de259af8fcd4ecbc4d80">arm_compute::graph::ChannelShuffleLayerNode::num_groups</a></div><div class="ttdeci">unsigned int num_groups() const</div><div class="ttdoc">Number of groups accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_channel_shuffle_layer_node_8cpp_source.xhtml#l00040">ChannelShuffleLayerNode.cpp:40</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#l00558">FunctionHelpers.h:558</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="classarm__compute_1_1graph_1_1_fully_connected_layer_node_xhtml_acadd42ba204d72f78bfef07cc4c864ab"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fully_connected_layer_node.xhtml#acadd42ba204d72f78bfef07cc4c864ab">arm_compute::graph::FullyConnectedLayerNode::info</a></div><div class="ttdeci">FullyConnectedLayerInfo info() const</div><div class="ttdoc">Fully connected layer addition information.</div><div class="ttdef"><b>Definition:</b> <a href="src_2graph_2nodes_2_fully_connected_layer_8cpp_source.xhtml#l00100">FullyConnectedLayer.cpp:100</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_y_o_l_o_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_y_o_l_o_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::YOLOLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_y_o_l_o_layer_node_8cpp_source.xhtml#l00074">YOLOLayerNode.cpp:74</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_ac74736e3863207232a23b7181c1d0f44"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#ac74736e3863207232a23b7181c1d0f44">arm_compute::TensorInfo::quantization_info</a></div><div class="ttdeci">QuantizationInfo quantization_info() const override</div><div class="ttdoc">Get the quantization settings (scale and offset) of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00311">TensorInfo.h:311</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#l01382">FunctionHelpers.h:1382</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1backends_1_1_fused_convolution_batch_normalization_function_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1backends_1_1_fused_convolution_batch_normalization_function.xhtml">arm_compute::graph::backends::FusedConvolutionBatchNormalizationFunction</a></div><div class="ttdoc">Wrapper function to first apply {NE, CL}BatchNormalizationLayer on the weights and then run {NE,...</div><div class="ttdef"><b>Definition:</b> <a href="_fused_convolution_batch_normalization_function_8h_source.xhtml#l00039">FusedConvolutionBatchNormalizationFunction.h:39</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#l01677">Types.h:1677</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#l01084">FunctionHelpers.h:1084</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_convolution_layer_node_xhtml_a32186582e0a6e02ed7ac3944f60b9c62"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_convolution_layer_node.xhtml#a32186582e0a6e02ed7ac3944f60b9c62">arm_compute::graph::ConvolutionLayerNode::fused_activation</a></div><div class="ttdeci">ActivationLayerInfo fused_activation() const</div><div class="ttdoc">Returns fused activation.</div><div class="ttdef"><b>Definition:</b> <a href="_convolution_layer_node_8cpp_source.xhtml#l00076">ConvolutionLayerNode.cpp:76</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_deconvolution_layer_node_xhtml_ae304796bd723ec2b2d50b88236498bd1"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_deconvolution_layer_node.xhtml#ae304796bd723ec2b2d50b88236498bd1">arm_compute::graph::DeconvolutionLayerNode::deconvolution_info</a></div><div class="ttdeci">PadStrideInfo deconvolution_info() const</div><div class="ttdoc">Deconvolution metadata accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_deconvolution_layer_node_8cpp_source.xhtml#l00042">DeconvolutionLayerNode.cpp:42</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#l00375">PixelWiseMultiplication.cpp:375</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#l00466">Error.h:466</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#l00916">FunctionHelpers.h:916</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_stack_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_stack_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::StackLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_stack_layer_node_8cpp_source.xhtml#l00105">StackLayerNode.cpp:105</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#l00803">Types.h:803</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_permute_layer_node_xhtml_a509cfef89595612c50bce4ef1eae181b"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_permute_layer_node.xhtml#a509cfef89595612c50bce4ef1eae181b">arm_compute::graph::PermuteLayerNode::permutation_vector</a></div><div class="ttdeci">const PermutationVector & permutation_vector() const</div><div class="ttdoc">Permutation vector accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_permute_layer_node_8cpp_source.xhtml#l00042">PermuteLayerNode.cpp:42</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_permute_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_permute_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::PermuteLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_permute_layer_node_8cpp_source.xhtml#l00077">PermuteLayerNode.cpp:77</a></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#l00821">FunctionHelpers.h:821</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_channel_shuffle_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_channel_shuffle_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::ChannelShuffleLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_channel_shuffle_layer_node_8cpp_source.xhtml#l00068">ChannelShuffleLayerNode.cpp:68</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#l01389">Types.h:1389</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1backends_1_1_fused_depthwise_convolution_batch_normalization_function_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1backends_1_1_fused_depthwise_convolution_batch_normalization_function.xhtml">arm_compute::graph::backends::FusedDepthwiseConvolutionBatchNormalizationFunction</a></div><div class="ttdoc">Wrapper function to first apply {NE, CL}BatchNormalizationLayer on the weights and then run {NE,...</div><div class="ttdef"><b>Definition:</b> <a href="_fused_depthwise_convolution_batch_normalization_function_8h_source.xhtml#l00039">FusedDepthwiseConvolutionBatchNormalizationFunction.h:39</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_batch_normalization_layer_node_xhtml_a32186582e0a6e02ed7ac3944f60b9c62"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_batch_normalization_layer_node.xhtml#a32186582e0a6e02ed7ac3944f60b9c62">arm_compute::graph::BatchNormalizationLayerNode::fused_activation</a></div><div class="ttdeci">ActivationLayerInfo fused_activation() const</div><div class="ttdoc">Returns fused activation.</div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_layer_node_8cpp_source.xhtml#l00046">BatchNormalizationLayerNode.cpp:46</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#l01046">FunctionHelpers.h:1046</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_eltwise_layer_node_xhtml_acda6687f669fe87581d7bff8fcd82ebc"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_eltwise_layer_node.xhtml#acda6687f669fe87581d7bff8fcd82ebc">arm_compute::graph::EltwiseLayerNode::eltwise_operation</a></div><div class="ttdeci">EltwiseOperation eltwise_operation() const</div><div class="ttdoc">Eltwise operation accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_eltwise_layer_node_8cpp_source.xhtml#l00040">EltwiseLayerNode.cpp:40</a></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#l00057">GraphContext.h:57</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#l01615">Types.h:1615</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_permute_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_permute_layer_node.xhtml">arm_compute::graph::PermuteLayerNode</a></div><div class="ttdoc">Permute Layer node.</div><div class="ttdef"><b>Definition:</b> <a href="_permute_layer_node_8h_source.xhtml#l00034">PermuteLayerNode.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_pooling_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_pooling_layer_node.xhtml">arm_compute::graph::PoolingLayerNode</a></div><div class="ttdoc">Pooling Layer node.</div><div class="ttdef"><b>Definition:</b> <a href="_pooling_layer_node_8h_source.xhtml#l00034">PoolingLayerNode.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_batch_normalization_layer_node_xhtml_acd39aa81617eab4d3482fa904d5dee87"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_batch_normalization_layer_node.xhtml#acd39aa81617eab4d3482fa904d5dee87">arm_compute::graph::BatchNormalizationLayerNode::epsilon</a></div><div class="ttdeci">float epsilon() const</div><div class="ttdoc">Epsilon parameter accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_layer_node_8cpp_source.xhtml#l00041">BatchNormalizationLayerNode.cpp:41</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_convolution_layer_node_xhtml_a984881c2c9e6de259af8fcd4ecbc4d80"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_convolution_layer_node.xhtml#a984881c2c9e6de259af8fcd4ecbc4d80">arm_compute::graph::ConvolutionLayerNode::num_groups</a></div><div class="ttdeci">unsigned int num_groups() const</div><div class="ttdoc">Number of groups in convolution accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_convolution_layer_node_8cpp_source.xhtml#l00071">ConvolutionLayerNode.cpp:71</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#l00054">Logger.h:54</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_ae157076aa533f9db0960dea1d5fc5014"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ae157076aa533f9db0960dea1d5fc5014">arm_compute::graph::backends::detail::create_stack_layer</a></div><div class="ttdeci">std::unique_ptr< arm_compute::IFunction > create_stack_layer(StackLayerNode &node)</div><div class="ttdoc">Create a backend layer stack function.</div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l01542">FunctionHelpers.h:1542</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2020 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="classarm__compute_1_1graph_1_1_detection_output_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_detection_output_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::DetectionOutputLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_detection_output_layer_node_8cpp_source.xhtml#l00082">DetectionOutputLayerNode.cpp:82</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node.xhtml">arm_compute::graph::FusedConvolutionBatchNormalizationNode</a></div><div class="ttdoc">Batch Normalization node.</div><div class="ttdef"><b>Definition:</b> <a href="_fused_convolution_batch_normalization_node_8h_source.xhtml#l00034">FusedConvolutionBatchNormalizationNode.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_pad_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_pad_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::PadLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_pad_layer_node_8cpp_source.xhtml#l00077">PadLayerNode.cpp:77</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_pooling_layer_node_xhtml_a27ad0a381c4ccbc80999d452c4dfe18b"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_pooling_layer_node.xhtml#a27ad0a381c4ccbc80999d452c4dfe18b">arm_compute::graph::PoolingLayerNode::pooling_info</a></div><div class="ttdeci">PoolingLayerInfo pooling_info() const</div><div class="ttdoc">Pooling metadata accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_pooling_layer_node_8cpp_source.xhtml#l00042">PoolingLayerNode.cpp:42</a></div></div> |
| <div class="ttc" id="_fused_convolution_batch_normalization_function_8h_xhtml"><div class="ttname"><a href="_fused_convolution_batch_normalization_function_8h.xhtml">FusedConvolutionBatchNormalizationFunction.h</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="classarm__compute_1_1graph_1_1_normalization_layer_node_xhtml_a3bfea94983e45ff8d1a3061206593349"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_normalization_layer_node.xhtml#a3bfea94983e45ff8d1a3061206593349">arm_compute::graph::NormalizationLayerNode::normalization_info</a></div><div class="ttdeci">NormalizationLayerInfo normalization_info() const</div><div class="ttdoc">Normalization info accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_normalization_layer_node_8cpp_source.xhtml#l00041">NormalizationLayerNode.cpp:41</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8fcf2ddd9a1d58b1b280f5c0aed71845"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">arm_compute::test::validation::input</a></div><div class="ttdeci">auto input</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">LSTMLayerQuantized.cpp:487</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#l01757">Types.h:1757</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#l01504">FunctionHelpers.h:1504</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_batch_normalization_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_batch_normalization_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::BatchNormalizationLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_layer_node_8cpp_source.xhtml#l00079">BatchNormalizationLayerNode.cpp:79</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="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node_xhtml_a984881c2c9e6de259af8fcd4ecbc4d80"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node.xhtml#a984881c2c9e6de259af8fcd4ecbc4d80">arm_compute::graph::FusedConvolutionBatchNormalizationNode::num_groups</a></div><div class="ttdeci">unsigned int num_groups() const</div><div class="ttdoc">Number of groups in convolution accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_fused_convolution_batch_normalization_node_8cpp_source.xhtml#l00076">FusedConvolutionBatchNormalizationNode.cpp:76</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_stack_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_stack_layer_node.xhtml">arm_compute::graph::StackLayerNode</a></div><div class="ttdoc">Stack Layer node.</div><div class="ttdef"><b>Definition:</b> <a href="_stack_layer_node_8h_source.xhtml#l00034">StackLayerNode.h:34</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a7ec865e1ee296647ec995b501e5ceb8b"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a7ec865e1ee296647ec995b501e5ceb8b">arm_compute::graph::backends::detail::create_fused_depthwise_convolution_batch_normalization_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_fused_depthwise_convolution_batch_normalization_layer(FusedDepthwiseConvolutionBatchNormalizationNode &node, GraphContext &ctx)</div><div class="ttdoc">Create a backend fused depthwise convolution batch normalization layer function.</div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00239">FunctionHelpers.h:239</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_fused_convolution_batch_normalization_node_xhtml_ad087f1f9aa1e444236911adf6c57df04"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node.xhtml#ad087f1f9aa1e444236911adf6c57df04">arm_compute::graph::FusedConvolutionBatchNormalizationNode::convolution_info</a></div><div class="ttdeci">PadStrideInfo convolution_info() const</div><div class="ttdoc">Convolution metadata accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_fused_convolution_batch_normalization_node_8cpp_source.xhtml#l00071">FusedConvolutionBatchNormalizationNode.cpp:71</a></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#l00045">INode.h:45</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#l00424">FunctionHelpers.h:424</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_pad_layer_node_xhtml_af98c64901f2fef6b6e26563bbb358f7e"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_pad_layer_node.xhtml#af98c64901f2fef6b6e26563bbb358f7e">arm_compute::graph::PadLayerNode::padding</a></div><div class="ttdeci">const PaddingList & padding() const</div><div class="ttdoc">Padding list accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_pad_layer_node_8cpp_source.xhtml#l00042">PadLayerNode.cpp:42</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#l01585">FunctionHelpers.h:1585</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#l00152">Error.h:152</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_upsample_layer_node_xhtml_aa7260d3e156a747cfa5ea5f2173ef3df"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_upsample_layer_node.xhtml#aa7260d3e156a747cfa5ea5f2173ef3df">arm_compute::graph::UpsampleLayerNode::info</a></div><div class="ttdeci">Size2D info() const</div><div class="ttdoc">Stride info metadata accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_upsample_layer_node_8cpp_source.xhtml#l00041">UpsampleLayerNode.cpp:41</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#l01421">FunctionHelpers.h:1421</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_pooling_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_pooling_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::PoolingLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_pooling_layer_node_8cpp_source.xhtml#l00091">PoolingLayerNode.cpp:91</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_quantization_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_quantization_layer_node.xhtml">arm_compute::graph::QuantizationLayerNode</a></div><div class="ttdoc">Quantization Layer node.</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_layer_node_8h_source.xhtml#l00034">QuantizationLayerNode.h:34</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="classarm__compute_1_1graph_1_1_prior_box_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_prior_box_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::PriorBoxLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_prior_box_layer_node_8cpp_source.xhtml#l00085">PriorBoxLayerNode.cpp:85</a></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="arm__compute_2graph_2_utils_8h_xhtml"><div class="ttname"><a href="arm__compute_2graph_2_utils_8h.xhtml">Utils.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_fused_depthwise_convolution_batch_normalization_node_xhtml_a32186582e0a6e02ed7ac3944f60b9c62"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fused_depthwise_convolution_batch_normalization_node.xhtml#a32186582e0a6e02ed7ac3944f60b9c62">arm_compute::graph::FusedDepthwiseConvolutionBatchNormalizationNode::fused_activation</a></div><div class="ttdeci">ActivationLayerInfo fused_activation() const</div><div class="ttdoc">Returns fused activation.</div><div class="ttdef"><b>Definition:</b> <a href="_fused_depthwise_convolution_batch_normalization_node_8cpp_source.xhtml#l00071">FusedDepthwiseConvolutionBatchNormalizationNode.cpp:71</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="classarm__compute_1_1graph_1_1_dequantization_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_dequantization_layer_node.xhtml">arm_compute::graph::DequantizationLayerNode</a></div><div class="ttdoc">Dequantize Layer node.</div><div class="ttdef"><b>Definition:</b> <a href="_dequantization_layer_node_8h_source.xhtml#l00038">DequantizationLayerNode.h:38</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a0b0eb3235749a2909dc5a101afe59a1b"><div class="ttname"><a href="_error_8h.xhtml#a0b0eb3235749a2909dc5a101afe59a1b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond, msg)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00456">Error.h:456</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a2270b3e1d20651d2d8341c858c890830"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">arm_compute::test::validation::num_groups</a></div><div class="ttdeci">const unsigned int num_groups</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00148">Im2Col.cpp:148</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_reshape_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_reshape_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::ReshapeLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="src_2graph_2nodes_2_reshape_layer_8cpp_source.xhtml#l00066">ReshapeLayer.cpp:66</a></div></div> |
| <div class="ttc" id="_fused_depthwise_convolution_batch_normalization_function_8h_xhtml"><div class="ttname"><a href="_fused_depthwise_convolution_batch_normalization_function_8h.xhtml">FusedDepthwiseConvolutionBatchNormalizationFunction.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_y_o_l_o_layer_node_xhtml_adb962dd0e5ae700a7ab8b64a437aea2a"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_y_o_l_o_layer_node.xhtml#adb962dd0e5ae700a7ab8b64a437aea2a">arm_compute::graph::YOLOLayerNode::num_classes</a></div><div class="ttdeci">int32_t num_classes() const</div><div class="ttdoc">Number of classes metadata accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_y_o_l_o_layer_node_8cpp_source.xhtml#l00046">YOLOLayerNode.cpp:46</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_slice_layer_node_xhtml_a476f08a0729f8861bec63b7e62c7b514"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_slice_layer_node.xhtml#a476f08a0729f8861bec63b7e62c7b514">arm_compute::graph::SliceLayerNode::starts</a></div><div class="ttdeci">Coordinates starts() const</div><div class="ttdoc">Start coordinates accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_slice_layer_node_8cpp_source.xhtml#l00042">SliceLayerNode.cpp:42</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1utils_1_1cast_xhtml_a2ea3d1fc01a3a442900249ca182ffa5e"><div class="ttname"><a href="namespacearm__compute_1_1utils_1_1cast.xhtml#a2ea3d1fc01a3a442900249ca182ffa5e">arm_compute::utils::cast::U</a></div><div class="ttdeci">U</div><div class="ttdef"><b>Definition:</b> <a href="_saturate_cast_8h_source.xhtml#l00057">SaturateCast.h:57</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_detection_post_process_layer_node_xhtml_a23ab280af362e61b91763038fc3194f4"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_detection_post_process_layer_node.xhtml#a23ab280af362e61b91763038fc3194f4">arm_compute::graph::DetectionPostProcessLayerNode::detection_post_process_info</a></div><div class="ttdeci">DetectionPostProcessLayerInfo detection_post_process_info() const</div><div class="ttdoc">DetectionPostProcess metadata accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_detection_post_process_layer_node_8cpp_source.xhtml#l00042">DetectionPostProcessLayerNode.cpp:42</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_reorg_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_reorg_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::ReorgLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_reorg_layer_node_8cpp_source.xhtml#l00088">ReorgLayerNode.cpp:88</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_softmax_layer_node_xhtml_a01524f49fa1d982d4382e390bef91de7"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_softmax_layer_node.xhtml#a01524f49fa1d982d4382e390bef91de7">arm_compute::graph::SoftmaxLayerNode::beta</a></div><div class="ttdeci">float beta() const</div><div class="ttdoc">Beta parameter accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_softmax_layer_node_8cpp_source.xhtml#l00041">SoftmaxLayerNode.cpp:41</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_pooling_layer_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_pooling_layer_info.xhtml">arm_compute::PoolingLayerInfo</a></div><div class="ttdoc">Pooling Layer Information struct.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01211">Types.h:1211</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_slice_layer_node_xhtml_a836dbfcb541878d045cac29f9b35d5cb"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_slice_layer_node.xhtml#a836dbfcb541878d045cac29f9b35d5cb">arm_compute::graph::SliceLayerNode::ends</a></div><div class="ttdeci">Coordinates ends() const</div><div class="ttdoc">End coordinates accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_slice_layer_node_8cpp_source.xhtml#l00047">SliceLayerNode.cpp:47</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#l00075">FunctionHelpers.h:75</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_depthwise_convolution_layer_node_xhtml_ad087f1f9aa1e444236911adf6c57df04"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_depthwise_convolution_layer_node.xhtml#ad087f1f9aa1e444236911adf6c57df04">arm_compute::graph::DepthwiseConvolutionLayerNode::convolution_info</a></div><div class="ttdeci">PadStrideInfo convolution_info() const</div><div class="ttdoc">Convolution metadata accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_depthwise_convolution_layer_node_8cpp_source.xhtml#l00058">DepthwiseConvolutionLayerNode.cpp:58</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#l01229">FunctionHelpers.h:1229</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'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#l00836">Types.h:836</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_afce1d2d783bb97a3a8c3c406c8cf6b9c"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#afce1d2d783bb97a3a8c3c406c8cf6b9c">arm_compute::graph::backends::detail::create_detection_output_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_detection_output_layer(DetectionOutputLayerNode &node)</div><div class="ttdoc">Create a backend detection output layer function.</div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00658">FunctionHelpers.h:658</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_1graph_1_1_convolution_layer_node_xhtml_ad087f1f9aa1e444236911adf6c57df04"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_convolution_layer_node.xhtml#ad087f1f9aa1e444236911adf6c57df04">arm_compute::graph::ConvolutionLayerNode::convolution_info</a></div><div class="ttdeci">PadStrideInfo convolution_info() const</div><div class="ttdoc">Convolution metadata accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_convolution_layer_node_8cpp_source.xhtml#l00066">ConvolutionLayerNode.cpp:66</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node_xhtml_a807d0a897f65b2fa1f8ea92892fa2c2a"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node.xhtml#a807d0a897f65b2fa1f8ea92892fa2c2a">arm_compute::graph::FusedConvolutionBatchNormalizationNode::fast_math_hint</a></div><div class="ttdeci">FastMathHint fast_math_hint() const</div><div class="ttdoc">Fast math hint accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_fused_convolution_batch_normalization_node_8cpp_source.xhtml#l00066">FusedConvolutionBatchNormalizationNode.cpp:66</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#l00686">Types.h:686</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_dequantization_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_dequantization_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::DequantizationLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_dequantization_layer_node_8cpp_source.xhtml#l00067">DequantizationLayerNode.cpp:67</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node_xhtml_a32186582e0a6e02ed7ac3944f60b9c62"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fused_convolution_batch_normalization_node.xhtml#a32186582e0a6e02ed7ac3944f60b9c62">arm_compute::graph::FusedConvolutionBatchNormalizationNode::fused_activation</a></div><div class="ttdeci">ActivationLayerInfo fused_activation() const</div><div class="ttdoc">Returns fused activation.</div><div class="ttdef"><b>Definition:</b> <a href="_fused_convolution_batch_normalization_node_8cpp_source.xhtml#l00081">FusedConvolutionBatchNormalizationNode.cpp:81</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="namespacearm__compute_1_1test_1_1validation_xhtml_a3161c2c93c655dd30953372064ec627b"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a3161c2c93c655dd30953372064ec627b">arm_compute::test::validation::alpha</a></div><div class="ttdeci">const float alpha</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_accumulate_8cpp_source.xhtml#l00103">Accumulate.cpp:103</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_detection_output_layer_node_xhtml_a4491336dccd18464fbbf617c981736cf"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_detection_output_layer_node.xhtml#a4491336dccd18464fbbf617c981736cf">arm_compute::graph::DetectionOutputLayerNode::detection_output_info</a></div><div class="ttdeci">DetectionOutputLayerInfo detection_output_info() const</div><div class="ttdoc">DetectionOutput metadata accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_detection_output_layer_node_8cpp_source.xhtml#l00042">DetectionOutputLayerNode.cpp:42</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_r_o_i_align_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_r_o_i_align_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::ROIAlignLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_r_o_i_align_layer_node_8cpp_source.xhtml#l00085">ROIAlignLayerNode.cpp:85</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#l01548">Types.h:1548</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="classarm__compute_1_1graph_1_1_resize_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_resize_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::ResizeLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_resize_layer_node_8cpp_source.xhtml#l00081">ResizeLayerNode.cpp:81</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a2dbe319a9ac9b6820b2ef5eff8c46ddc"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a2dbe319a9ac9b6820b2ef5eff8c46ddc">arm_compute::graph::backends::detail::create_detection_post_process_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_detection_post_process_layer(DetectionPostProcessLayerNode &node)</div><div class="ttdoc">Create a backend detection post process layer function.</div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00704">FunctionHelpers.h:704</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_i_tensor_handle_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_i_tensor_handle.xhtml">arm_compute::graph::ITensorHandle</a></div><div class="ttdoc">Tensor handle interface object.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_handle_8h_source.xhtml#l00038">ITensorHandle.h:38</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_ac77fa3bf0d7d7c3fde6243192f93f380"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#ac77fa3bf0d7d7c3fde6243192f93f380">arm_compute::graph::backends::detail::create_deconvolution_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_deconvolution_layer(DeconvolutionLayerNode &node, GraphContext &ctx)</div><div class="ttdoc">Create a backend deconvolution layer function.</div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l00515">FunctionHelpers.h:515</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#l01139">Utils.h:1139</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_detection_post_process_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_detection_post_process_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::DetectionPostProcessLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_detection_post_process_layer_node_8cpp_source.xhtml#l00094">DetectionPostProcessLayerNode.cpp:94</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#l00077">Utils.h:77</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="namespacearm__compute_1_1graph_1_1backends_1_1detail_xhtml_a5567ed5ad9c8fb45d2748bab27163530"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a5567ed5ad9c8fb45d2748bab27163530">arm_compute::graph::backends::detail::create_print_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_print_layer(PrintLayerNode &node)</div><div class="ttdoc">Create a backend print layer function.</div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l01199">FunctionHelpers.h:1199</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="classarm__compute_1_1graph_1_1_p_relu_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_p_relu_layer_node.xhtml">arm_compute::graph::PReluLayerNode</a></div><div class="ttdoc">PRelu Layer node.</div><div class="ttdef"><b>Definition:</b> <a href="_p_relu_layer_node_8h_source.xhtml#l00034">PReluLayerNode.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_convolution_layer_node_xhtml_a16b2c6652c4cee5b566daf018f768a42"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_convolution_layer_node.xhtml#a16b2c6652c4cee5b566daf018f768a42">arm_compute::graph::ConvolutionLayerNode::convolution_method</a></div><div class="ttdeci">ConvolutionMethod convolution_method() const</div><div class="ttdoc">Convolution layer method accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_convolution_layer_node_8cpp_source.xhtml#l00051">ConvolutionLayerNode.cpp:51</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_fully_connected_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fully_connected_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::FullyConnectedLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="src_2graph_2nodes_2_fully_connected_layer_8cpp_source.xhtml#l00126">FullyConnectedLayer.cpp:126</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_output_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_detection_output_layer_info.xhtml">arm_compute::DetectionOutputLayerInfo</a></div><div class="ttdoc">Detection Output layer info.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00973">Types.h:973</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_detection_post_process_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_detection_post_process_layer_node.xhtml">arm_compute::graph::DetectionPostProcessLayerNode</a></div><div class="ttdoc">DetectionPostProcess Layer node.</div><div class="ttdef"><b>Definition:</b> <a href="_detection_post_process_layer_node_8h_source.xhtml#l00034">DetectionPostProcessLayerNode.h:34</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="classarm__compute_1_1graph_1_1_bounding_box_transform_layer_node_xhtml_a7a5bf7cea9e9cf19a6cf3e5240c5fff7"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_bounding_box_transform_layer_node.xhtml#a7a5bf7cea9e9cf19a6cf3e5240c5fff7">arm_compute::graph::BoundingBoxTransformLayerNode::info</a></div><div class="ttdeci">const BoundingBoxTransformInfo & info() const</div><div class="ttdoc">BoundingBoxTransformInfo accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_bounding_box_transform_layer_node_8cpp_source.xhtml#l00042">BoundingBoxTransformLayerNode.cpp:42</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#l00860">FunctionHelpers.h:860</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#l00099">FunctionHelpers.h:99</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_detection_output_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_detection_output_layer_node.xhtml">arm_compute::graph::DetectionOutputLayerNode</a></div><div class="ttdoc">DetectionOutput Layer node.</div><div class="ttdef"><b>Definition:</b> <a href="_detection_output_layer_node_8h_source.xhtml#l00034">DetectionOutputLayerNode.h:34</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_accc088009d44c521706aa98d6387ee21"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">arm_compute::test::validation::axis</a></div><div class="ttdeci">axis</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_stack_layer_8cpp_source.xhtml#l00226">StackLayer.cpp:226</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_stack_layer_node_xhtml_a18ffaef3d2889fbba089ffbf7ea2f12d"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_stack_layer_node.xhtml#a18ffaef3d2889fbba089ffbf7ea2f12d">arm_compute::graph::StackLayerNode::axis</a></div><div class="ttdeci">int axis() const</div><div class="ttdoc">Stack axis parameter accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_stack_layer_node_8cpp_source.xhtml#l00045">StackLayerNode.cpp:45</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_y_o_l_o_layer_node_xhtml_a39a8dd296461705ee5cb54eacb4b2818"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_y_o_l_o_layer_node.xhtml#a39a8dd296461705ee5cb54eacb4b2818">arm_compute::graph::YOLOLayerNode::activation_info</a></div><div class="ttdeci">ActivationLayerInfo activation_info() const</div><div class="ttdoc">Activation metadata accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_y_o_l_o_layer_node_8cpp_source.xhtml#l00041">YOLOLayerNode.cpp:41</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92"><div class="ttname"><a href="namespacearm__compute.xhtml#a14d24d90ab4ba2956e92e27890ba4c91a8d6b5cada83510220f59e00ce86d4d92">arm_compute::PaddingMode::CONSTANT</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#l01394">Types.h:1394</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="namespacearm__compute_xhtml_a3a440b3893fa10608d4428958be1c52ea696b031073e74bf2cb98e5ef201d4aa3"><div class="ttname"><a href="namespacearm__compute.xhtml#a3a440b3893fa10608d4428958be1c52ea696b031073e74bf2cb98e5ef201d4aa3">arm_compute::CLVersion::UNKNOWN</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#l01374">Types.h:1374</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_slice_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_slice_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::SliceLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_slice_layer_node_8cpp_source.xhtml#l00086">SliceLayerNode.cpp:86</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_1_1detail_xhtml_a31be99a5d0f75045fc411e211824baad"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends_1_1detail.xhtml#a31be99a5d0f75045fc411e211824baad">arm_compute::graph::backends::detail::create_quantization_layer</a></div><div class="ttdeci">std::unique_ptr< IFunction > create_quantization_layer(QuantizationLayerNode &node)</div><div class="ttdoc">Create a backend quantization layer function.</div><div class="ttdef"><b>Definition:</b> <a href="_function_helpers_8h_source.xhtml#l01271">FunctionHelpers.h:1271</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_upsample_layer_node_xhtml_a8e71fc913825230e6e26af0242260ce4"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_upsample_layer_node.xhtml#a8e71fc913825230e6e26af0242260ce4">arm_compute::graph::UpsampleLayerNode::upsampling_policy</a></div><div class="ttdeci">InterpolationPolicy upsampling_policy() const</div><div class="ttdoc">Upsampling policy metadata accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_upsample_layer_node_8cpp_source.xhtml#l00046">UpsampleLayerNode.cpp:46</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#l00089">Utils.h:89</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#l01006">FunctionHelpers.h:1006</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#l00050">Logger.h:50</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#l00108">Types.h:108</a></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#l00294">FunctionHelpers.h:294</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_post_process_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_detection_post_process_layer_info.xhtml">arm_compute::DetectionPostProcessLayerInfo</a></div><div class="ttdoc">Detection Output layer info.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01092">Types.h:1092</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="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">ConvolutionLayer.cpp:188</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_generate_proposals_layer_node_xhtml_acfa649555ddb4df4cc5ae52b8205ee5f"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_generate_proposals_layer_node.xhtml#acfa649555ddb4df4cc5ae52b8205ee5f">arm_compute::graph::GenerateProposalsLayerNode::info</a></div><div class="ttdeci">const GenerateProposalsInfo & info() const</div><div class="ttdoc">GenerateProposalsInfo accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_generate_proposals_layer_node_8cpp_source.xhtml#l00042">GenerateProposalsLayerNode.cpp:42</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="classarm__compute_1_1graph_1_1_eltwise_layer_node_xhtml_aa7b3781f10fc0ac73a9a4f748e22d3d4"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_eltwise_layer_node.xhtml#aa7b3781f10fc0ac73a9a4f748e22d3d4">arm_compute::graph::EltwiseLayerNode::convert_policy</a></div><div class="ttdeci">ConvertPolicy convert_policy() const</div><div class="ttdoc">Convert policy accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_eltwise_layer_node_8cpp_source.xhtml#l00045">EltwiseLayerNode.cpp:45</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_activation_layer_node_xhtml_a39a8dd296461705ee5cb54eacb4b2818"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_activation_layer_node.xhtml#a39a8dd296461705ee5cb54eacb4b2818">arm_compute::graph::ActivationLayerNode::activation_info</a></div><div class="ttdeci">ActivationLayerInfo activation_info() const</div><div class="ttdoc">Activation metadata accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_activation_layer_node_8cpp_source.xhtml#l00040">ActivationLayerNode.cpp:40</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#l00059">FunctionHelpers.h:59</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_p_relu_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_p_relu_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::PReluLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_p_relu_layer_node_8cpp_source.xhtml#l00061">PReluLayerNode.cpp:61</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_fully_connected_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fully_connected_layer_node.xhtml">arm_compute::graph::FullyConnectedLayerNode</a></div><div class="ttdoc">Fully Connected Layer node.</div><div class="ttdef"><b>Definition:</b> <a href="_fully_connected_layer_node_8h_source.xhtml#l00034">FullyConnectedLayerNode.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_depthwise_convolution_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_depthwise_convolution_layer_node.xhtml">arm_compute::graph::DepthwiseConvolutionLayerNode</a></div><div class="ttdoc">Depthwise Convolution Layer node.</div><div class="ttdef"><b>Definition:</b> <a href="_depthwise_convolution_layer_node_8h_source.xhtml#l00034">DepthwiseConvolutionLayerNode.h:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_flatten_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_flatten_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::FlattenLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_flatten_layer_node_8cpp_source.xhtml#l00065">FlattenLayerNode.cpp:65</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="classarm__compute_1_1graph_1_1_fused_depthwise_convolution_batch_normalization_node_xhtml_acd39aa81617eab4d3482fa904d5dee87"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fused_depthwise_convolution_batch_normalization_node.xhtml#acd39aa81617eab4d3482fa904d5dee87">arm_compute::graph::FusedDepthwiseConvolutionBatchNormalizationNode::epsilon</a></div><div class="ttdeci">float epsilon() const</div><div class="ttdoc">Epsilon parameter accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_fused_depthwise_convolution_batch_normalization_node_8cpp_source.xhtml#l00056">FusedDepthwiseConvolutionBatchNormalizationNode.cpp:56</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#l01649">Types.h:1649</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'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_1test_1_1validation_xhtml_a4f4125dba5283887b34f889b1c615c0c"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">arm_compute::test::validation::info</a></div><div class="ttdeci">info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">ConvolutionLayer.cpp:182</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_print_layer_node_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_print_layer_node.xhtml">arm_compute::graph::PrintLayerNode</a></div><div class="ttdoc">Print Layer node.</div><div class="ttdef"><b>Definition:</b> <a href="_print_layer_node_8h_source.xhtml#l00037">PrintLayerNode.h:37</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_1_1backends_xhtml_a32d8fea34ca818386a078939a03e3cb8"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1backends.xhtml#a32d8fea34ca818386a078939a03e3cb8">arm_compute::graph::backends::get_weights_manager</a></div><div class="ttdeci">std::shared_ptr< IWeightsManager > get_weights_manager(GraphContext &ctx, Target target)</div><div class="ttdoc">Returns the weights manager for a given target.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2backends_2_utils_8h_source.xhtml#l00102">Utils.h:102</a></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_1_tensor_info_xhtml_a269b19ce3f357ac65f41f9951906e38e"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">arm_compute::TensorInfo::tensor_shape</a></div><div class="ttdeci">const TensorShape & tensor_shape() const override</div><div class="ttdoc">Size for each dimension of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00261">TensorInfo.h:261</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's type.</div><div class="ttdef"><b>Definition:</b> <a href="_concatenate_layer_node_8cpp_source.xhtml#l00130">ConcatenateLayerNode.cpp:130</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1graph_1_1_tensor_descriptor_xhtml_a4b52bb397c7296e8efe864967b44f97e"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a4b52bb397c7296e8efe864967b44f97e">arm_compute::graph::TensorDescriptor::layout</a></div><div class="ttdeci">DataLayout layout</div><div class="ttdoc">Data layout.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_descriptor_8h_source.xhtml#l00111">TensorDescriptor.h:111</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_absolute_difference_8cpp_source.xhtml#l00113">AbsoluteDifference.cpp:113</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#l01123">FunctionHelpers.h:1123</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_activation_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_activation_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::ActivationLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_activation_layer_node_8cpp_source.xhtml#l00074">ActivationLayerNode.cpp:74</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdoc">[DataLayout enum definition]</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00117">Types.h:117</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#l00367">FunctionHelpers.h:367</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#l00359">Types.h:359</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#l01345">FunctionHelpers.h:1345</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a1df15aed3ed531f442ecea2a131d65a4"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a1df15aed3ed531f442ecea2a131d65a4">arm_compute::graph::get_dimension_idx</a></div><div class="ttdeci">size_t get_dimension_idx(DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get index of a tensor's given dimension depending on its layout.</div><div class="ttdef"><b>Definition:</b> <a href="src_2graph_2_utils_8cpp_source.xhtml#l00129">Utils.cpp:129</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#l01629">FunctionHelpers.h:1629</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_prior_box_layer_node_xhtml_a0f62f59c57a7cdbdc20f7d850f1dfd8c"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_prior_box_layer_node.xhtml#a0f62f59c57a7cdbdc20f7d850f1dfd8c">arm_compute::graph::PriorBoxLayerNode::priorbox_info</a></div><div class="ttdeci">PriorBoxLayerInfo priorbox_info() const</div><div class="ttdoc">PriorBox metadata accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_prior_box_layer_node_8cpp_source.xhtml#l00042">PriorBoxLayerNode.cpp:42</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#l01308">FunctionHelpers.h:1308</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#l00332">FunctionHelpers.h:332</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_resize_layer_node_xhtml_a718c049decea6397c493df9cb2f581da"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_resize_layer_node.xhtml#a718c049decea6397c493df9cb2f581da">arm_compute::graph::ResizeLayerNode::policy</a></div><div class="ttdeci">InterpolationPolicy policy() const</div><div class="ttdoc">Interpolation policy accessor.</div><div class="ttdef"><b>Definition:</b> <a href="_resize_layer_node_8cpp_source.xhtml#l00041">ResizeLayerNode.cpp:41</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1quantization_xhtml_a552dc3787d7ea1675f3e4e8993501d58"><div class="ttname"><a href="namespacearm__compute_1_1quantization.xhtml#a552dc3787d7ea1675f3e4e8993501d58">arm_compute::quantization::epsilon</a></div><div class="ttdeci">constexpr float epsilon</div><div class="ttdef"><b>Definition:</b> <a href="_asymm_helpers_8cpp_source.xhtml#l00036">AsymmHelpers.cpp:36</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1_normalize_planar_y_u_v_layer_node_xhtml_a65d13dc93e2df5e8ab725263cf9f4ac5"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_normalize_planar_y_u_v_layer_node.xhtml#a65d13dc93e2df5e8ab725263cf9f4ac5">arm_compute::graph::NormalizePlanarYUVLayerNode::type</a></div><div class="ttdeci">NodeType type() const override</div><div class="ttdoc">Returns node's type.</div><div class="ttdef"><b>Definition:</b> <a href="_normalize_planar_y_u_v_layer_node_8cpp_source.xhtml#l00063">NormalizePlanarYUVLayerNode.cpp:63</a></div></div> |
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