| <!-- HTML header for doxygen 1.8.15--> |
| <!-- Remember to use version doxygen 1.8.15 +--> |
| <!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 1.8.15"/> |
| <meta name="robots" content="NOINDEX, NOFOLLOW" /> <!-- Prevent indexing by search engines --> |
| <title>Compute Library: NEGEMMConvolutionLayer Class Reference</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"> |
| /* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt GPL-v2 */ |
| $(document).ready(initResizable); |
| /* @license-end */</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="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="Compute Library" src="https://raw.githubusercontent.com/ARM-software/ComputeLibrary/gh-pages/ACL_logo.png" style="max-width: 100%;margin-top: 15px;margin-left: 10px"/> |
| <td style="padding-left: 0.5em;"> |
| <div id="projectname"> |
|  <span id="projectnumber">20.02.1</span> |
| </div> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <!-- end header part --> |
| <!-- Generated by Doxygen 1.8.15 --> |
| <script type="text/javascript"> |
| /* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&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&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&dn=gpl-2.0.txt GPL-v2 */ |
| $(document).ready(function(){initNavTree('classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml','');}); |
| /* @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="summary"> |
| <a href="#pub-methods">Public Member Functions</a> | |
| <a href="#pub-static-methods">Static Public Member Functions</a> </div> |
| <div class="headertitle"> |
| <div class="title">NEGEMMConvolutionLayer Class Reference</div> </div> |
| </div><!--header--> |
| <div class="contents"> |
| |
| <p>Basic function to compute the convolution layer. |
| <a href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml#details">More...</a></p> |
| |
| <p><code>#include <<a class="el" href="_n_e_g_e_m_m_convolution_layer_8h_source.xhtml">NEGEMMConvolutionLayer.h</a>></code></p> |
| <div class="dynheader"> |
| Collaboration diagram for NEGEMMConvolutionLayer:</div> |
| <div class="dyncontent"> |
| <div class="center"><iframe scrolling="no" frameborder="0" src="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer__coll__graph.svg" width="202" height="112"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe> |
| </div> |
| <center><span class="legend">[<a target="top" href="graph_legend.xhtml">legend</a>]</span></center></div> |
| <table class="memberdecls"> |
| <tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a> |
| Public Member Functions</h2></td></tr> |
| <tr class="memitem:a627cee3df543519d4a134291189a3b9e"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml#a627cee3df543519d4a134291189a3b9e">NEGEMMConvolutionLayer</a> (const std::shared_ptr< <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> > &memory_manager=nullptr, <a class="el" href="classarm__compute_1_1_i_weights_manager.xhtml">IWeightsManager</a> *weights_manager=nullptr)</td></tr> |
| <tr class="memdesc:a627cee3df543519d4a134291189a3b9e"><td class="mdescLeft"> </td><td class="mdescRight">Constructor. <a href="#a627cee3df543519d4a134291189a3b9e">More...</a><br /></td></tr> |
| <tr class="separator:a627cee3df543519d4a134291189a3b9e"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:af4d64dcaa798d09b11067f19b3c48ebe"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml#af4d64dcaa798d09b11067f19b3c48ebe">NEGEMMConvolutionLayer</a> (const <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a> &)=delete</td></tr> |
| <tr class="memdesc:af4d64dcaa798d09b11067f19b3c48ebe"><td class="mdescLeft"> </td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers) <a href="#af4d64dcaa798d09b11067f19b3c48ebe">More...</a><br /></td></tr> |
| <tr class="separator:af4d64dcaa798d09b11067f19b3c48ebe"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a24fed20754466c50bb2c823444556a6d"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml#a24fed20754466c50bb2c823444556a6d">NEGEMMConvolutionLayer</a> (<a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a> &&)=default</td></tr> |
| <tr class="memdesc:a24fed20754466c50bb2c823444556a6d"><td class="mdescLeft"> </td><td class="mdescRight">Default move constructor. <a href="#a24fed20754466c50bb2c823444556a6d">More...</a><br /></td></tr> |
| <tr class="separator:a24fed20754466c50bb2c823444556a6d"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:ac13c7e80f7e9e6f1e2912ef50678c5c9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml#ac13c7e80f7e9e6f1e2912ef50678c5c9">operator=</a> (const <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a> &)=delete</td></tr> |
| <tr class="memdesc:ac13c7e80f7e9e6f1e2912ef50678c5c9"><td class="mdescLeft"> </td><td class="mdescRight">Prevent instances of this class from being copied (As this class contains pointers) <a href="#ac13c7e80f7e9e6f1e2912ef50678c5c9">More...</a><br /></td></tr> |
| <tr class="separator:ac13c7e80f7e9e6f1e2912ef50678c5c9"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a0a7030af14bb803820435ca75009b0f9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml#a0a7030af14bb803820435ca75009b0f9">operator=</a> (<a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a> &&)=default</td></tr> |
| <tr class="memdesc:a0a7030af14bb803820435ca75009b0f9"><td class="mdescLeft"> </td><td class="mdescRight">Default move assignment operator. <a href="#a0a7030af14bb803820435ca75009b0f9">More...</a><br /></td></tr> |
| <tr class="separator:a0a7030af14bb803820435ca75009b0f9"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:a97f4fd717623515cacaa206a889933ce"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml#a97f4fd717623515cacaa206a889933ce">configure</a> (const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *input, const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *weights, const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *biases, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output, const <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &conv_info, const <a class="el" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a> &weights_info=<a class="el" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>(), const <a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &dilation=<a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1U, 1U), const ActivationLayerInfo &act_info=ActivationLayerInfo(), unsigned int num_groups=1)</td></tr> |
| <tr class="memdesc:a97f4fd717623515cacaa206a889933ce"><td class="mdescLeft"> </td><td class="mdescRight">Set the input and output tensors. <a href="#a97f4fd717623515cacaa206a889933ce">More...</a><br /></td></tr> |
| <tr class="separator:a97f4fd717623515cacaa206a889933ce"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:ad1717410afd0be936c6213a63c8005fb"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a> () override</td></tr> |
| <tr class="memdesc:ad1717410afd0be936c6213a63c8005fb"><td class="mdescLeft"> </td><td class="mdescRight">Run the kernels contained in the function. <a href="#ad1717410afd0be936c6213a63c8005fb">More...</a><br /></td></tr> |
| <tr class="separator:ad1717410afd0be936c6213a63c8005fb"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="memitem:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a> () override</td></tr> |
| <tr class="memdesc:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="mdescLeft"> </td><td class="mdescRight">Prepare the function for executing. <a href="#aa9b93ef660fc3c5b4b19d3fc7b891b77">More...</a><br /></td></tr> |
| <tr class="separator:aa9b93ef660fc3c5b4b19d3fc7b891b77"><td class="memSeparator" colspan="2"> </td></tr> |
| <tr class="inherit_header pub_methods_classarm__compute_1_1_i_function"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classarm__compute_1_1_i_function')"><img src="closed.png" alt="-"/> Public Member Functions inherited from <a class="el" href="classarm__compute_1_1_i_function.xhtml">IFunction</a></td></tr> |
| <tr class="memitem:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memItemLeft" align="right" valign="top">virtual </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_i_function.xhtml#ab921ecc3f3f6ae2b4bd61f3e1998d8c4">~IFunction</a> ()=default</td></tr> |
| <tr class="memdesc:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="mdescLeft"> </td><td class="mdescRight">Destructor. <a href="classarm__compute_1_1_i_function.xhtml#ab921ecc3f3f6ae2b4bd61f3e1998d8c4">More...</a><br /></td></tr> |
| <tr class="separator:ab921ecc3f3f6ae2b4bd61f3e1998d8c4 inherit pub_methods_classarm__compute_1_1_i_function"><td class="memSeparator" colspan="2"> </td></tr> |
| </table><table class="memberdecls"> |
| <tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a> |
| Static Public Member Functions</h2></td></tr> |
| <tr class="memitem:a3113fd3147c1bbc06b3f9890063c87c7"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classarm__compute_1_1_status.xhtml">Status</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml#a3113fd3147c1bbc06b3f9890063c87c7">validate</a> (const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *weights, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *biases, const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output, const <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &conv_info, const <a class="el" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a> &weights_info=<a class="el" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>(), const <a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &dilation=<a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1U, 1U), const ActivationLayerInfo &act_info=ActivationLayerInfo(), unsigned int num_groups=1)</td></tr> |
| <tr class="memdesc:a3113fd3147c1bbc06b3f9890063c87c7"><td class="mdescLeft"> </td><td class="mdescRight">Static function to check if given info will lead to a valid configuration of <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a>. <a href="#a3113fd3147c1bbc06b3f9890063c87c7">More...</a><br /></td></tr> |
| <tr class="separator:a3113fd3147c1bbc06b3f9890063c87c7"><td class="memSeparator" colspan="2"> </td></tr> |
| </table> |
| <a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2> |
| <div class="textblock"><p>Basic function to compute the convolution layer. </p> |
| <p>This function calls the following NEON kernels/functions:</p> |
| <ol type="1"> |
| <li><a class="el" href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml">NEIm2ColKernel</a></li> |
| <li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml">NEGEMM</a> (if the data type is FP32 or FP16)</li> |
| <li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core.xhtml">NEGEMMLowpMatrixMultiplyCore</a> (if the data type is QASYMM8/QASYMM8_SIGNED)</li> |
| <li><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_quantize_down_int32_to_uint8_scale_by_fixed_point.xhtml">NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint</a> (if the data type is QASYMM8/QASYMM8_SIGNED)</li> |
| <li><a class="el" href="classarm__compute_1_1_n_e_arithmetic_addition_kernel.xhtml">NEArithmeticAdditionKernel</a> (if biases != nullptr and we have a 1x1 convolution with the NHWC data layout)</li> |
| <li><a class="el" href="classarm__compute_1_1_n_e_col2_im_kernel.xhtml">NECol2ImKernel</a> (if NCHW data layout) </li> |
| </ol> |
| |
| <p class="definition">Definition at line <a class="el" href="_n_e_g_e_m_m_convolution_layer_8h_source.xhtml#l00146">146</a> of file <a class="el" href="_n_e_g_e_m_m_convolution_layer_8h_source.xhtml">NEGEMMConvolutionLayer.h</a>.</p> |
| </div><h2 class="groupheader">Constructor & Destructor Documentation</h2> |
| <a id="a627cee3df543519d4a134291189a3b9e"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a627cee3df543519d4a134291189a3b9e">◆ </a></span>NEGEMMConvolutionLayer() <span class="overload">[1/3]</span></h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a> </td> |
| <td>(</td> |
| <td class="paramtype">const std::shared_ptr< <a class="el" href="classarm__compute_1_1_i_memory_manager.xhtml">IMemoryManager</a> > & </td> |
| <td class="paramname"><em>memory_manager</em> = <code>nullptr</code>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_i_weights_manager.xhtml">IWeightsManager</a> * </td> |
| <td class="paramname"><em>weights_manager</em> = <code>nullptr</code> </td> |
| </tr> |
| <tr> |
| <td></td> |
| <td>)</td> |
| <td></td><td></td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Constructor. </p> |
| |
| <p class="definition">Definition at line <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00092">92</a> of file <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml">NEGEMMConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  : _memory_group(memory_manager), _weights_manager(weights_manager), _reshape_weights(), _reshape_weights_managed(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager),</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  _col2im_kernel(), _reshape_layer(), _original_weights(<span class="keyword">nullptr</span>), _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _data_layout(<a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>), _skip_im2col(<span class="keyword">false</span>),</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  _skip_col2im(<span class="keyword">false</span>), _is_quantized(<span class="keyword">false</span>), _is_prepared(<span class="keyword">false</span>)</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> {</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> }</div><div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a></div><div class="ttdoc">Num samples, channels, height, width.</div></div> |
| </div><!-- fragment --> |
| </div> |
| </div> |
| <a id="af4d64dcaa798d09b11067f19b3c48ebe"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#af4d64dcaa798d09b11067f19b3c48ebe">◆ </a></span>NEGEMMConvolutionLayer() <span class="overload">[2/3]</span></h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a> </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a> & </td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">delete</span></span> </td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Prevent instances of this class from being copied (As this class contains pointers) </p> |
| |
| </div> |
| </div> |
| <a id="a24fed20754466c50bb2c823444556a6d"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a24fed20754466c50bb2c823444556a6d">◆ </a></span>NEGEMMConvolutionLayer() <span class="overload">[3/3]</span></h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a> </td> |
| <td>(</td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a> && </td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">default</span></span> </td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Default move constructor. </p> |
| |
| </div> |
| </div> |
| <h2 class="groupheader">Member Function Documentation</h2> |
| <a id="a97f4fd717623515cacaa206a889933ce"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a97f4fd717623515cacaa206a889933ce">◆ </a></span>configure()</h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="memname"> |
| <tr> |
| <td class="memname">void configure </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> * </td> |
| <td class="paramname"><em>input</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> * </td> |
| <td class="paramname"><em>weights</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> * </td> |
| <td class="paramname"><em>biases</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> * </td> |
| <td class="paramname"><em>output</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> & </td> |
| <td class="paramname"><em>conv_info</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a> & </td> |
| <td class="paramname"><em>weights_info</em> = <code><a class="el" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>()</code>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> & </td> |
| <td class="paramname"><em>dilation</em> = <code><a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1U, 1U)</code>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> & </td> |
| <td class="paramname"><em>act_info</em> = <code><a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>()</code>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">unsigned int </td> |
| <td class="paramname"><em>num_groups</em> = <code>1</code> </td> |
| </tr> |
| <tr> |
| <td></td> |
| <td>)</td> |
| <td></td><td></td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Set the input and output tensors. </p> |
| <dl class="params"><dt>Parameters</dt><dd> |
| <table class="params"> |
| <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Source tensor. 3 lower dimensions represent a single input [width, height, IFM], while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">weights</td><td>Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/F16/F32. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">biases</td><td>Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Should match <code>input</code> data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type. </td></tr> |
| <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. Data types supported: Same as <code>input</code>. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">conv_info</td><td>Contains padding and stride information described in <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">weights_info</td><td>Specifies if the weights tensor has been reshaped with <a class="el" href="classarm__compute_1_1_n_e_weights_reshape_kernel.xhtml" title="NEON kernel to perform reshaping on the weights used by convolution and locally connected layer.">NEWeightsReshapeKernel</a>. If this is not part of the fully connected layer the weights tensor has also been transposed with <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_transpose1x_w_kernel.xhtml" title="NEON kernel which transposes the elements of a matrix in chunks of 1xW, where W is equal to (16 / ele...">NEGEMMTranspose1xWKernel</a>. Data type supported: Same as <code>input</code>. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">dilation</td><td>(Optional) Dilation, in elements, across x and y. Defaults to (1, 1). </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">act_info</td><td>(Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">num_groups</td><td>(Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported </td></tr> |
| </table> |
| </dd> |
| </dl> |
| |
| <p class="definition">Definition at line <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00240">240</a> of file <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml">NEGEMMConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><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>  <a class="code" href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a>(<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>, output);</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7cb842ebfe255726066039853a4322f0">weights_info</a>);</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml#a3113fd3147c1bbc06b3f9890063c87c7">NEGEMMConvolutionLayer::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info(),</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</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>(),</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  biases != <span class="keyword">nullptr</span> ? biases->info() : <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  output->info(),</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7cb842ebfe255726066039853a4322f0">weights_info</a>,</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>,</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>,</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>));</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span> </div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_type();</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</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> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_layout();</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_width = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_kernels = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">DataLayoutDimension::BATCHES</a>);</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> </div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_width = <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#a8813441b655b97c00139c6a5a6390e97">dimension</a>(idx_width);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_height = <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#a8813441b655b97c00139c6a5a6390e97">dimension</a>(idx_height);</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>  _is_prepared = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7cb842ebfe255726066039853a4322f0">weights_info</a>.retain_internal_weights();</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  _original_weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</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="l00267"></a><span class="lineno"> 267</span>  _data_layout = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>;</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  _skip_im2col = (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a> && kernel_width == 1 && kernel_height == 1 && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride().first == 1 && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride().second == 1);</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="keyword">const</span> ITensor *gemm_input_to_use = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>;</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  ITensor *gemm_output_to_use = output;</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> </div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="comment">// Get convolved dimensions</span></div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_w = 0;</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_h = 0;</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  std::tie(conv_w, conv_h) = <a class="code" href="namespacearm__compute.xhtml#a138beaeb1260b90cb03bc3f761628724">scaled_dimensions</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(idx_width),</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->dimension(idx_height),</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  kernel_width,</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  kernel_height,</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</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">// Check if GEMM3D is supported</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>)</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  {</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  _skip_col2im = bool(validate_gemm3d(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info(), <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="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, conv_h, <span class="keyword">true</span>));</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="comment">// If not supported, we need to perform im2col and col2im (or reshape layer)</span></div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keywordflow">if</span>(!_skip_col2im)</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  {</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  _skip_im2col = <span class="keyword">false</span>;</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  }</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  }</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  {</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  _skip_col2im = <span class="keyword">false</span>;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</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">// Get parameters from conv_info</span></div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride_x = 0;</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride_y = 0;</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  std::tie(stride_x, stride_y) = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride();</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span> </div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> mat_weights_cols = <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#a8813441b655b97c00139c6a5a6390e97">dimension</a>(idx_kernels);</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span> </div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="comment">// _weights_reshaped will be auto configured in the kernel.</span></div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="comment">// Just append biases and do not transpose 1xW as it will be reshaped in NEGEMM</span></div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="keyword">const</span> ITensor *weights_to_use = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> </div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keywordflow">if</span>(_weights_manager && _weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">are_weights_managed</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>))</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  {</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  _reshape_weights_managed.<a class="code" href="classarm__compute_1_1weights__transformations_1_1_n_e_convolution_layer_reshape_weights_transform.xhtml#a2cf5a69621c3765993ba5752dfb8ea8c">configure</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="l00312"></a><span class="lineno"> 312</span>  weights_to_use = _weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a9b3e3aff065fafe490a5e190bf530ada">acquire</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &_reshape_weights_managed);</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  }</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  {</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  _reshape_weights.<a class="code" href="classarm__compute_1_1_n_e_convolution_layer_reshape_weights.xhtml#a61ca4e4a2e1326e12855748838eab503">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">nullptr</span>, &_weights_reshaped);</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  weights_to_use = &_weights_reshaped;</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  }</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span> </div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="comment">// Create tensor to store im2col reshaped inputs</span></div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="keywordflow">if</span>(!_skip_im2col)</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  {</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_im2col_output);</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span> </div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <span class="comment">// Configure</span></div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  _im2col_kernel.<a class="code" href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#a585edc13576fe5f51f7cc493751fef52">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &_im2col_output, Size2D(kernel_width, kernel_height), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <span class="keyword">false</span>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span> </div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="comment">// Update GEMM input</span></div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  gemm_input_to_use = &_im2col_output;</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  }</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span> </div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="comment">// Create temporary GEMM output tensor in case we cannot skip col2im</span></div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <span class="keywordflow">if</span>(!_skip_col2im)</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  {</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  TensorShape shape_gemm;</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span> </div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="comment">// Calculate GEMM output shape</span></div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  shape_gemm = _im2col_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  shape_gemm.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, mat_weights_cols);</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  shape_gemm.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(1, conv_w * conv_h);</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span> </div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="comment">// FIXME: input->clone() doesn't work with subtensors for grouped convolutions.</span></div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  TensorInfo info_gemm(shape_gemm, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>);</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  info_gemm.set_quantization_info(output->info()->quantization_info()).<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59cb3ec4cae7835d0a0283be56ef789">set_data_layout</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->info()->data_layout());</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  _gemm_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(info_gemm);</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_gemm_output);</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span> </div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="comment">// Update GEMM output</span></div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  gemm_output_to_use = &_gemm_output;</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  }</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span> </div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="comment">// Configure GEMM</span></div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="comment">// In case we need to skip col2im, GEMM3D (gemm_3d_depth != 0) must be called in order to avoid reshaping the output matrix</span></div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> gemm_3d_depth = _skip_col2im ? conv_h : 0;</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  configure_mm(gemm_input_to_use, weights_to_use, biases, gemm_output_to_use, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, gemm_3d_depth);</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span> </div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <span class="keywordflow">if</span>(!_skip_im2col)</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  {</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  _im2col_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  }</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span> </div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="keywordflow">if</span>(!_skip_col2im)</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  {</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <span class="keywordflow">if</span>(_data_layout == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  {</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="comment">// Configure col2im</span></div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  _col2im_kernel.<a class="code" href="classarm__compute_1_1_n_e_col2_im_kernel.xhtml#a5617685de2460c02aa189c0134880c9e">configure</a>(gemm_output_to_use, output, Size2D(conv_w, conv_h));</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  {</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="comment">// Configure reshape layer</span></div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  _reshape_layer.<a class="code" href="classarm__compute_1_1_n_e_reshape_layer.xhtml#a83a344e60eb7db895953a942abf16628">configure</a>(gemm_output_to_use, output);</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  }</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  }</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span> </div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <span class="keywordflow">if</span>(_is_quantized && !_skip_col2im)</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>  _tmp_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  }</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span> </div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="keywordflow">if</span>(!_skip_col2im || _is_quantized)</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  {</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  _gemm_output.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  }</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span> </div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <a class="code" href="_error_8h.xhtml#a0b0eb3235749a2909dc5a101afe59a1b">ARM_COMPUTE_ERROR_ON_MSG</a>((output->info()->dimension(idx_width) != conv_w) || (output->info()->dimension(idx_height) != conv_h),</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="stringliteral">"Output shape does not match the expected one"</span>);</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> }</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="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_1_tensor_allocator_xhtml_a3fc6adad84b23f10d54d5a7b6928f872"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">arm_compute::TensorAllocator::init</a></div><div class="ttdeci">void init(const TensorAllocator &allocator, const Coordinates &coords, TensorInfo &sub_info)</div><div class="ttdoc">Shares the same backing memory with another tensor allocator, while the tensor info might be differen...</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00108">TensorAllocator.cpp:108</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ad3fd4136244e42ad89b01c02b904336d"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">arm_compute::test::validation::dilation</a></div><div class="ttdeci">dilation</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="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_1_1test_1_1validation_xhtml_a7cb842ebfe255726066039853a4322f0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7cb842ebfe255726066039853a4322f0">arm_compute::test::validation::weights_info</a></div><div class="ttdeci">weights_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_batch_normalization_layer_8cpp_source.xhtml#l00196">BatchNormalizationLayer.cpp:196</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_1_tensor_info_xhtml_a8813441b655b97c00139c6a5a6390e97"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">arm_compute::TensorInfo::dimension</a></div><div class="ttdeci">size_t dimension(size_t index) const override</div><div class="ttdoc">Return the size of the requested dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00232">TensorInfo.h:232</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::DataLayoutDimension::HEIGHT</a></div><div class="ttdoc">height</div></div> |
| <div class="ttc" id="_error_8h_xhtml_a938dcd406ce611ef5345ad2531cdb948"><div class="ttname"><a href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_THROW_ON(status)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00455">Error.h:455</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer_xhtml_a3113fd3147c1bbc06b3f9890063c87c7"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml#a3113fd3147c1bbc06b3f9890063c87c7">arm_compute::NEGEMMConvolutionLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info=WeightsInfo(), const Size2D &dilation=Size2D(1U, 1U), const ActivationLayerInfo &act_info=ActivationLayerInfo(), unsigned int num_groups=1)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEGEMMConvolutionLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00390">NEGEMMConvolutionLayer.cpp:390</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_col2_im_kernel_xhtml_a5617685de2460c02aa189c0134880c9e"><div class="ttname"><a href="classarm__compute_1_1_n_e_col2_im_kernel.xhtml#a5617685de2460c02aa189c0134880c9e">arm_compute::NECol2ImKernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output, const Size2D &convolved_dims)</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_col2_im_kernel_8cpp_source.xhtml#l00107">NECol2ImKernel.cpp:107</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a138beaeb1260b90cb03bc3f761628724"><div class="ttname"><a href="namespacearm__compute.xhtml#a138beaeb1260b90cb03bc3f761628724">arm_compute::scaled_dimensions</a></div><div class="ttdeci">std::pair< unsigned int, unsigned int > scaled_dimensions(int width, int height, int kernel_width, int kernel_height, const PadStrideInfo &pad_stride_info, const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Returns expected width and height of output scaled tensor depending on dimensions rounding mode.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_utils_8cpp_source.xhtml#l00402">Utils.cpp:402</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_a47d74e4e51f9b1a636c4831bd747a97c"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">arm_compute::Tensor::info</a></div><div class="ttdeci">ITensorInfo * 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="runtime_2_tensor_8cpp_source.xhtml#l00033">Tensor.cpp:33</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="classarm__compute_1_1_memory_group_xhtml_a6fc0a49304c152c20a0f6df0634fb3cd"><div class="ttname"><a href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">arm_compute::MemoryGroup::manage</a></div><div class="ttdeci">void manage(IMemoryManageable *obj) override</div><div class="ttdoc">Sets a object to be managed by the given memory group.</div><div class="ttdef"><b>Definition:</b> <a href="_memory_group_8h_source.xhtml#l00079">MemoryGroup.h:79</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_weights_manager_xhtml_a386f2182d06bad88bffebd18ace91f46"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">arm_compute::IWeightsManager::are_weights_managed</a></div><div class="ttdeci">bool are_weights_managed(const ITensor *weights)</div><div class="ttdoc">Check if the weights are managed.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00112">IWeightsManager.cpp:112</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_1_i_tensor_info_xhtml_a7c66505457d00ece3aa4b34cab80757d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">arm_compute::ITensorInfo::tensor_shape</a></div><div class="ttdeci">virtual const TensorShape & tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor.</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_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">arm_compute::DataLayoutDimension::BATCHES</a></div><div class="ttdoc">batches</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a></div><div class="ttdoc">Num samples, channels, height, width.</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ae59cb3ec4cae7835d0a0283be56ef789"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59cb3ec4cae7835d0a0283be56ef789">arm_compute::test::validation::set_data_layout</a></div><div class="ttdeci">src_info set_data_layout(data_layout)</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="_validate_8h_xhtml_a921b705e9e3e0fe928928447869e62a5"><div class="ttname"><a href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00161">Validate.h:161</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">arm_compute::DataLayout::NHWC</a></div><div class="ttdoc">Num samples, height, width, channels.</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_1_n_e_reshape_layer_xhtml_a83a344e60eb7db895953a942abf16628"><div class="ttname"><a href="classarm__compute_1_1_n_e_reshape_layer.xhtml#a83a344e60eb7db895953a942abf16628">arm_compute::NEReshapeLayer::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output)</div><div class="ttdoc">Initialise the kernel's inputs and outputs.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_reshape_layer_8cpp_source.xhtml#l00034">NEReshapeLayer.cpp:34</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_im2_col_kernel_xhtml_a585edc13576fe5f51f7cc493751fef52"><div class="ttname"><a href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#a585edc13576fe5f51f7cc493751fef52">arm_compute::NEIm2ColKernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation=Size2D(1U, 1U), unsigned int num_groups=1)</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_im2_col_kernel_8cpp_source.xhtml#l00350">NEIm2ColKernel.cpp:350</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a9c54fb6cea3557692fe7c00c40bb40ad"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">arm_compute::TensorShape::set</a></div><div class="ttdeci">TensorShape & set(size_t dimension, size_t value, bool apply_dim_correction=true)</div><div class="ttdoc">Accessor to set the value of one of the dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00078">TensorShape.h:78</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::DataLayoutDimension::WIDTH</a></div><div class="ttdoc">width</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a5f5b6c4337eac9e2e0046ca2304d80dc"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">arm_compute::test::validation::data_type</a></div><div class="ttdeci">data_type</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_arithmetic_addition_8cpp_source.xhtml#l00138">ArithmeticAddition.cpp:138</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00327">Helpers.inl:327</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_convolution_layer_reshape_weights_xhtml_a61ca4e4a2e1326e12855748838eab503"><div class="ttname"><a href="classarm__compute_1_1_n_e_convolution_layer_reshape_weights.xhtml#a61ca4e4a2e1326e12855748838eab503">arm_compute::NEConvolutionLayerReshapeWeights::configure</a></div><div class="ttdeci">void configure(const ITensor *weights, const ITensor *biases, ITensor *output)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00045">NEGEMMConvolutionLayer.cpp:45</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdoc">Available data types.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00075">Types.h:75</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="classarm__compute_1_1_i_weights_manager_xhtml_a9b3e3aff065fafe490a5e190bf530ada"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a9b3e3aff065fafe490a5e190bf530ada">arm_compute::IWeightsManager::acquire</a></div><div class="ttdeci">ITensor * acquire(const ITensor *weights, ITransformWeights *weights_transform)</div><div class="ttdoc">Acquire the requested reshape tensor of the selected weights.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00117">IWeightsManager.cpp:117</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1weights__transformations_1_1_n_e_convolution_layer_reshape_weights_transform_xhtml_a2cf5a69621c3765993ba5752dfb8ea8c"><div class="ttname"><a href="classarm__compute_1_1weights__transformations_1_1_n_e_convolution_layer_reshape_weights_transform.xhtml#a2cf5a69621c3765993ba5752dfb8ea8c">arm_compute::weights_transformations::NEConvolutionLayerReshapeWeightsTransform::configure</a></div><div class="ttdeci">void configure(const ITensor *input, const ITensor *biases)</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_convolution_layer_8h_source.xhtml#l00096">NEGEMMConvolutionLayer.h:96</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_i_weights_manager_8cpp_source.xhtml#l00117">IWeightsManager::acquire()</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00183">arm_compute::test::validation::act_info</a>, <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator::allocate()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor::allocator()</a>, <a class="el" href="_i_weights_manager_8cpp_source.xhtml#l00112">IWeightsManager::are_weights_managed()</a>, <a class="el" href="_error_8h_source.xhtml#l00456">ARM_COMPUTE_ERROR_ON_MSG</a>, <a class="el" href="_validate_8h_source.xhtml#l00161">ARM_COMPUTE_ERROR_ON_NULLPTR</a>, <a class="el" href="_error_8h_source.xhtml#l00455">ARM_COMPUTE_ERROR_THROW_ON</a>, <a class="el" href="_error_8h_source.xhtml#l00152">ARM_COMPUTE_UNUSED</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">arm_compute::BATCHES</a>, <a class="el" href="_n_e_reshape_layer_8cpp_source.xhtml#l00034">NEReshapeLayer::configure()</a>, <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00045">NEConvolutionLayerReshapeWeights::configure()</a>, <a class="el" href="_n_e_col2_im_kernel_8cpp_source.xhtml#l00107">NECol2ImKernel::configure()</a>, <a class="el" href="_n_e_im2_col_kernel_8cpp_source.xhtml#l00350">NEIm2ColKernel::configure()</a>, <a class="el" href="_n_e_g_e_m_m_convolution_layer_8h_source.xhtml#l00096">NEConvolutionLayerReshapeWeightsTransform::configure()</a>, <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00597">arm_compute::test::validation::conv_info</a>, <a class="el" href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00146">arm_compute::test::validation::data_layout</a>, <a class="el" href="_c_l_2_arithmetic_addition_8cpp_source.xhtml#l00138">arm_compute::test::validation::data_type</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">arm_compute::test::validation::dilation</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="_tensor_info_8h_source.xhtml#l00232">TensorInfo::dimension()</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00327">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::HEIGHT</a>, <a class="el" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">ITensor::info()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00033">Tensor::info()</a>, <a class="el" href="_c_l_tensor_8cpp_source.xhtml#l00041">CLTensor::info()</a>, <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00108">TensorAllocator::init()</a>, <a class="el" href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">arm_compute::test::validation::input</a>, <a class="el" href="arm__compute_2core_2_utils_8h_source.xhtml#l01139">arm_compute::is_data_type_quantized_asymmetric()</a>, <a class="el" href="_memory_group_8h_source.xhtml#l00079">MemoryGroup::manage()</a>, <a class="el" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::NCHW</a>, <a class="el" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">arm_compute::NHWC</a>, <a class="el" href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00148">arm_compute::test::validation::num_groups</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">ITensorInfo::quantization_info()</a>, <a class="el" href="src_2core_2_utils_8cpp_source.xhtml#l00402">arm_compute::scaled_dimensions()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00078">TensorShape::set()</a>, <a class="el" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59cb3ec4cae7835d0a0283be56ef789">arm_compute::test::validation::set_data_layout()</a>, <a class="el" href="src_2core_2_tensor_info_8cpp_source.xhtml#l00372">TensorInfo::set_quantization_info()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00390">NEGEMMConvolutionLayer::validate()</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">arm_compute::test::validation::weights</a>, <a class="el" href="_c_l_2_batch_normalization_layer_8cpp_source.xhtml#l00196">arm_compute::test::validation::weights_info</a>, and <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::WIDTH</a>.</p> |
| |
| </div> |
| </div> |
| <a id="ac13c7e80f7e9e6f1e2912ef50678c5c9"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#ac13c7e80f7e9e6f1e2912ef50678c5c9">◆ </a></span>operator=() <span class="overload">[1/2]</span></h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a>& operator= </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a> & </td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">delete</span></span> </td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Prevent instances of this class from being copied (As this class contains pointers) </p> |
| |
| </div> |
| </div> |
| <a id="a0a7030af14bb803820435ca75009b0f9"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a0a7030af14bb803820435ca75009b0f9">◆ </a></span>operator=() <span class="overload">[2/2]</span></h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a>& operator= </td> |
| <td>(</td> |
| <td class="paramtype"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a> && </td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">default</span></span> </td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Default move assignment operator. </p> |
| |
| </div> |
| </div> |
| <a id="aa9b93ef660fc3c5b4b19d3fc7b891b77"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#aa9b93ef660fc3c5b4b19d3fc7b891b77">◆ </a></span>prepare()</h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname">void prepare </td> |
| <td>(</td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Prepare the function for executing. </p> |
| <p>Any one off pre-processing step required by the function is handled here</p> |
| <dl class="section note"><dt>Note</dt><dd>Prepare stage might not need all the function's buffers' backing memory to be available in order to execute </dd></dl> |
| |
| <p>Reimplemented from <a class="el" href="classarm__compute_1_1_i_function.xhtml#a820f7291c24155a2980512fae45aac26">IFunction</a>.</p> |
| |
| <p class="definition">Definition at line <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00563">563</a> of file <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml">NEGEMMConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00564"></a><span class="lineno"> 564</span> {</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  {</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <span class="keywordflow">if</span>(_weights_manager && _weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">are_weights_managed</a>(_original_weights))</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  {</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  _weights_manager-><a class="code" href="classarm__compute_1_1_i_weights_manager.xhtml#a73808ac61e51d72c7d6349d6d51dcf37">run</a>(_original_weights, &_reshape_weights_managed);</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  }</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  {</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <span class="comment">// Run weights reshaping and mark original weights tensor as unused</span></div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  _weights_reshaped.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  _reshape_weights.<a class="code" href="classarm__compute_1_1_n_e_convolution_layer_reshape_weights.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  _original_weights-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  }</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span> </div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  <span class="comment">// Prepare GEMM</span></div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  _is_quantized ? _mm_gemmlowp.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>() : _mm_gemm.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  <span class="keywordflow">if</span>(!_weights_reshaped.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">is_used</a>())</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>  _weights_reshaped.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a1468b0adb6ec3f9d38aa7d60b8a91974">free</a>();</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  }</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span> </div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  }</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span> }</div><div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NEGEMMLowpMatrixMultiplyCore::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00550">NEGEMMLowpMatrixMultiplyCore.cpp:550</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a209ea2ddfdfa80703799c92da8beb643"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">arm_compute::ITensor::is_used</a></div><div class="ttdeci">bool is_used() const</div><div class="ttdoc">Flags if the tensor is used or not.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00162">ITensor.cpp:162</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a9bc00234de9adf8c99a21eb1d7d494c2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">arm_compute::ITensor::mark_as_unused</a></div><div class="ttdeci">void mark_as_unused() const</div><div class="ttdoc">Marks a tensor as unused.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00167">ITensor.cpp:167</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_weights_manager_xhtml_a386f2182d06bad88bffebd18ace91f46"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a386f2182d06bad88bffebd18ace91f46">arm_compute::IWeightsManager::are_weights_managed</a></div><div class="ttdeci">bool are_weights_managed(const ITensor *weights)</div><div class="ttdoc">Check if the weights are managed.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00112">IWeightsManager.cpp:112</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a1468b0adb6ec3f9d38aa7d60b8a91974"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a1468b0adb6ec3f9d38aa7d60b8a91974">arm_compute::TensorAllocator::free</a></div><div class="ttdeci">void free() override</div><div class="ttdoc">Free allocated CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00148">TensorAllocator.cpp:148</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_convolution_layer_reshape_weights_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_convolution_layer_reshape_weights.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEConvolutionLayerReshapeWeights::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00087">NEGEMMConvolutionLayer.cpp:87</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NEGEMM::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_8cpp_source.xhtml#l00335">NEGEMM.cpp:335</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_weights_manager_xhtml_a73808ac61e51d72c7d6349d6d51dcf37"><div class="ttname"><a href="classarm__compute_1_1_i_weights_manager.xhtml#a73808ac61e51d72c7d6349d6d51dcf37">arm_compute::IWeightsManager::run</a></div><div class="ttdeci">ITensor * run(const ITensor *weights, ITransformWeights *weights_transform)</div><div class="ttdoc">Run the reshape function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_weights_manager_8cpp_source.xhtml#l00051">IWeightsManager.cpp:51</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator::allocate()</a>, <a class="el" href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor::allocator()</a>, <a class="el" href="_i_weights_manager_8cpp_source.xhtml#l00112">IWeightsManager::are_weights_managed()</a>, <a class="el" href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00148">TensorAllocator::free()</a>, <a class="el" href="_i_tensor_8cpp_source.xhtml#l00162">ITensor::is_used()</a>, <a class="el" href="_i_tensor_8cpp_source.xhtml#l00167">ITensor::mark_as_unused()</a>, <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml#l00335">NEGEMM::prepare()</a>, <a class="el" href="_n_e_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00550">NEGEMMLowpMatrixMultiplyCore::prepare()</a>, <a class="el" href="_i_weights_manager_8cpp_source.xhtml#l00051">IWeightsManager::run()</a>, and <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00087">NEConvolutionLayerReshapeWeights::run()</a>.</p> |
| |
| <p class="reference">Referenced by <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00524">NEGEMMConvolutionLayer::run()</a>.</p> |
| |
| </div> |
| </div> |
| <a id="ad1717410afd0be936c6213a63c8005fb"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#ad1717410afd0be936c6213a63c8005fb">◆ </a></span>run()</h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname">void run </td> |
| <td>(</td> |
| <td class="paramname"></td><td>)</td> |
| <td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Run the kernels contained in the function. </p> |
| <p>For NEON kernels:</p><ul> |
| <li>Multi-threading is used for the kernels which are parallelisable.</li> |
| <li>By default std::thread::hardware_concurrency() threads are used.</li> |
| </ul> |
| <dl class="section note"><dt>Note</dt><dd><a class="el" href="classarm__compute_1_1_c_p_p_scheduler.xhtml#ae64eebaa07f4d2da6cc2ba538c3cb095">CPPScheduler::set_num_threads()</a> can be used to manually set the number of threads</dd></dl> |
| <p>For OpenCL kernels:</p><ul> |
| <li>All the kernels are enqueued on the queue associated with <a class="el" href="classarm__compute_1_1_c_l_scheduler.xhtml" title="Provides global access to a CL context and command queue.">CLScheduler</a>.</li> |
| <li>The queue is then flushed.</li> |
| </ul> |
| <dl class="section note"><dt>Note</dt><dd>The function will not block until the kernels are executed. It is the user's responsibility to wait. </dd> |
| <dd> |
| Will call <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77" title="Prepare the function for executing.">prepare()</a> on first run if hasn't been done </dd></dl> |
| |
| <p>Implements <a class="el" href="classarm__compute_1_1_i_function.xhtml#a18954417d3124a8095783ea13dc6d00b">IFunction</a>.</p> |
| |
| <p class="definition">Definition at line <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00524">524</a> of file <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml">NEGEMMConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00525"></a><span class="lineno"> 525</span> {</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  <a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span> </div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  MemoryGroupResourceScope scope_mg(_memory_group);</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span> </div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <span class="keywordflow">if</span>(!_skip_im2col)</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  {</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <span class="comment">// Run input reshaping</span></div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> y_dim = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(_data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&_im2col_kernel, y_dim);</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  }</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span> </div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="comment">// Runs NEGEMM or NEGEMMLowpMatrixMultiplyCore functions</span></div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <span class="keywordflow">if</span>(_is_quantized)</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  {</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  <span class="comment">// Run gemmlowp</span></div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  _mm_gemmlowp.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  }</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  {</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <span class="comment">// Run gemm</span></div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  _mm_gemm.<a class="code" href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">run</a>();</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  }</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span> </div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <span class="comment">// Reshape output matrix</span></div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <span class="keywordflow">if</span>(!_skip_col2im)</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  {</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <span class="keywordflow">if</span>(_data_layout == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  {</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&_col2im_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  }</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  {</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  _reshape_layer.<a class="code" href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</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>  }</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span> }</div><div class="ttc" id="classarm__compute_1_1_i_n_e_simple_function_no_border_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_simple_function_no_border.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::INESimpleFunctionNoBorder::run</a></div><div class="ttdeci">void run() override final</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_n_e_simple_function_no_border_8cpp_source.xhtml#l00037">INESimpleFunctionNoBorder.cpp:37</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::DataLayoutDimension::HEIGHT</a></div><div class="ttdoc">height</div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEGEMM::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_8cpp_source.xhtml#l00285">NEGEMM.cpp:285</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_lowp_matrix_multiply_core.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NEGEMMLowpMatrixMultiplyCore::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00479">NEGEMMLowpMatrixMultiplyCore.cpp:479</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a></div><div class="ttdoc">Num samples, channels, height, width.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_ad2d402364fa822b0b7775081291eeca9"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">arm_compute::Window::DimY</a></div><div class="ttdeci">static constexpr size_t DimY</div><div class="ttdoc">Alias for dimension 1 also known as Y dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00045">Window.h:45</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_scheduler_xhtml_a4e58f95544bd5ac6559a421671bd9842"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">arm_compute::IScheduler::schedule</a></div><div class="ttdeci">virtual void schedule(ICPPKernel *kernel, const Hints &hints)=0</div><div class="ttdoc">Runs the kernel in the same thread as the caller synchronously.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::NEGEMMConvolutionLayer::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00563">NEGEMMConvolutionLayer.cpp:563</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00327">Helpers.inl:327</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_scheduler_xhtml_a0d63ca713bab377aabcfb63c192b8429"><div class="ttname"><a href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">arm_compute::Scheduler::get</a></div><div class="ttdeci">static IScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_scheduler_8cpp_source.xhtml#l00095">Scheduler.cpp:95</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_window_8h_source.xhtml#l00045">Window::DimY</a>, <a class="el" href="_scheduler_8cpp_source.xhtml#l00095">Scheduler::get()</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00327">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::HEIGHT</a>, <a class="el" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::NCHW</a>, <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00563">NEGEMMConvolutionLayer::prepare()</a>, <a class="el" href="_i_n_e_simple_function_no_border_8cpp_source.xhtml#l00037">INESimpleFunctionNoBorder::run()</a>, <a class="el" href="_n_e_g_e_m_m_8cpp_source.xhtml#l00285">NEGEMM::run()</a>, <a class="el" href="_n_e_g_e_m_m_lowp_matrix_multiply_core_8cpp_source.xhtml#l00479">NEGEMMLowpMatrixMultiplyCore::run()</a>, and <a class="el" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">IScheduler::schedule()</a>.</p> |
| |
| </div> |
| </div> |
| <a id="a3113fd3147c1bbc06b3f9890063c87c7"></a> |
| <h2 class="memtitle"><span class="permalink"><a href="#a3113fd3147c1bbc06b3f9890063c87c7">◆ </a></span>validate()</h2> |
| |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="mlabels"> |
| <tr> |
| <td class="mlabels-left"> |
| <table class="memname"> |
| <tr> |
| <td class="memname"><a class="el" href="classarm__compute_1_1_status.xhtml">Status</a> validate </td> |
| <td>(</td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> * </td> |
| <td class="paramname"><em>input</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> * </td> |
| <td class="paramname"><em>weights</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> * </td> |
| <td class="paramname"><em>biases</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> * </td> |
| <td class="paramname"><em>output</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> & </td> |
| <td class="paramname"><em>conv_info</em>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a> & </td> |
| <td class="paramname"><em>weights_info</em> = <code><a class="el" href="classarm__compute_1_1_weights_info.xhtml">WeightsInfo</a>()</code>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> & </td> |
| <td class="paramname"><em>dilation</em> = <code><a class="el" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1U, 1U)</code>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">const <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> & </td> |
| <td class="paramname"><em>act_info</em> = <code><a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>()</code>, </td> |
| </tr> |
| <tr> |
| <td class="paramkey"></td> |
| <td></td> |
| <td class="paramtype">unsigned int </td> |
| <td class="paramname"><em>num_groups</em> = <code>1</code> </td> |
| </tr> |
| <tr> |
| <td></td> |
| <td>)</td> |
| <td></td><td></td> |
| </tr> |
| </table> |
| </td> |
| <td class="mlabels-right"> |
| <span class="mlabels"><span class="mlabel">static</span></span> </td> |
| </tr> |
| </table> |
| </div><div class="memdoc"> |
| |
| <p>Static function to check if given info will lead to a valid configuration of <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a>. </p> |
| <dl class="params"><dt>Parameters</dt><dd> |
| <table class="params"> |
| <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Source tensor info. 3 lower dimensions represent a single input [width, height, IFM], while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">weights</td><td>Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/F16/F32. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">biases</td><td>Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Should match <code>input</code> data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">output</td><td>Destination tensor info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. Data types supported: Same as <code>input</code>. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">conv_info</td><td>Contains padding and stride information described in <a class="el" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">weights_info</td><td>Specifies if the weights tensor has been reshaped with <a class="el" href="classarm__compute_1_1_n_e_weights_reshape_kernel.xhtml" title="NEON kernel to perform reshaping on the weights used by convolution and locally connected layer.">NEWeightsReshapeKernel</a>. If this is not part of the fully connected layer the weights tensor has also been transposed with <a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_transpose1x_w_kernel.xhtml" title="NEON kernel which transposes the elements of a matrix in chunks of 1xW, where W is equal to (16 / ele...">NEGEMMTranspose1xWKernel</a>. Data type supported: Same as <code>input</code>. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">dilation</td><td>(Optional) Dilation, in elements, across x and y. Defaults to (1, 1). </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">act_info</td><td>(Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. </td></tr> |
| <tr><td class="paramdir">[in]</td><td class="paramname">num_groups</td><td>(Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported</td></tr> |
| </table> |
| </dd> |
| </dl> |
| <dl class="section return"><dt>Returns</dt><dd>a status </dd></dl> |
| |
| <p class="definition">Definition at line <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00390">390</a> of file <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml">NEGEMMConvolutionLayer.cpp</a>.</p> |
| <div class="fragment"><div class="line"><a name="l00392"></a><span class="lineno"> 392</span> {</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(<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>, output);</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7cb842ebfe255726066039853a4322f0">weights_info</a>.are_reshaped(), <span class="stringliteral">"Weights already reshaped are not supported!"</span>);</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a329f5d0c4b0c80e3474951d2c4435dd9">DataType::QASYMM8_SIGNED</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, 1, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a329f5d0c4b0c80e3474951d2c4435dd9">DataType::QASYMM8_SIGNED</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a34f500e941c4df30b870126ec868ebd5">DataType::QSYMM8_PER_CHANNEL</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <a class="code" href="_validate_8h.xhtml#abdb9168800c70e5e2c4c020a3b905738">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT</a>(<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>);</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> > 1, <span class="stringliteral">"Grouping (num_groups != 1) is not supported on NEON"</span>);</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span> </div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="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> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_layout();</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_type();</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_width = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_channel = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_kernels = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">DataLayoutDimension::BATCHES</a>);</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span> </div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_width = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_width);</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_height = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_height);</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>  TensorInfo im2col_reshaped_info{};</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  TensorInfo info_gemm{};</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  TensorInfo tmp_info{};</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  TensorInfo weights_reshaped_info{};</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="keyword">const</span> ITensorInfo *gemm_input_to_use = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>;</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <span class="keyword">const</span> ITensorInfo *gemm_output_to_use = output;</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <span class="keyword">const</span> ITensorInfo *weights_to_use = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span> </div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> append_bias = <span class="keyword">false</span>;</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</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#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>);</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keywordtype">bool</span> skip_im2col = (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a> && kernel_width == 1 && kernel_height == 1 && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride().first == 1 && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.stride().second == 1);</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span> </div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="comment">// Get convolved dimensions</span></div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_w = 0;</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_h = 0;</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>  std::tie(conv_w, conv_h) = <a class="code" href="namespacearm__compute.xhtml#a138beaeb1260b90cb03bc3f761628724">scaled_dimensions</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->dimension(idx_width),</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->dimension(idx_height),</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  kernel_width,</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  kernel_height,</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span> </div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="comment">// Check if GEMM3D is supported</span></div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <span class="keywordtype">bool</span> skip_col2im = <span class="keyword">false</span>;</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>)</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  {</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  skip_col2im = bool(validate_gemm3d(<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>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, conv_h, <span class="keyword">true</span>));</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  <span class="comment">// If not supported, we need to perform im2col and col2im (or reshape layer)</span></div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <span class="keywordflow">if</span>(!skip_col2im)</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>  skip_im2col = <span class="keyword">false</span>;</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  }</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  }</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span> </div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <span class="keywordflow">if</span>(skip_col2im)</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">// If not supported, we need to perform im2col and col2im (or reshape layer)</span></div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <span class="keywordflow">if</span>(!<span class="keywordtype">bool</span>(validate_gemm3d(<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>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, conv_h, skip_im2col)))</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  {</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  skip_im2col = <span class="keyword">false</span>;</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  skip_col2im = <span class="keyword">false</span>;</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  }</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  }</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span> </div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_channel) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->dimension(idx_channel));</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->num_dimensions() > 4);</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> </div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <span class="comment">// Validate biases</span></div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <span class="keywordflow">if</span>(biases != <span class="keyword">nullptr</span>)</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>  <span class="keywordflow">if</span>(is_quantized)</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  {</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(biases, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  }</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  {</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, biases);</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  }</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(biases->dimension(0) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_kernels));</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(biases->num_dimensions() > 1);</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  }</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> mat_weights_cols = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_kernels);</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> mat_weights_rows = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_width) * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_height) * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->dimension(idx_channel);</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span> </div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <span class="comment">// Output tensor auto inizialization if not yet initialized</span></div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_convolution_layer_reshape_weights.xhtml#a224bc0dd323828beab8c7edeeb539cb3">NEConvolutionLayerReshapeWeights::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">nullptr</span>, <span class="keyword">nullptr</span>));</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  weights_reshaped_info = TensorInfo(<a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6365b505b5c1b98916425bc692b6ea49">compute_weights_reshaped_shape</a>(*<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, append_bias), 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>);</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  weights_reshaped_info.set_quantization_info(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>->quantization_info());</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  weights_to_use = &weights_reshaped_info;</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span> </div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <span class="keywordflow">if</span>(!skip_im2col)</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  {</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="comment">// Create tensor info for im2col reshaped inputs</span></div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <span class="comment">// For NEON the batch size is on the fourth dimension</span></div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <span class="comment">// TODO (giaiod01): Auto-initialize the output shape of im2col COMPMID-1482</span></div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  TensorShape shape_im2col = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->tensor_shape();</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  shape_im2col.set(0, mat_weights_rows);</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  shape_im2col.set(1, conv_w * conv_h);</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  shape_im2col.set(2, 1);</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span> </div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  im2col_reshaped_info = TensorInfo(shape_im2col, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>);</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  im2col_reshaped_info.set_quantization_info(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->quantization_info());</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span> </div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#a4e256965ba7798ffe1358469be661e5a">NEIm2ColKernel::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, append_bias, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>));</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  gemm_input_to_use = &im2col_reshaped_info;</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  }</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span> </div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  <span class="comment">// Create temporary GEMM output tensor in case we cannot skip col2im</span></div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <span class="keywordflow">if</span>(!skip_col2im)</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  {</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  TensorShape shape_gemm = gemm_input_to_use->tensor_shape();</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  shape_gemm.set(0, mat_weights_cols);</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  shape_gemm.set(1, conv_w * conv_h);</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  info_gemm = TensorInfo(shape_gemm, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>);</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  }</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  {</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  info_gemm = TensorInfo(output->tensor_shape(), 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">data_type</a>);</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  }</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  info_gemm.set_quantization_info(output->quantization_info()).<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59cb3ec4cae7835d0a0283be56ef789">set_data_layout</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>->data_layout());</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  gemm_output_to_use = &info_gemm;</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(validate_mm(gemm_input_to_use, weights_to_use, biases, gemm_output_to_use, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>, skip_col2im ? conv_h : 0, skip_im2col));</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span> </div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <span class="comment">// Validate Col2Im/ReshapeLayer</span></div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <span class="keywordflow">if</span>(!skip_col2im && (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>))</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  {</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_col2_im_kernel.xhtml#a35842b155e2204bb6831588a0ffcc6d3">NECol2ImKernel::validate</a>(gemm_output_to_use, output, Size2D(conv_w, conv_h)));</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  }</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span> </div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <span class="keywordflow">return</span> Status{};</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span> }</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_ad3fd4136244e42ad89b01c02b904336d"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">arm_compute::test::validation::dilation</a></div><div class="ttdeci">dilation</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="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_1_1test_1_1validation_xhtml_a7cb842ebfe255726066039853a4322f0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7cb842ebfe255726066039853a4322f0">arm_compute::test::validation::weights_info</a></div><div class="ttdeci">weights_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_batch_normalization_layer_8cpp_source.xhtml#l00196">BatchNormalizationLayer.cpp:196</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_abdb9168800c70e5e2c4c020a3b905738"><div class="ttname"><a href="_validate_8h.xhtml#abdb9168800c70e5e2c4c020a3b905738">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00494">Validate.h:494</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a8f3ff7da485ff7e75dab07baadf5b4bd"><div class="ttname"><a href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00545">Validate.h:545</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="_error_8h_xhtml_a8a1e1c105f0bdaf37db408c7cfcb77a4"><div class="ttname"><a href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ON_ERROR(status)</div><div class="ttdoc">Checks if a status contains an error and returns it.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00204">Error.h:204</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_ae7eed178dac535c6e727061b1f5bc6eb"><div class="ttname"><a href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00792">Validate.h:792</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::DataLayoutDimension::HEIGHT</a></div><div class="ttdoc">height</div></div> |
| <div class="ttc" id="_error_8h_xhtml_a206d6e247e0957ac3dee45d27756fc25"><div class="ttname"><a href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true, an error is returned.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00296">Error.h:296</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::Format::F16</a></div><div class="ttdoc">1 channel, 1 F16 per channel</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a138beaeb1260b90cb03bc3f761628724"><div class="ttname"><a href="namespacearm__compute.xhtml#a138beaeb1260b90cb03bc3f761628724">arm_compute::scaled_dimensions</a></div><div class="ttdeci">std::pair< unsigned int, unsigned int > scaled_dimensions(int width, int height, int kernel_width, int kernel_height, const PadStrideInfo &pad_stride_info, const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Returns expected width and height of output scaled tensor depending on dimensions rounding mode.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_utils_8cpp_source.xhtml#l00402">Utils.cpp:402</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="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_1_n_e_convolution_layer_reshape_weights_xhtml_a224bc0dd323828beab8c7edeeb539cb3"><div class="ttname"><a href="classarm__compute_1_1_n_e_convolution_layer_reshape_weights.xhtml#a224bc0dd323828beab8c7edeeb539cb3">arm_compute::NEConvolutionLayerReshapeWeights::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEConvolutionLayerReshap...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00060">NEGEMMConvolutionLayer.cpp:60</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::DataType::QASYMM8</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number unsigned</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="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::DataLayoutDimension::CHANNEL</a></div><div class="ttdoc">channel</div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_col2_im_kernel_xhtml_a35842b155e2204bb6831588a0ffcc6d3"><div class="ttname"><a href="classarm__compute_1_1_n_e_col2_im_kernel.xhtml#a35842b155e2204bb6831588a0ffcc6d3">arm_compute::NECol2ImKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &convolved_dims)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NECol2ImKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_col2_im_kernel_8cpp_source.xhtml#l00138">NECol2ImKernel.cpp:138</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">arm_compute::DataLayoutDimension::BATCHES</a></div><div class="ttdoc">batches</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a></div><div class="ttdoc">Num samples, channels, height, width.</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ae59cb3ec4cae7835d0a0283be56ef789"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59cb3ec4cae7835d0a0283be56ef789">arm_compute::test::validation::set_data_layout</a></div><div class="ttdeci">src_info set_data_layout(data_layout)</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="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a34f500e941c4df30b870126ec868ebd5"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a34f500e941c4df30b870126ec868ebd5">arm_compute::DataType::QSYMM8_PER_CHANNEL</a></div><div class="ttdoc">quantized, symmetric per channel fixed-point 8-bit number</div></div> |
| <div class="ttc" id="_validate_8h_xhtml_aff911654521523937ff24372a870b89f"><div class="ttname"><a href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00163">Validate.h:163</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a6365b505b5c1b98916425bc692b6ea49"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6365b505b5c1b98916425bc692b6ea49">arm_compute::misc::shape_calculator::compute_weights_reshaped_shape</a></div><div class="ttdeci">TensorShape compute_weights_reshaped_shape(const ITensorInfo &weights, bool has_bias=false, unsigned int num_groups=1)</div><div class="ttdoc">Calculate the reshaped shape of the weights.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00149">ShapeCalculator.h:149</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">arm_compute::DataLayout::NHWC</a></div><div class="ttdoc">Num samples, height, width, channels.</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="_error_8h_xhtml_a1c69762a42ab8add645d0a949b6f4b1f"><div class="ttname"><a href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond, msg)</div><div class="ttdoc">If the condition is true, an error is returned.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00244">Error.h:244</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::DataLayoutDimension::WIDTH</a></div><div class="ttdoc">width</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a329f5d0c4b0c80e3474951d2c4435dd9"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a329f5d0c4b0c80e3474951d2c4435dd9">arm_compute::DataType::QASYMM8_SIGNED</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number signed</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a5f5b6c4337eac9e2e0046ca2304d80dc"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a5f5b6c4337eac9e2e0046ca2304d80dc">arm_compute::test::validation::data_type</a></div><div class="ttdeci">data_type</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_arithmetic_addition_8cpp_source.xhtml#l00138">ArithmeticAddition.cpp:138</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00327">Helpers.inl:327</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdoc">Available data types.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00075">Types.h:75</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="classarm__compute_1_1_n_e_im2_col_kernel_xhtml_a4e256965ba7798ffe1358469be661e5a"><div class="ttname"><a href="classarm__compute_1_1_n_e_im2_col_kernel.xhtml#a4e256965ba7798ffe1358469be661e5a">arm_compute::NEIm2ColKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation=Size2D(1U, 1U), unsigned int num_groups=1)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEIm2ColKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_im2_col_kernel_8cpp_source.xhtml#l00423">NEIm2ColKernel.cpp:423</a></div></div> |
| </div><!-- fragment --> |
| <p class="reference">References <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00183">arm_compute::test::validation::act_info</a>, <a class="el" href="_error_8h_source.xhtml#l00296">ARM_COMPUTE_RETURN_ERROR_ON</a>, <a class="el" href="_validate_8h_source.xhtml#l00792">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>, <a class="el" href="_validate_8h_source.xhtml#l00494">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT</a>, <a class="el" href="_validate_8h_source.xhtml#l00545">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>, <a class="el" href="_error_8h_source.xhtml#l00244">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>, <a class="el" href="_validate_8h_source.xhtml#l00163">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>, <a class="el" href="_error_8h_source.xhtml#l00204">ARM_COMPUTE_RETURN_ON_ERROR</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">arm_compute::BATCHES</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::CHANNEL</a>, <a class="el" href="_shape_calculator_8h_source.xhtml#l00149">arm_compute::misc::shape_calculator::compute_weights_reshaped_shape()</a>, <a class="el" href="_c_l_2_winograd_8cpp_source.xhtml#l00597">arm_compute::test::validation::conv_info</a>, <a class="el" href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00146">arm_compute::test::validation::data_layout</a>, <a class="el" href="_c_l_2_arithmetic_addition_8cpp_source.xhtml#l00138">arm_compute::test::validation::data_type</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">arm_compute::test::validation::dilation</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">ITensorInfo::dimension()</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::F16</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00327">arm_compute::get_data_layout_dimension_index()</a>, <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::HEIGHT</a>, <a class="el" href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">arm_compute::test::validation::input</a>, <a class="el" href="arm__compute_2core_2_utils_8h_source.xhtml#l01139">arm_compute::is_data_type_quantized_asymmetric()</a>, <a class="el" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::NCHW</a>, <a class="el" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">arm_compute::NHWC</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">ITensorInfo::num_dimensions()</a>, <a class="el" href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00148">arm_compute::test::validation::num_groups</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::QASYMM8</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a329f5d0c4b0c80e3474951d2c4435dd9">arm_compute::QASYMM8_SIGNED</a>, <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a34f500e941c4df30b870126ec868ebd5">arm_compute::QSYMM8_PER_CHANNEL</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">ITensorInfo::quantization_info()</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::S32</a>, <a class="el" href="src_2core_2_utils_8cpp_source.xhtml#l00402">arm_compute::scaled_dimensions()</a>, <a class="el" href="_tensor_shape_8h_source.xhtml#l00078">TensorShape::set()</a>, <a class="el" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae59cb3ec4cae7835d0a0283be56ef789">arm_compute::test::validation::set_data_layout()</a>, <a class="el" href="src_2core_2_tensor_info_8cpp_source.xhtml#l00372">TensorInfo::set_quantization_info()</a>, <a class="el" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">ITensorInfo::tensor_shape()</a>, <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00060">NEConvolutionLayerReshapeWeights::validate()</a>, <a class="el" href="_n_e_col2_im_kernel_8cpp_source.xhtml#l00138">NECol2ImKernel::validate()</a>, <a class="el" href="_n_e_im2_col_kernel_8cpp_source.xhtml#l00423">NEIm2ColKernel::validate()</a>, <a class="el" href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00188">arm_compute::test::validation::weights</a>, <a class="el" href="_c_l_2_batch_normalization_layer_8cpp_source.xhtml#l00196">arm_compute::test::validation::weights_info</a>, and <a class="el" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::WIDTH</a>.</p> |
| |
| <p class="reference">Referenced by <a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml#l00240">NEGEMMConvolutionLayer::configure()</a>, and <a class="el" href="_n_e_convolution_layer_8cpp_source.xhtml#l00089">NEConvolutionLayer::validate()</a>.</p> |
| |
| </div> |
| </div> |
| <hr/>The documentation for this class was generated from the following files:<ul> |
| <li>arm_compute/runtime/NEON/functions/<a class="el" href="_n_e_g_e_m_m_convolution_layer_8h_source.xhtml">NEGEMMConvolutionLayer.h</a></li> |
| <li>src/runtime/NEON/functions/<a class="el" href="_n_e_g_e_m_m_convolution_layer_8cpp_source.xhtml">NEGEMMConvolutionLayer.cpp</a></li> |
| </ul> |
| </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="namespacearm__compute.xhtml">arm_compute</a></li><li class="navelem"><a class="el" href="classarm__compute_1_1_n_e_g_e_m_m_convolution_layer.xhtml">NEGEMMConvolutionLayer</a></li> |
| <li class="footer">Generated on Thu Mar 5 2020 16:07:15 for Compute Library by |
| <a href="http://www.doxygen.org/index.html"> |
| <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.15 </li> |
| </ul> |
| </div> |
| </body> |
| </html> |
