Gitiles
Code Review Sign In
review.mlplatform.org / ml / armnn / 38b600d8abb2c5f7a44511b5deddf441f975d51d / . / 24.02 / _gpu_fsa_depthwise_convolution2d_8cpp_source.html
blob: bca6653ce067a3f6d49f532103ba8b20e090d5a1 [file] [log] [blame]
<!-- HTML header for doxygen 1.8.17-->
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "https://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 1.8.17"/>
<meta name="viewport" content="width=device-width, initial-scale=1"/>
<title>Arm NN: src/backends/gpuFsa/layers/GpuFsaDepthwiseConvolution2d.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>
<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" async="async" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
<link href="customdoxygen.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: 15rem; margin-top: .5rem; margin-left 13px"/>
<td id="projectalign" style="padding-left: 0.9em;">
<div id="projectname">
&#160;<span id="projectnumber">24.02</span>
</div>
</td>
</tr>
</tbody>
</table>
</div>
<!-- end header part -->
<!-- Generated by Doxygen 1.8.17 -->
<script type="text/javascript">
/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&amp;dn=gpl-2.0.txt GPL-v2 */
var searchBox = new SearchBox("searchBox", "search",false,'Search');
/* @license-end */
</script>
<script type="text/javascript" src="menudata.js"></script>
<script type="text/javascript" src="menu.js"></script>
<script type="text/javascript">
/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&amp;dn=gpl-2.0.txt GPL-v2 */
$(function() {
initMenu('',true,false,'search.php','Search');
$(document).ready(function() { init_search(); });
});
/* @license-end */</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">
/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&amp;dn=gpl-2.0.txt GPL-v2 */
$(document).ready(function(){initNavTree('_gpu_fsa_depthwise_convolution2d_8cpp_source.html',''); initResizable(); });
/* @license-end */
</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">GpuFsaDepthwiseConvolution2d.cpp</div> </div>
</div><!--header-->
<div class="contents">
<a href="_gpu_fsa_depthwise_convolution2d_8cpp.html">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 © 2024 Arm Ltd and Contributors. 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="_gpu_fsa_depthwise_convolution2d_8hpp.html">GpuFsaDepthwiseConvolution2d.hpp</a>&quot;</span></div>
<div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_utils_gpu_fsa_8hpp.html">UtilsGpuFsa.hpp</a>&quot;</span></div>
<div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160; </div>
<div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_workload_utils_8hpp.html">backendsCommon/WorkloadUtils.hpp</a>&gt;</span></div>
<div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160; </div>
<div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_arm_compute_tensor_utils_8hpp.html">aclCommon/ArmComputeTensorUtils.hpp</a>&gt;</span></div>
<div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160; </div>
<div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<span class="preprocessor">#include &lt;arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadContext.h&gt;</span></div>
<div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;<span class="preprocessor">#include &lt;arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h&gt;</span></div>
<div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="preprocessor">#include &lt;arm_compute/dynamic_fusion/sketch/gpu/operators/GpuDepthwiseConv2d.h&gt;</span></div>
<div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;<span class="preprocessor">#include &lt;arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h&gt;</span></div>
<div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160; </div>
<div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;<span class="preprocessor">#include &lt;vector&gt;</span></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;<span class="keyword">using namespace </span>arm_compute::experimental::dynamic_fusion;</div>
<div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160;<span class="keyword">using namespace </span>armnn::armcomputetensorutils;</div>
<div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160; </div>
<div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearmnn.html">armnn</a></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; </div>
<div class="line"><a name="l00026"></a><span class="lineno"><a class="line" href="namespacearmnn.html#af2016aed3575b7302e79c72830c23025"> 26</a></span>&#160;<a class="code" href="namespacearmnn.html#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a> <a class="code" href="namespacearmnn.html#af2016aed3575b7302e79c72830c23025">GpuFsaDepthwiseConvolution2dValidate</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>&amp; input,</div>
<div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160; <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html">DepthwiseConvolution2dDescriptor</a>&amp; descriptor,</div>
<div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>&amp; weights,</div>
<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.html">Optional&lt;TensorInfo&gt;</a>&amp; biases)</div>
<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;{</div>
<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160; <span class="comment">// Create a new workload sketch, for validation purposes</span></div>
<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160; <span class="keyword">auto</span> compileCtx = arm_compute::CLKernelLibrary::get().get_compile_context();</div>
<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160; <span class="keyword">auto</span> workloadContext = GpuWorkloadContext(&amp;compileCtx);</div>
<div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160; GpuWorkloadSketch sketch{ &amp;workloadContext };</div>
<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160; </div>
<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160; <span class="comment">// Build and create tensor infos using the sketch</span></div>
<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160; <span class="keyword">const</span> arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>);</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="comment">// ArmNN format for weights for depthwise is [1, H, W, C] independently of the input/output layout</span></div>
<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; <span class="comment">//</span></div>
<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; <span class="comment">// ACL format for weights for depthwise is:</span></div>
<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160; <span class="comment">// - [1, H, W, C] for [N, H, W, C] input/output layout (matches with ArmNN)</span></div>
<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; <span class="comment">// - [1, C, H, W] for [N, C, H, W] input/output layout</span></div>
<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; <span class="comment">//</span></div>
<div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; <span class="comment">// Therefore ArmNN weights have to be permuted when input/output layout is [N, C, H, W] to pass them to ACL.</span></div>
<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; <span class="comment">// The PermuteDepthwiseConv2dWeights backend optimization takes care of this, but it has not been performed yet,</span></div>
<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; <span class="comment">// so we do the permute here for the TensorInfo weights.</span></div>
<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> aclDepthMultiplier;</div>
<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> weightsPermuted;</div>
<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; std::tie(weightsPermuted, aclDepthMultiplier) = <a class="code" href="namespacearmnn.html#ac4aa9e41515b354234645f115c49de32">Convert1HWOTensorInfoToAcl</a>(weights, input,descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>);</div>
<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; <span class="keyword">auto</span> weightsShape = weightsPermuted.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div>
<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; weightsPermuted.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({weightsShape[1], weightsShape[2], weightsShape[3]});</div>
<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; </div>
<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; arm_compute::TensorInfo aclWeightsInfo = BuildArmComputeTensorInfo(weightsPermuted, descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>);</div>
<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; aclWeightsInfo.set_are_values_constant(weights.<a class="code" href="classarmnn_1_1_tensor_info.html#a945263e85c27f3216a8323cfc16d8919">IsConstant</a>());</div>
<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; </div>
<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; <span class="keyword">auto</span> inputInfo = workloadContext.create_tensor_info(aclInputInfo);</div>
<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; <span class="keyword">auto</span> weightInfo = workloadContext.create_tensor_info(aclWeightsInfo);</div>
<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; </div>
<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; <span class="comment">// Only create the bias tensor info if enabled, otherwise pass nullptr to validate_op</span></div>
<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; arm_compute::TensorInfo aclBiasInfo;</div>
<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; arm_compute::ITensorInfo* biasSketchInfoPtr = <span class="keyword">nullptr</span>;</div>
<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; </div>
<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; <span class="keywordflow">if</span> (descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div>
<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; {</div>
<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; <span class="keywordflow">if</span>(!biases.<a class="code" href="classarmnn_1_1_optional_base.html#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>())</div>
<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; {</div>
<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.html">InvalidArgumentException</a>(</div>
<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; <span class="stringliteral">&quot;GpuFsaDepthwiseConvolution2dValidate: No biases set when biases are enabled&quot;</span>);</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; aclBiasInfo = BuildArmComputeTensorInfo(biases.<a class="code" href="classarmnn_1_1_optional_reference_switch.html#a77c7d528ac063d870b8c8426ec81c1c3">value</a>(), descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>);</div>
<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; aclBiasInfo.set_are_values_constant(biases.<a class="code" href="classarmnn_1_1_optional_reference_switch.html#a77c7d528ac063d870b8c8426ec81c1c3">value</a>().IsConstant());</div>
<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; </div>
<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; biasSketchInfoPtr = workloadContext.create_tensor_info(aclBiasInfo);</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; </div>
<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; DepthwiseConv2dAttributes depthwiseConv2dAttributes = <a class="code" href="_utils_gpu_fsa_8cpp.html#a54d1f7200479dad24822853a1e1da500">CreateDWConv2dAttributes</a>(descriptor, aclDepthMultiplier);</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; <span class="comment">// Validate operator, check status and update reasonIfUnsupported</span></div>
<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; <a class="code" href="namespacearmnn.html#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a> aclStatus = GpuDepthwiseConv2d::validate_op(sketch,</div>
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; inputInfo,</div>
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; weightInfo,</div>
<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; biasSketchInfoPtr,</div>
<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; depthwiseConv2dAttributes);</div>
<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; </div>
<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; <span class="keywordflow">return</span> aclStatus;</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; </div>
<div class="line"><a name="l00089"></a><span class="lineno"><a class="line" href="namespacearmnn.html#a5bdf4240a1fbda27b5fc84baba721781"> 89</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacearmnn.html#a5bdf4240a1fbda27b5fc84baba721781">GpuFsaDepthwiseConvolution2dCreateOp</a>(<a class="code" href="structarmnn_1_1_gpu_fsa_pre_compiled_blob.html">GpuFsaPreCompiledBlob</a>* blob,</div>
<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>&amp; input,</div>
<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html">DepthwiseConvolution2dDescriptor</a>&amp; descriptor,</div>
<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>&amp; weights,</div>
<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.html">Optional&lt;TensorInfo&gt;</a>&amp; biases)</div>
<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160;{</div>
<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160;<span class="comment">/*</span></div>
<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160;<span class="comment">* Creating an Op for the GpuFsa backend requires us to create and maintain quite a bit of data, which is then stored</span></div>
<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160;<span class="comment">* in a GpuFsaPreCompiledBlob for execution later. Specifically we need:</span></div>
<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160;<span class="comment">* GpuWorkloadContext, this contains the TensorInfos and is unique to the Graph being executed</span></div>
<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160;<span class="comment">* Sketch, this is similar to a subgraph and can contain one or more operations. Multiple ops can be &quot;fused&quot; together</span></div>
<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160;<span class="comment">* using a single sketch.</span></div>
<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160;<span class="comment">* The inputTensorinfos / outputTensorInfos, these are pointers to the TensorInfos used when creating the sketch.</span></div>
<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160;<span class="comment">* They refer to the TensorInfos stored within the GpuWorkloadContext and are needed when executing the sketch</span></div>
<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160;<span class="comment">* as the TensorInfos used when creating the Tensors must match those used to create the Sketch. Otherwise the runtime</span></div>
<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160;<span class="comment">* doesn&#39;t know which Tensors to use.</span></div>
<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160;<span class="comment">*/</span></div>
<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; GpuWorkloadSketch* sketch = blob-&gt;<a class="code" href="structarmnn_1_1_gpu_fsa_pre_compiled_blob.html#a14f92a9f65e32c3da896e7b1d45abd02">sketch</a>.get();</div>
<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; GpuWorkloadContext* workloadContext = blob-&gt;<a class="code" href="structarmnn_1_1_gpu_fsa_pre_compiled_blob.html#a255c9012137b149ffb46d83c23f2df43">workloadContext</a>.get();</div>
<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; std::vector&lt;arm_compute::ITensorInfo*&gt; inputTensorInfos = {};</div>
<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; std::vector&lt;arm_compute::ITensorInfo*&gt; outputTensorInfos = {};</div>
<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; </div>
<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; <span class="comment">// Build and create tensor infos using the sketch</span></div>
<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; <span class="keyword">const</span> arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>);</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; <span class="comment">// ArmNN format for weights for depthwise is [1, H, W, C] independently of the input/output layout</span></div>
<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; <span class="comment">//</span></div>
<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; <span class="comment">// ACL format for weights for depthwise is:</span></div>
<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; <span class="comment">// - [1, H, W, C] for [N, H, W, C] input/output layout (matches with ArmNN)</span></div>
<div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; <span class="comment">// - [1, C, H, W] for [N, C, H, W] input/output layout</span></div>
<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; <span class="comment">//</span></div>
<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <span class="comment">// Therefore ArmNN weights have to be permuted when input/output layout is [N, C, H, W] to pass them to ACL.</span></div>
<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; <span class="comment">// The PermuteDepthwiseConv2dWeights backend optimization takes care of this, but it has not been performed yet,</span></div>
<div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; <span class="comment">// so we do the permute here for the TensorInfo weights.</span></div>
<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> aclDepthMultiplier;</div>
<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a> weightsPermuted;</div>
<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; std::tie(weightsPermuted, aclDepthMultiplier) = <a class="code" href="namespacearmnn.html#ac4aa9e41515b354234645f115c49de32">Convert1HWOTensorInfoToAcl</a>(weights, input,descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>);</div>
<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; <span class="keyword">auto</span> weightsShape = weightsPermuted.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div>
<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; weightsPermuted.<a class="code" href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({weightsShape[1], weightsShape[2], weightsShape[3]});</div>
<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; </div>
<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; arm_compute::TensorInfo aclWeightsInfo = BuildArmComputeTensorInfo(weightsPermuted, descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>);</div>
<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; aclWeightsInfo.set_are_values_constant(weights.<a class="code" href="classarmnn_1_1_tensor_info.html#a945263e85c27f3216a8323cfc16d8919">IsConstant</a>());</div>
<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; </div>
<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; inputTensorInfos.emplace_back(workloadContext-&gt;create_tensor_info(aclInputInfo));</div>
<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; inputTensorInfos.emplace_back(workloadContext-&gt;create_tensor_info(aclWeightsInfo));</div>
<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; </div>
<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; <span class="comment">// Only create the bias tensor info if enabled, otherwise pass nullptr to validate_op</span></div>
<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; arm_compute::TensorInfo aclBiasInfo;</div>
<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; arm_compute::ITensorInfo* biasSketchInfoPtr = <span class="keyword">nullptr</span>;</div>
<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; </div>
<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; <span class="keywordflow">if</span> (descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</div>
<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; {</div>
<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; <span class="keywordflow">if</span>(!biases.<a class="code" href="classarmnn_1_1_optional_base.html#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>())</div>
<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; {</div>
<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.html">InvalidArgumentException</a>(<span class="stringliteral">&quot;GpuFsaConvolution2dValidate: No biases set when biases are enabled&quot;</span>);</div>
<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; }</div>
<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; aclBiasInfo = BuildArmComputeTensorInfo(biases.<a class="code" href="classarmnn_1_1_optional_reference_switch.html#a77c7d528ac063d870b8c8426ec81c1c3">value</a>(), descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>);</div>
<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; aclBiasInfo.set_are_values_constant(biases.<a class="code" href="classarmnn_1_1_optional_reference_switch.html#a77c7d528ac063d870b8c8426ec81c1c3">value</a>().IsConstant());</div>
<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; </div>
<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; inputTensorInfos.emplace_back(workloadContext-&gt;create_tensor_info(aclBiasInfo));</div>
<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; biasSketchInfoPtr = inputTensorInfos[2];</div>
<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; }</div>
<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; </div>
<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; DepthwiseConv2dAttributes depthwiseConv2dAttributes = <a class="code" href="_utils_gpu_fsa_8cpp.html#a54d1f7200479dad24822853a1e1da500">CreateDWConv2dAttributes</a>(descriptor, aclDepthMultiplier);</div>
<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; </div>
<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; <span class="comment">// Validate operator, check status and update reasonIfUnsupported</span></div>
<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; <a class="code" href="namespacearmnn.html#a67a0db04d321a74b7e7fcfd3f1a3f70b">arm_compute::Status</a> aclStatus = GpuDepthwiseConv2d::validate_op(*sketch,</div>
<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; inputTensorInfos[0],</div>
<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; inputTensorInfos[1],</div>
<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; biasSketchInfoPtr,</div>
<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; depthwiseConv2dAttributes);</div>
<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160; </div>
<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; <span class="keyword">const</span> <span class="keywordtype">bool</span> supported = (aclStatus.error_code() == arm_compute::ErrorCode::OK);</div>
<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; <span class="keywordflow">if</span> (!supported)</div>
<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; {</div>
<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_backend_capability_exception.html">BackendCapabilityException</a>(</div>
<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; <span class="stringliteral">&quot;\&quot;GpuFsa\&quot; backend failed during DepthwiseConvolution2D operation validation&quot;</span>);</div>
<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; }</div>
<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160; </div>
<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; <span class="comment">// Create the Op within the Sketch using the TensorInfos we have stored</span></div>
<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160; arm_compute::ITensorInfo* convOutInfo = GpuDepthwiseConv2d::create_op(*sketch,</div>
<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160; inputTensorInfos[0],</div>
<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; inputTensorInfos[1],</div>
<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; biasSketchInfoPtr,</div>
<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; depthwiseConv2dAttributes);</div>
<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; </div>
<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; outputTensorInfos.emplace_back(workloadContext-&gt;create_tensor_info());</div>
<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; GpuOutput::create_op(*sketch, convOutInfo, outputTensorInfos[0]);</div>
<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; </div>
<div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; <span class="comment">// Store the TensorInfos within the blob as unique_ptrs to be used later</span></div>
<div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; blob-&gt;<a class="code" href="structarmnn_1_1_gpu_fsa_pre_compiled_blob.html#a479b90f0b24c427502d94b716117e034">inputTensorInfos</a> = std::make_unique&lt;std::vector&lt;arm_compute::ITensorInfo*&gt;&gt;(inputTensorInfos);</div>
<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; blob-&gt;<a class="code" href="structarmnn_1_1_gpu_fsa_pre_compiled_blob.html#ac49bf679a23aa84f06a6bde3440a4c40">outputTensorInfos</a> = std::make_unique&lt;std::vector&lt;arm_compute::ITensorInfo*&gt;&gt;(outputTensorInfos);</div>
<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160;}</div>
<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; </div>
<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160;} <span class="comment">// namespace armnn</span></div>
</div><!-- fragment --></div><!-- contents -->
</div><!-- doc-content -->
<div class="ttc" id="anamespacearmnn_html_ac4aa9e41515b354234645f115c49de32"><div class="ttname"><a href="namespacearmnn.html#ac4aa9e41515b354234645f115c49de32">armnn::Convert1HWOTensorInfoToAcl</a></div><div class="ttdeci">std::tuple&lt; TensorInfo, unsigned int &gt; Convert1HWOTensorInfoToAcl(const TensorInfo &amp;weightInfo, const TensorInfo &amp;inputInfo, const DataLayout dataLayout)</div><div class="ttdoc">Weights for depthwise have a datalayout of [1,H,W,O] = [1,H,W,I*M] This function coverts a TensorInfo...</div><div class="ttdef"><b>Definition:</b> <a href="_workload_utils_8cpp_source.html#l00176">WorkloadUtils.cpp:176</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_optional_html"><div class="ttname"><a href="classarmnn_1_1_optional.html">armnn::Optional</a></div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.html#l00270">Optional.hpp:270</a></div></div>
<div class="ttc" id="astructarmnn_1_1_depthwise_convolution2d_descriptor_html_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">armnn::DepthwiseConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00708">Descriptors.hpp:708</a></div></div>
<div class="ttc" id="a_workload_utils_8hpp_html"><div class="ttname"><a href="_workload_utils_8hpp.html">WorkloadUtils.hpp</a></div></div>
<div class="ttc" id="astructarmnn_1_1_depthwise_convolution2d_descriptor_html_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#a6089e1ca91914015777ea780a513131a">armnn::DepthwiseConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC).</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00710">Descriptors.hpp:710</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_tensor_info_html"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a945263e85c27f3216a8323cfc16d8919"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a945263e85c27f3216a8323cfc16d8919">armnn::TensorInfo::IsConstant</a></div><div class="ttdeci">bool IsConstant() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00509">Tensor.cpp:509</a></div></div>
<div class="ttc" id="astructarmnn_1_1_gpu_fsa_pre_compiled_blob_html_a479b90f0b24c427502d94b716117e034"><div class="ttname"><a href="structarmnn_1_1_gpu_fsa_pre_compiled_blob.html#a479b90f0b24c427502d94b716117e034">armnn::GpuFsaPreCompiledBlob::inputTensorInfos</a></div><div class="ttdeci">std::unique_ptr&lt; std::vector&lt; arm_compute::ITensorInfo * &gt; &gt; inputTensorInfos</div><div class="ttdef"><b>Definition:</b> <a href="_gpu_fsa_backend_8hpp_source.html#l00037">GpuFsaBackend.hpp:37</a></div></div>
<div class="ttc" id="a_utils_gpu_fsa_8cpp_html_a54d1f7200479dad24822853a1e1da500"><div class="ttname"><a href="_utils_gpu_fsa_8cpp.html#a54d1f7200479dad24822853a1e1da500">CreateDWConv2dAttributes</a></div><div class="ttdeci">arm_compute::experimental::dynamic_fusion::DepthwiseConv2dAttributes CreateDWConv2dAttributes(const DepthwiseConvolution2dDescriptor &amp;descriptor, const unsigned int aclDepthMultiplier)</div><div class="ttdoc">Utility function used to setup an arm_compute::DepthwiseConv2dAttributes object from given descriptor...</div><div class="ttdef"><b>Definition:</b> <a href="_utils_gpu_fsa_8cpp_source.html#l00029">UtilsGpuFsa.cpp:29</a></div></div>
<div class="ttc" id="astructarmnn_1_1_gpu_fsa_pre_compiled_blob_html_a14f92a9f65e32c3da896e7b1d45abd02"><div class="ttname"><a href="structarmnn_1_1_gpu_fsa_pre_compiled_blob.html#a14f92a9f65e32c3da896e7b1d45abd02">armnn::GpuFsaPreCompiledBlob::sketch</a></div><div class="ttdeci">std::unique_ptr&lt; arm_compute::experimental::dynamic_fusion::GpuWorkloadSketch &gt; sketch</div><div class="ttdef"><b>Definition:</b> <a href="_gpu_fsa_backend_8hpp_source.html#l00034">GpuFsaBackend.hpp:34</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_invalid_argument_exception_html"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.html">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.html#l00080">Exceptions.hpp:80</a></div></div>
<div class="ttc" id="anamespacearmnn_html_a5bdf4240a1fbda27b5fc84baba721781"><div class="ttname"><a href="namespacearmnn.html#a5bdf4240a1fbda27b5fc84baba721781">armnn::GpuFsaDepthwiseConvolution2dCreateOp</a></div><div class="ttdeci">void GpuFsaDepthwiseConvolution2dCreateOp(GpuFsaPreCompiledBlob *blob, const TensorInfo &amp;input, const DepthwiseConvolution2dDescriptor &amp;descriptor, const TensorInfo &amp;weights, const Optional&lt; TensorInfo &gt; &amp;biases)</div><div class="ttdef"><b>Definition:</b> <a href="_gpu_fsa_depthwise_convolution2d_8cpp_source.html#l00089">GpuFsaDepthwiseConvolution2d.cpp:89</a></div></div>
<div class="ttc" id="a_gpu_fsa_depthwise_convolution2d_8hpp_html"><div class="ttname"><a href="_gpu_fsa_depthwise_convolution2d_8hpp.html">GpuFsaDepthwiseConvolution2d.hpp</a></div></div>
<div class="ttc" id="anamespacearmnn_html_a67a0db04d321a74b7e7fcfd3f1a3f70b"><div class="ttname"><a href="namespacearmnn.html#a67a0db04d321a74b7e7fcfd3f1a3f70b">armnn::Status</a></div><div class="ttdeci">Status</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00042">Types.hpp:42</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#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.html#l00193">Tensor.hpp:193</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_backend_capability_exception_html"><div class="ttname"><a href="classarmnn_1_1_backend_capability_exception.html">armnn::BackendCapabilityException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.html#l00152">Exceptions.hpp:152</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_tensor_info_html_abe8889e8150beef5fd204b2d87b49298"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">armnn::TensorInfo::SetShape</a></div><div class="ttdeci">void SetShape(const TensorShape &amp;newShape)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00195">Tensor.hpp:195</a></div></div>
<div class="ttc" id="anamespacearmnn_html"><div class="ttname"><a href="namespacearmnn.html">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors.</div><div class="ttdef"><b>Definition:</b> <a href="01__00__quick__start_8dox_source.html#l00006">01_00_quick_start.dox:6</a></div></div>
<div class="ttc" id="a_arm_compute_tensor_utils_8hpp_html"><div class="ttname"><a href="_arm_compute_tensor_utils_8hpp.html">ArmComputeTensorUtils.hpp</a></div></div>
<div class="ttc" id="astructarmnn_1_1_gpu_fsa_pre_compiled_blob_html_a255c9012137b149ffb46d83c23f2df43"><div class="ttname"><a href="structarmnn_1_1_gpu_fsa_pre_compiled_blob.html#a255c9012137b149ffb46d83c23f2df43">armnn::GpuFsaPreCompiledBlob::workloadContext</a></div><div class="ttdeci">std::shared_ptr&lt; arm_compute::experimental::dynamic_fusion::GpuWorkloadContext &gt; workloadContext</div><div class="ttdef"><b>Definition:</b> <a href="_gpu_fsa_backend_8hpp_source.html#l00035">GpuFsaBackend.hpp:35</a></div></div>
<div class="ttc" id="astructarmnn_1_1_gpu_fsa_pre_compiled_blob_html"><div class="ttname"><a href="structarmnn_1_1_gpu_fsa_pre_compiled_blob.html">armnn::GpuFsaPreCompiledBlob</a></div><div class="ttdoc">A structure which contains all the elements needed to execute a fused workload in the GpuFsa Backend.</div><div class="ttdef"><b>Definition:</b> <a href="_gpu_fsa_backend_8hpp_source.html#l00032">GpuFsaBackend.hpp:32</a></div></div>
<div class="ttc" id="a_utils_gpu_fsa_8hpp_html"><div class="ttname"><a href="_utils_gpu_fsa_8hpp.html">UtilsGpuFsa.hpp</a></div></div>
<div class="ttc" id="anamespacearmnn_html_af2016aed3575b7302e79c72830c23025"><div class="ttname"><a href="namespacearmnn.html#af2016aed3575b7302e79c72830c23025">armnn::GpuFsaDepthwiseConvolution2dValidate</a></div><div class="ttdeci">arm_compute::Status GpuFsaDepthwiseConvolution2dValidate(const TensorInfo &amp;input, const DepthwiseConvolution2dDescriptor &amp;descriptor, const TensorInfo &amp;weights, const Optional&lt; TensorInfo &gt; &amp;biases)</div><div class="ttdef"><b>Definition:</b> <a href="_gpu_fsa_depthwise_convolution2d_8cpp_source.html#l00026">GpuFsaDepthwiseConvolution2d.cpp:26</a></div></div>
<div class="ttc" id="astructarmnn_1_1_depthwise_convolution2d_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.html">armnn::DepthwiseConvolution2dDescriptor</a></div><div class="ttdoc">A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00659">Descriptors.hpp:659</a></div></div>
<div class="ttc" id="astructarmnn_1_1_gpu_fsa_pre_compiled_blob_html_ac49bf679a23aa84f06a6bde3440a4c40"><div class="ttname"><a href="structarmnn_1_1_gpu_fsa_pre_compiled_blob.html#ac49bf679a23aa84f06a6bde3440a4c40">armnn::GpuFsaPreCompiledBlob::outputTensorInfos</a></div><div class="ttdeci">std::unique_ptr&lt; std::vector&lt; arm_compute::ITensorInfo * &gt; &gt; outputTensorInfos</div><div class="ttdef"><b>Definition:</b> <a href="_gpu_fsa_backend_8hpp_source.html#l00038">GpuFsaBackend.hpp:38</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_optional_reference_switch_html_a77c7d528ac063d870b8c8426ec81c1c3"><div class="ttname"><a href="classarmnn_1_1_optional_reference_switch.html#a77c7d528ac063d870b8c8426ec81c1c3">armnn::OptionalReferenceSwitch&lt; std::is_reference&lt; T &gt;::value, T &gt;::value</a></div><div class="ttdeci">const T &amp; value() const</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.html#l00146">Optional.hpp:146</a></div></div>
<div class="ttc" id="aclassarmnn_1_1_optional_base_html_a86b749ce2c4bc627fa8a1fcfaf0e314f"><div class="ttname"><a href="classarmnn_1_1_optional_base.html#a86b749ce2c4bc627fa8a1fcfaf0e314f">armnn::OptionalBase::has_value</a></div><div class="ttdeci">bool has_value() const noexcept</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.html#l00053">Optional.hpp:53</a></div></div>
<!-- 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.html">src</a></li><li class="navelem"><a class="el" href="dir_0f3cdec46afbc61a1ded8e1687c9c9a0.html">backends</a></li><li class="navelem"><a class="el" href="dir_dd5880bc3520e42c5318e86a9fdc97f6.html">gpuFsa</a></li><li class="navelem"><a class="el" href="dir_6735d107a3ea44e973a098b746f1b3bd.html">layers</a></li><li class="navelem"><a class="el" href="_gpu_fsa_depthwise_convolution2d_8cpp.html">GpuFsaDepthwiseConvolution2d.cpp</a></li>
<li class="footer">Generated on Wed Feb 14 2024 16:36:16 for Arm NN by
<a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.17 </li>
</ul>
</div>
</body>
</html>