Google Git
Sign in
android / platform / external / armnn / eba482e6 / . / Documentation / _prelu_layer_8cpp_source.xhtml
blob: f08af4a3820d6b1df470a04368f98df106a11750 [file] [log] [blame]
<!-- Copyright (c) 2020 ARM Limited. -->
<!-- -->
<!-- SPDX-License-Identifier: MIT -->
<!-- -->
<!-- HTML header for doxygen 1.8.13-->
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
<meta name="generator" content="Doxygen 1.8.13"/>
<meta name="robots" content="NOINDEX, NOFOLLOW" />
<meta name="viewport" content="width=device-width, initial-scale=1"/>
<title>ArmNN: src/armnn/layers/PreluLayer.cpp Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="navtree.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="resize.js"></script>
<script type="text/javascript" src="navtreedata.js"></script>
<script type="text/javascript" src="navtree.js"></script>
<script type="text/javascript">
$(document).ready(initResizable);
</script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
extensions: ["tex2jax.js"],
jax: ["input/TeX","output/HTML-CSS"],
});
</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
<link href="stylesheet.css" rel="stylesheet" type="text/css"/>
</head>
<body>
<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
<div id="titlearea">
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
<img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/>
<td style="padding-left: 0.5em;">
<div id="projectname">
&#160;<span id="projectnumber">20.02</span>
</div>
</td>
</tr>
</tbody>
</table>
</div>
<!-- end header part -->
<!-- Generated by Doxygen 1.8.13 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
<script type="text/javascript" src="menudata.js"></script>
<script type="text/javascript" src="menu.js"></script>
<script type="text/javascript">
$(function() {
initMenu('',true,false,'search.php','Search');
$(document).ready(function() { init_search(); });
});
</script>
<div id="main-nav"></div>
</div><!-- top -->
<div id="side-nav" class="ui-resizable side-nav-resizable">
<div id="nav-tree">
<div id="nav-tree-contents">
<div id="nav-sync" class="sync"></div>
</div>
</div>
<div id="splitbar" style="-moz-user-select:none;"
class="ui-resizable-handle">
</div>
</div>
<script type="text/javascript">
$(document).ready(function(){initNavTree('_prelu_layer_8cpp_source.xhtml','');});
</script>
<div id="doc-content">
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
</div>
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
<iframe src="javascript:void(0)" frameborder="0"
name="MSearchResults" id="MSearchResults">
</iframe>
</div>
<div class="header">
<div class="headertitle">
<div class="title">PreluLayer.cpp</div> </div>
</div><!--header-->
<div class="contents">
<a href="_prelu_layer_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;</div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_prelu_layer_8hpp.xhtml">PreluLayer.hpp</a>&quot;</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;</div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_layer_clone_base_8hpp.xhtml">LayerCloneBase.hpp</a>&quot;</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_workload_data_8hpp.xhtml">backendsCommon/WorkloadData.hpp</a>&gt;</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_workload_factory_8hpp.xhtml">backendsCommon/WorkloadFactory.hpp</a>&gt;</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_cpu_tensor_handle_8hpp.xhtml">backendsCommon/CpuTensorHandle.hpp</a>&gt;</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;{</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;</div><div class="line"><a name="l00017"></a><span class="lineno"><a class="line" href="classarmnn_1_1_prelu_layer.xhtml#ad3ae922a3829fa9cf40bd13bc211f40b"> 17</a></span>&#160;<a class="code" href="classarmnn_1_1_prelu_layer.xhtml#ad3ae922a3829fa9cf40bd13bc211f40b">PreluLayer::PreluLayer</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* name)</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160; : <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>(2, 1, <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4">LayerType</a>::<a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a0c5967f09e0669c840ebb1ed0da85e32">Prelu</a>, name)</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;{}</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;</div><div class="line"><a name="l00021"></a><span class="lineno"><a class="line" href="classarmnn_1_1_prelu_layer.xhtml#adfa912d0c4c6c00f1af2cbfa799572b7"> 21</a></span>&#160;std::unique_ptr&lt;IWorkload&gt; <a class="code" href="classarmnn_1_1_prelu_layer.xhtml#adfa912d0c4c6c00f1af2cbfa799572b7">PreluLayer::CreateWorkload</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>&amp; factory)<span class="keyword"> const</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160; <a class="code" href="structarmnn_1_1_prelu_queue_descriptor.xhtml">PreluQueueDescriptor</a> descriptor;</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160; <span class="keywordflow">return</span> factory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#adf4a93f605e4e7dad6aee0b4d2159171">CreatePrelu</a>(descriptor, <a class="code" href="classarmnn_1_1_layer.xhtml#a30a858b2b26d651a066537e499fbf40d">PrepInfoAndDesc</a>(descriptor));</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;}</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;</div><div class="line"><a name="l00028"></a><span class="lineno"><a class="line" href="classarmnn_1_1_prelu_layer.xhtml#af5dd85c2adbf7e9f95d9c984157620a9"> 28</a></span>&#160;<a class="code" href="classarmnn_1_1_prelu_layer.xhtml">PreluLayer</a>* <a class="code" href="classarmnn_1_1_prelu_layer.xhtml#af5dd85c2adbf7e9f95d9c984157620a9">PreluLayer::Clone</a>(<a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>&amp; graph)<span class="keyword"> const</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160; <span class="keyword">auto</span> layer = CloneBase&lt;PreluLayer&gt;(graph, <a class="code" href="classarmnn_1_1_layer.xhtml#a7ddf0cf6f620d59c10e63495ace795d0">GetName</a>());</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160; <span class="keywordflow">return</span> std::move(layer);</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;}</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160;</div><div class="line"><a name="l00035"></a><span class="lineno"><a class="line" href="classarmnn_1_1_prelu_layer.xhtml#a65ca562c882ad619684445a1402f415a"> 35</a></span>&#160;std::vector&lt;TensorShape&gt; <a class="code" href="classarmnn_1_1_prelu_layer.xhtml#a65ca562c882ad619684445a1402f415a">PreluLayer::InferOutputShapes</a>(<span class="keyword">const</span> std::vector&lt;TensorShape&gt;&amp; inputShapes)<span class="keyword"> const</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160; BOOST_ASSERT(inputShapes.size() == 2);</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160;</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>&amp; inputShape = inputShapes[0];</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>&amp; alphaShape = inputShapes[1];</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160;</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShapeDimensions = inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> alphaShapeDimensions = alphaShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160;</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; BOOST_ASSERT(inputShapeDimensions &gt; 0);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; BOOST_ASSERT(alphaShapeDimensions &gt; 0);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160;</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <span class="comment">// The size of the output is the maximum size along each dimension of the input operands,</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; <span class="comment">// it starts with the trailing dimensions, and works its way forward</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputDimensions = std::max(inputShapeDimensions, alphaShapeDimensions);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160;</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape(outputDimensions);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160;</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; <span class="keywordtype">int</span> inputShapeIndex = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">int</span>&gt;(inputShapeDimensions) - 1;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; <span class="keywordtype">int</span> alphaShapeIndex = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">int</span>&gt;(alphaShapeDimensions) - 1;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShapeIndex = outputDimensions - 1;</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; <span class="comment">// Loop backwards through the common part of the shapes</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; <span class="keywordflow">while</span> (inputShapeIndex &gt;= 0 &amp;&amp; alphaShapeIndex &gt;= 0)</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; {</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputDimension = inputShape[<a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(inputShapeIndex)];</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> alphaDimension = alphaShape[<a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(alphaShapeIndex)];</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <span class="comment">// Check that the inputs are broadcast compatible</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; BOOST_ASSERT_MSG(inputDimension == alphaDimension || inputDimension == 1 || alphaDimension == 1,</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; <span class="stringliteral">&quot;PreluLayer: Dimensions should either match or one should be of size 1&quot;</span>);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; outputShape[outputShapeIndex] = std::max(inputDimension, alphaDimension);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; inputShapeIndex--;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; alphaShapeIndex--;</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; outputShapeIndex--;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; }</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160;</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; <span class="comment">// Loop backwards through the remaing part of the input shape (if any)</span></div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; <span class="keywordflow">while</span> (inputShapeIndex &gt;= 0)</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; {</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; outputShape[outputShapeIndex] = inputShape[<a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(inputShapeIndex)];</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160;</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; inputShapeIndex--;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; outputShapeIndex--;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; }</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; <span class="comment">// Loop backwards through the remaing part of the alpha shape (if any)</span></div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; <span class="keywordflow">while</span> (alphaShapeIndex &gt;= 0)</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; {</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; outputShape[outputShapeIndex] = alphaShape[<a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a>&lt;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt;(alphaShapeIndex)];</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160;</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; alphaShapeIndex--;</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; outputShapeIndex--;</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; }</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160;</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; <span class="keywordflow">return</span> { outputShape };</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160;}</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160;</div><div class="line"><a name="l00097"></a><span class="lineno"><a class="line" href="classarmnn_1_1_prelu_layer.xhtml#a8c8f543d7e9729362c266d12ec169966"> 97</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_prelu_layer.xhtml#a8c8f543d7e9729362c266d12ec169966">PreluLayer::ValidateTensorShapesFromInputs</a>()</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160;{</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; <a class="code" href="classarmnn_1_1_layer.xhtml#a0607e36e88f38c34c71c663164b76776">VerifyLayerConnections</a>(2, <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>());</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160;</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; std::vector&lt;TensorShape&gt; inferredShapes = <a class="code" href="classarmnn_1_1_prelu_layer.xhtml#a65ca562c882ad619684445a1402f415a">InferOutputShapes</a>(</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; {</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; <a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(0).<a class="code" href="classarmnn_1_1_input_slot.xhtml#a3153abb7c0c0a84629079b2fac7db54f">GetConnection</a>()-&gt;<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(),</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; <a class="code" href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">GetInputSlot</a>(1).<a class="code" href="classarmnn_1_1_input_slot.xhtml#a3153abb7c0c0a84629079b2fac7db54f">GetConnection</a>()-&gt;<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; });</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160;</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; BOOST_ASSERT(inferredShapes.size() == 1);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; ConditionalThrowIfNotEqual&lt;LayerValidationException&gt;(</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; <span class="stringliteral">&quot;PreluLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.&quot;</span>,</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; <a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#ada2ad7d1caeeb4ef6195c8925fad6a65">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(),</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; inferredShapes[0]);</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160;}</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160;</div><div class="line"><a name="l00115"></a><span class="lineno"><a class="line" href="classarmnn_1_1_prelu_layer.xhtml#a75a50f464326fefa605ea84ae2c9be85"> 115</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarmnn_1_1_prelu_layer.xhtml#a75a50f464326fefa605ea84ae2c9be85">PreluLayer::Accept</a>(<a class="code" href="classarmnn_1_1_i_layer_visitor.xhtml">ILayerVisitor</a>&amp; visitor)<span class="keyword"> const</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160;<span class="keyword"></span>{</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; visitor.<a class="code" href="classarmnn_1_1_i_layer_visitor.xhtml#a4f6971a5d2c164c691dc7943f4befd5c">VisitPreluLayer</a>(<span class="keyword">this</span>, <a class="code" href="classarmnn_1_1_layer.xhtml#a7ddf0cf6f620d59c10e63495ace795d0">GetName</a>());</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160;}</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160;</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160;} <span class="comment">// namespace armnn</span></div><div class="ttc" id="classarmnn_1_1_prelu_layer_xhtml_adfa912d0c4c6c00f1af2cbfa799572b7"><div class="ttname"><a href="classarmnn_1_1_prelu_layer.xhtml#adfa912d0c4c6c00f1af2cbfa799572b7">armnn::PreluLayer::CreateWorkload</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreateWorkload(const IWorkloadFactory &amp;factory) const override</div><div class="ttdoc">Makes a workload for the PReLU type. </div><div class="ttdef"><b>Definition:</b> <a href="_prelu_layer_8cpp_source.xhtml#l00021">PreluLayer.cpp:21</a></div></div>
<div class="ttc" id="_workload_data_8hpp_xhtml"><div class="ttname"><a href="_workload_data_8hpp.xhtml">WorkloadData.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape &amp; GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00088">Tensor.hpp:88</a></div></div>
<div class="ttc" id="classarmnn_1_1_prelu_layer_xhtml_af5dd85c2adbf7e9f95d9c984157620a9"><div class="ttname"><a href="classarmnn_1_1_prelu_layer.xhtml#af5dd85c2adbf7e9f95d9c984157620a9">armnn::PreluLayer::Clone</a></div><div class="ttdeci">PreluLayer * Clone(Graph &amp;graph) const override</div><div class="ttdoc">Creates a dynamically-allocated copy of this layer. </div><div class="ttdef"><b>Definition:</b> <a href="_prelu_layer_8cpp_source.xhtml#l00028">PreluLayer.cpp:28</a></div></div>
<div class="ttc" id="_workload_factory_8hpp_xhtml"><div class="ttname"><a href="_workload_factory_8hpp.xhtml">WorkloadFactory.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8hpp_source.xhtml#l00021">WorkloadFactory.hpp:21</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_layer_visitor_xhtml_a4f6971a5d2c164c691dc7943f4befd5c"><div class="ttname"><a href="classarmnn_1_1_i_layer_visitor.xhtml#a4f6971a5d2c164c691dc7943f4befd5c">armnn::ILayerVisitor::VisitPreluLayer</a></div><div class="ttdeci">virtual void VisitPreluLayer(const IConnectableLayer *layer, const char *name=nullptr)=0</div><div class="ttdoc">Function that a PReLU activation layer should call back to when its Accept(ILayerVisitor&amp;) function i...</div></div>
<div class="ttc" id="classarmnn_1_1_prelu_layer_xhtml_a75a50f464326fefa605ea84ae2c9be85"><div class="ttname"><a href="classarmnn_1_1_prelu_layer.xhtml#a75a50f464326fefa605ea84ae2c9be85">armnn::PreluLayer::Accept</a></div><div class="ttdeci">void Accept(ILayerVisitor &amp;visitor) const override</div><div class="ttdoc">Apply a visitor to this layer. </div><div class="ttdef"><b>Definition:</b> <a href="_prelu_layer_8cpp_source.xhtml#l00115">PreluLayer.cpp:115</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2020 ARM Limited. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00025">00_introduction.dox:25</a></div></div>
<div class="ttc" id="classarmnn_1_1_prelu_layer_xhtml_ad3ae922a3829fa9cf40bd13bc211f40b"><div class="ttname"><a href="classarmnn_1_1_prelu_layer.xhtml#ad3ae922a3829fa9cf40bd13bc211f40b">armnn::PreluLayer::PreluLayer</a></div><div class="ttdeci">PreluLayer(const char *name)</div><div class="ttdoc">Constructor to create a PreluLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_prelu_layer_8cpp_source.xhtml#l00017">PreluLayer.cpp:17</a></div></div>
<div class="ttc" id="structarmnn_1_1_prelu_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_prelu_queue_descriptor.xhtml">armnn::PreluQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00489">WorkloadData.hpp:489</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
<div class="ttc" id="classarmnn_1_1_input_slot_xhtml_a3153abb7c0c0a84629079b2fac7db54f"><div class="ttname"><a href="classarmnn_1_1_input_slot.xhtml#a3153abb7c0c0a84629079b2fac7db54f">armnn::InputSlot::GetConnection</a></div><div class="ttdeci">const IOutputSlot * GetConnection() const override</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00199">Layer.hpp:199</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a0c5967f09e0669c840ebb1ed0da85e32"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a0c5967f09e0669c840ebb1ed0da85e32">armnn::LayerType::Prelu</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_a0607e36e88f38c34c71c663164b76776"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a0607e36e88f38c34c71c663164b76776">armnn::Layer::VerifyLayerConnections</a></div><div class="ttdeci">void VerifyLayerConnections(unsigned int expectedConnections, const CheckLocation &amp;location) const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00338">Layer.cpp:338</a></div></div>
<div class="ttc" id="_prelu_layer_8hpp_xhtml"><div class="ttname"><a href="_prelu_layer_8hpp.xhtml">PreluLayer.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_acf8b8e23bf647836592982f97088d375"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#acf8b8e23bf647836592982f97088d375">armnn::Layer::GetInputSlot</a></div><div class="ttdeci">const InputSlot &amp; GetInputSlot(unsigned int index) const override</div><div class="ttdoc">Get a const input slot handle by slot index. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00310">Layer.hpp:310</a></div></div>
<div class="ttc" id="classarmnn_1_1_prelu_layer_xhtml_a65ca562c882ad619684445a1402f415a"><div class="ttname"><a href="classarmnn_1_1_prelu_layer.xhtml#a65ca562c882ad619684445a1402f415a">armnn::PreluLayer::InferOutputShapes</a></div><div class="ttdeci">std::vector&lt; TensorShape &gt; InferOutputShapes(const std::vector&lt; TensorShape &gt; &amp;inputShapes) const override</div><div class="ttdoc">By default returns inputShapes if the number of inputs are equal to number of outputs, otherwise infers the output shapes from given input shapes and layer properties. </div><div class="ttdef"><b>Definition:</b> <a href="_prelu_layer_8cpp_source.xhtml#l00035">PreluLayer.cpp:35</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_a30a858b2b26d651a066537e499fbf40d"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a30a858b2b26d651a066537e499fbf40d">armnn::Layer::PrepInfoAndDesc</a></div><div class="ttdeci">WorkloadInfo PrepInfoAndDesc(QueueDescriptor &amp;descriptor) const</div><div class="ttdoc">Helper function to reduce duplication in *LayerCreateWorkload. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00351">Layer.hpp:351</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a37fa39012e90d568df7f774cd6d1e956"><div class="ttname"><a href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t&lt; std::is_unsigned&lt; Source &gt;::value &amp;&amp;std::is_unsigned&lt; Dest &gt;::value, Dest &gt; numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00033">NumericCast.hpp:33</a></div></div>
<div class="ttc" id="_exceptions_8hpp_xhtml_aa3be76aec4ce713822a5ea1ecbb7bc61"><div class="ttname"><a href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a></div><div class="ttdeci">#define CHECK_LOCATION()</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00192">Exceptions.hpp:192</a></div></div>
<div class="ttc" id="classarmnn_1_1_graph_xhtml"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml">armnn::Graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00029">Graph.hpp:29</a></div></div>
<div class="ttc" id="_cpu_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="_cpu_tensor_handle_8hpp.xhtml">CpuTensorHandle.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorShape::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00043">Tensor.hpp:43</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_layer_visitor_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_layer_visitor.xhtml">armnn::ILayerVisitor</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_layer_visitor_8hpp_source.xhtml#l00016">ILayerVisitor.hpp:16</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_a0e36688a43c35668d8db5257274c68fe"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">armnn::Layer::GetOutputSlot</a></div><div class="ttdeci">const OutputSlot &amp; GetOutputSlot(unsigned int index=0) const override</div><div class="ttdoc">Get the const output slot handle by slot index. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00312">Layer.hpp:312</a></div></div>
<div class="ttc" id="classarmnn_1_1_prelu_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_prelu_layer.xhtml">armnn::PreluLayer</a></div><div class="ttdef"><b>Definition:</b> <a href="_prelu_layer_8hpp_source.xhtml#l00014">PreluLayer.hpp:14</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a9943775a364fc4ab53b85ac88f311886"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">armnn::IOutputSlot::GetTensorInfo</a></div><div class="ttdeci">virtual const TensorInfo &amp; GetTensorInfo() const =0</div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml_a7ddf0cf6f620d59c10e63495ace795d0"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a7ddf0cf6f620d59c10e63495ace795d0">armnn::Layer::GetName</a></div><div class="ttdeci">const char * GetName() const override</div><div class="ttdoc">Returns the name of the layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00305">Layer.hpp:305</a></div></div>
<div class="ttc" id="classarmnn_1_1_prelu_layer_xhtml_a8c8f543d7e9729362c266d12ec169966"><div class="ttname"><a href="classarmnn_1_1_prelu_layer.xhtml#a8c8f543d7e9729362c266d12ec169966">armnn::PreluLayer::ValidateTensorShapesFromInputs</a></div><div class="ttdeci">void ValidateTensorShapesFromInputs() override</div><div class="ttdoc">Check if the input tensor shape(s) will lead to a valid configuration of PreluLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_prelu_layer_8cpp_source.xhtml#l00097">PreluLayer.cpp:97</a></div></div>
<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_ada2ad7d1caeeb4ef6195c8925fad6a65"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#ada2ad7d1caeeb4ef6195c8925fad6a65">armnn::OutputSlot::GetTensorInfo</a></div><div class="ttdeci">const TensorInfo &amp; GetTensorInfo() const override</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00063">Layer.cpp:63</a></div></div>
<div class="ttc" id="classarmnn_1_1_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml">armnn::Layer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00209">Layer.hpp:209</a></div></div>
<div class="ttc" id="_layer_clone_base_8hpp_xhtml"><div class="ttname"><a href="_layer_clone_base_8hpp.xhtml">LayerCloneBase.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4">armnn::LayerType</a></div><div class="ttdeci">LayerType</div><div class="ttdef"><b>Definition:</b> <a href="_internal_types_8hpp_source.xhtml#l00014">InternalTypes.hpp:14</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_adf4a93f605e4e7dad6aee0b4d2159171"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#adf4a93f605e4e7dad6aee0b4d2159171">armnn::IWorkloadFactory::CreatePrelu</a></div><div class="ttdeci">virtual std::unique_ptr&lt; IWorkload &gt; CreatePrelu(const PreluQueueDescriptor &amp;descriptor, const WorkloadInfo &amp;info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01352">WorkloadFactory.cpp:1352</a></div></div>
</div><!-- fragment --></div><!-- contents -->
</div><!-- doc-content -->
<!-- start footer part -->
<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
<ul>
<li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_e0a84d05c80a2ef4231141dcbbeac5c8.xhtml">armnn</a></li><li class="navelem"><a class="el" href="dir_9da6642ce0fd5a8c83524f1b621275be.xhtml">layers</a></li><li class="navelem"><a class="el" href="_prelu_layer_8cpp.xhtml">PreluLayer.cpp</a></li>
<li class="footer">Generated on Fri Mar 13 2020 16:09:08 for ArmNN by
<a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 </li>
</ul>
</div>
</body>
</html>