Google Git
Sign in
android / platform / external / ComputeLibrary / 975dfe175 / . / documentation / _c_l_locally_connected_layer_8cpp_source.xhtml
blob: 599bf23251bf18ae3c128a797bab4902df31108f [file] [log] [blame]
<!-- 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: src/runtime/CL/functions/CLLocallyConnectedLayer.cpp Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="navtree.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="resize.js"></script>
<script type="text/javascript" src="navtreedata.js"></script>
<script type="text/javascript" src="navtree.js"></script>
<script type="text/javascript">
/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&amp;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">
&#160;<span id="projectnumber">19.08</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&amp;dn=gpl-2.0.txt GPL-v2 */
var searchBox = new SearchBox("searchBox", "search",false,'Search');
/* @license-end */
</script>
<script type="text/javascript" src="menudata.js"></script>
<script type="text/javascript" src="menu.js"></script>
<script type="text/javascript">
/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&amp;dn=gpl-2.0.txt GPL-v2 */
$(function() {
initMenu('',true,false,'search.php','Search');
$(document).ready(function() { init_search(); });
});
/* @license-end */</script>
<div id="main-nav"></div>
</div><!-- top -->
<div id="side-nav" class="ui-resizable side-nav-resizable">
<div id="nav-tree">
<div id="nav-tree-contents">
<div id="nav-sync" class="sync"></div>
</div>
</div>
<div id="splitbar" style="-moz-user-select:none;"
class="ui-resizable-handle">
</div>
</div>
<script type="text/javascript">
/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&amp;dn=gpl-2.0.txt GPL-v2 */
$(document).ready(function(){initNavTree('_c_l_locally_connected_layer_8cpp_source.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="headertitle">
<div class="title">CLLocallyConnectedLayer.cpp</div> </div>
</div><!--header-->
<div class="contents">
<a href="_c_l_locally_connected_layer_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment"> * Copyright (c) 2017-2019 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;<span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;<span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;<span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;<span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;<span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;<span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160;<span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_c_l_locally_connected_layer_8h.xhtml">arm_compute/runtime/CL/functions/CLLocallyConnectedLayer.h</a>&quot;</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_pixel_value_8h.xhtml">arm_compute/core/PixelValue.h</a>&quot;</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="arm__compute_2core_2_utils_8h.xhtml">arm_compute/core/Utils.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_validate_8h.xhtml">arm_compute/core/Validate.h</a>&quot;</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_c_l_scheduler_8h.xhtml">arm_compute/runtime/CL/CLScheduler.h</a>&quot;</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;<span class="preprocessor">#include &lt;cmath&gt;</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160;<span class="preprocessor">#include &lt;tuple&gt;</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a>;</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;<span class="keyword">namespace</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160;{</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160;<span class="keywordtype">void</span> calculate_shapes(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *biases, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>,</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;shape_wr, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;shape_im2col, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;shape_gemm)</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160;{</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(output);</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernel_width = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;dimension(0);</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; <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>-&gt;dimension(1);</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160;</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; <span class="keywordtype">bool</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a> = (biases != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160;</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <span class="comment">// Get convolved dimensions</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_w = 0;</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_h = 0;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; std::tie(conv_w, conv_h) = <a class="code" href="namespacearm__compute.xhtml#abbe3399a66c35c3c353030cd0a84c936">scaled_dimensions</a>(input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0),</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1),</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; kernel_width,</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; kernel_height,</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; <span class="keyword">const</span> <span class="keywordtype">size_t</span> mat_weights_cols = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;dimension(3);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; <span class="keyword">const</span> <span class="keywordtype">size_t</span> mat_weights_rows = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;dimension(0) * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;dimension(1) * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;dimension(2) + ((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>) ? 1 : 0);</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; <span class="keyword">const</span> <span class="keywordtype">size_t</span> mat_weights_num = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;dimension(4);</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; shape_wr = <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(mat_weights_cols, mat_weights_rows, mat_weights_num);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; <span class="keyword">const</span> <span class="keywordtype">size_t</span> mat_input_cols = mat_weights_rows;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; <span class="keyword">const</span> <span class="keywordtype">size_t</span> mat_input_rows = conv_w * conv_h;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; shape_im2col = input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; <span class="keywordflow">if</span>(shape_im2col.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a80a5f2d6e3a697c9aad893a3b4242615">num_dimensions</a>() &gt;= 3)</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; {</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#acb74edf42335de0dca0da5158b704c4b">remove_dimension</a>(2);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160; }</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, mat_input_cols);</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(1, mat_input_rows);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; shape_gemm = shape_im2col;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; 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="l00076"></a><span class="lineno"> 76</span>&#160; shape_gemm.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(1, mat_input_rows);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160;}</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160;} <span class="comment">// namespace</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160;</div><div class="line"><a name="l00080"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml#a69b25e30cdc636553317bc9cf6f8315b"> 80</a></span>&#160;<a class="code" href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml#a69b25e30cdc636553317bc9cf6f8315b">CLLocallyConnectedLayer::CLLocallyConnectedLayer</a>(std::shared_ptr&lt;IMemoryManager&gt; memory_manager)</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; : _memory_group(std::move(memory_manager)), _input_im2col_kernel(), _weights_reshape_kernel(), _mm_kernel(), _output_col2im_kernel(), _input_im2col_reshaped(), _weights_reshaped(), _gemm_output(),</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; _is_prepared(false), _original_weights(nullptr)</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160;{</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160;}</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160;</div><div class="line"><a name="l00086"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml#a126f91e344585b85ae09a7b76e68c520"> 86</a></span>&#160;<a class="code" href="classarm__compute_1_1_status.xhtml">Status</a> <a class="code" href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml#a126f91e344585b85ae09a7b76e68c520">CLLocallyConnectedLayer::validate</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *biases, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>)</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160;{</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(input, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; <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>-&gt;dimension(2) != input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(2));</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; <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#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>.padding_is_symmetric());</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160;</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; <span class="keywordtype">bool</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a> = (biases != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160;</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>)</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; {</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(biases-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;dimension(3));</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(biases-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() &gt; 2);</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; }</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160;</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; <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>-&gt;dimension(0);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; <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>-&gt;dimension(1);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; <span class="comment">// Get convolved dimensions</span></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_w = 0;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_h = 0;</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; std::tie(conv_w, conv_h) = <a class="code" href="namespacearm__compute.xhtml#abbe3399a66c35c3c353030cd0a84c936">scaled_dimensions</a>(input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0), input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1), kernel_width, kernel_height,</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; <a class="code" href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>((output-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) != conv_w) || (output-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) != conv_h), <span class="stringliteral">&quot;Output shape does not match the expected one&quot;</span>);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; <a class="code" href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;dimension(4) != (conv_w * conv_h), <span class="stringliteral">&quot;Weights shape does not match the expected one&quot;</span>);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160;</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; <span class="comment">// Calculate intermediate buffer shapes</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_wr;</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_im2col;</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_gemm;</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; calculate_shapes(input, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, shape_wr, shape_im2col, shape_gemm);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160;</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> weights_reshaped_info(shape_wr, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;data_type());</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> input_im2col_reshaped_info(shape_im2col, 1, input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> gemm_output_info(shape_gemm, 1, input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_im2_col_kernel.xhtml#a4e256965ba7798ffe1358469be661e5a">CLIm2ColKernel::validate</a>(input, &amp;input_im2col_reshaped_info, <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(kernel_width, kernel_height), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>));</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_weights_reshape_kernel.xhtml#a90066609e6f00aad9d9a2a8951aef7aa">CLWeightsReshapeKernel::validate</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, &amp;weights_reshaped_info));</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_locally_connected_matrix_multiply_kernel.xhtml#a0d647d83c8512fa95fa9adb8fb3e0cab">CLLocallyConnectedMatrixMultiplyKernel::validate</a>(&amp;input_im2col_reshaped_info, &amp;weights_reshaped_info, &amp;gemm_output_info));</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_l_col2_im_kernel.xhtml#a1877ad9505323d75f1a301f30a528ab4">CLCol2ImKernel::validate</a>(&amp;gemm_output_info, output, <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(conv_w, conv_h)));</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160;</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160;}</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160;</div><div class="line"><a name="l00130"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml#a0e2cfb6fb263bd6f761756c816574345"> 130</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml#a0e2cfb6fb263bd6f761756c816574345">CLLocallyConnectedLayer::configure</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *biases, <a class="code" href="classarm__compute_1_1_i_c_l_tensor.xhtml">ICLTensor</a> *output, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>)</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160;{</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; <a class="code" href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a>(input, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(<a class="code" href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml#a126f91e344585b85ae09a7b76e68c520">CLLocallyConnectedLayer::validate</a>(input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>(), biases == <span class="keyword">nullptr</span> ? nullptr : biases-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), output-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>));</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; <span class="keywordtype">bool</span> _has_bias = (biases != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; _original_weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; _is_prepared = <span class="keyword">false</span>;</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160;</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; <span class="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>-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(0);</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; <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>-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(1);</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160;</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; <span class="comment">// Get convolved dimensions</span></div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_w = 0;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_h = 0;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; std::tie(conv_w, conv_h) = <a class="code" href="namespacearm__compute.xhtml#abbe3399a66c35c3c353030cd0a84c936">scaled_dimensions</a>(input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0), input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1), kernel_width, kernel_height,</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>);</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160;</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; <span class="comment">// Calculate intermediate buffer shapes</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_wr;</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_im2col;</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_gemm;</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; calculate_shapes(input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>(), biases == <span class="keyword">nullptr</span> ? nullptr : biases-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), output-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, shape_wr, shape_im2col, shape_gemm);</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; _weights_reshaped.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(shape_wr, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>()));</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; _input_im2col_reshaped.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(shape_im2col, 1, input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; _gemm_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(shape_gemm, 1, input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()));</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160;</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; <span class="comment">// Manage intermediate buffers</span></div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_input_im2col_reshaped);</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160; _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_gemm_output);</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160;</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; <span class="comment">// Configure kernels</span></div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; _input_im2col_kernel.<a class="code" href="classarm__compute_1_1_c_l_im2_col_kernel.xhtml#a6bc1be5100f77f4a136d8935b06d5ac6">configure</a>(input, &amp;_input_im2col_reshaped, <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(kernel_width, kernel_height), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, _has_bias);</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; _weights_reshape_kernel.<a class="code" href="classarm__compute_1_1_c_l_weights_reshape_kernel.xhtml#a32630202628891fb30458ef84581ad2a">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, biases, &amp;_weights_reshaped);</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; _mm_kernel.<a class="code" href="classarm__compute_1_1_c_l_locally_connected_matrix_multiply_kernel.xhtml#a083cbb32c94ef493098ee169dc36c486">configure</a>(&amp;_input_im2col_reshaped, &amp;_weights_reshaped, &amp;_gemm_output);</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; _output_col2im_kernel.<a class="code" href="classarm__compute_1_1_c_l_col2_im_kernel.xhtml#a109e7bd51a51a6aecaa2402a06deb3a7">configure</a>(&amp;_gemm_output, output, <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(conv_w, conv_h));</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160;</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; <span class="comment">// Allocate intermediate tensors</span></div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160; _input_im2col_reshaped.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160; _gemm_output.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160;</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a6c2059df991a75abef4eb643510c9544">tune_kernel_static</a>(_input_im2col_kernel);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160;}</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160;</div><div class="line"><a name="l00175"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb"> 175</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">CLLocallyConnectedLayer::run</a>()</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160;{</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; <a class="code" href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160;</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; <a class="code" href="classarm__compute_1_1_memory_group_resource_scope.xhtml">MemoryGroupResourceScope</a> scope_mg(_memory_group);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160;</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; <span class="comment">// Run input reshaping</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_input_im2col_kernel);</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160;</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; <span class="comment">// Runs vector matrix multiply on reshaped matrices</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_mm_kernel);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160;</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; <span class="comment">// Reshape output matrix</span></div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_output_col2im_kernel, <span class="keyword">false</span>);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160;}</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160;</div><div class="line"><a name="l00191"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77"> 191</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">CLLocallyConnectedLayer::prepare</a>()</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160;{</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160; <span class="keywordflow">if</span>(!_is_prepared)</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; {</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!_original_weights-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">is_used</a>());</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160;</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; <span class="comment">// Run weights reshaping and mark original weights tensor as unused</span></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; _weights_reshaped.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">enqueue</a>(_weights_reshape_kernel);</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; _original_weights-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160;</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#ad381d1aed28b4b1e1f5a710633934580">queue</a>().finish();</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160; _is_prepared = <span class="keyword">true</span>;</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160; }</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160;}</div><div class="ttc" id="_pixel_value_8h_xhtml"><div class="ttname"><a href="_pixel_value_8h.xhtml">PixelValue.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a1f4e725b8e1ea36b30e09dc08ae6961d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">arm_compute::ITensorInfo::num_dimensions</a></div><div class="ttdeci">virtual size_t num_dimensions() const =0</div><div class="ttdoc">The number of dimensions of the tensor (rank)</div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_im2_col_kernel_xhtml_a6bc1be5100f77f4a136d8935b06d5ac6"><div class="ttname"><a href="classarm__compute_1_1_c_l_im2_col_kernel.xhtml#a6bc1be5100f77f4a136d8935b06d5ac6">arm_compute::CLIm2ColKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output, const Size2D &amp;kernel_dims, const PadStrideInfo &amp;conv_info, bool has_bias, const Size2D &amp;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="_c_l_im2_col_kernel_8cpp_source.xhtml#l00294">CLIm2ColKernel.cpp:294</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_acb74edf42335de0dca0da5158b704c4b"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#acb74edf42335de0dca0da5158b704c4b">arm_compute::TensorShape::remove_dimension</a></div><div class="ttdeci">void remove_dimension(size_t n)</div><div class="ttdoc">Accessor to remove the dimension n from the tensor shape.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00110">TensorShape.h:110</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_locally_connected_matrix_multiply_kernel_xhtml_a083cbb32c94ef493098ee169dc36c486"><div class="ttname"><a href="classarm__compute_1_1_c_l_locally_connected_matrix_multiply_kernel.xhtml#a083cbb32c94ef493098ee169dc36c486">arm_compute::CLLocallyConnectedMatrixMultiplyKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output)</div><div class="ttdoc">Initialise the kernel's input, output and alpha.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_locally_connected_matrix_multiply_kernel_8cpp_source.xhtml#l00084">CLLocallyConnectedMatrixMultiplyKernel.cpp:84</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#l00035">CLTensor.cpp:35</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_weights_reshape_kernel_xhtml_a90066609e6f00aad9d9a2a8951aef7aa"><div class="ttname"><a href="classarm__compute_1_1_c_l_weights_reshape_kernel.xhtml#a90066609e6f00aad9d9a2a8951aef7aa">arm_compute::CLWeightsReshapeKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *biases, const ITensorInfo *output, unsigned int num_groups=1)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLWeightsReshapeKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_weights_reshape_kernel_8cpp_source.xhtml#l00112">CLWeightsReshapeKernel.cpp:112</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a178f0d3d87f959e00a743328d95359d2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">arm_compute::ITensorInfo::dimension</a></div><div class="ttdeci">virtual size_t dimension(size_t index) const =0</div><div class="ttdoc">Return the size of the requested dimension.</div></div>
<div class="ttc" id="namespacearm__compute_xhtml_abbe3399a66c35c3c353030cd0a84c936"><div class="ttname"><a href="namespacearm__compute.xhtml#abbe3399a66c35c3c353030cd0a84c936">arm_compute::scaled_dimensions</a></div><div class="ttdeci">std::pair&lt; unsigned int, unsigned int &gt; scaled_dimensions(unsigned int width, unsigned int height, unsigned int kernel_width, unsigned int kernel_height, const PadStrideInfo &amp;pad_stride_info, const Size2D &amp;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#l00387">Utils.cpp:387</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a9b58d0eb9a2af8e6d7908695e1557d6c"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a9b58d0eb9a2af8e6d7908695e1557d6c">arm_compute::CLScheduler::get</a></div><div class="ttdeci">static CLScheduler &amp; get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00041">CLScheduler.cpp:41</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#l00599">Winograd.cpp:599</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#l00193">Error.h:193</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#l00223">TensorInfo.h:223</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7cfb31af63202568efef5214acfbf3ba"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">arm_compute::ITensorInfo::data_type</a></div><div class="ttdeci">virtual DataType data_type() const =0</div><div class="ttdoc">Data type used for each element of the tensor.</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="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00337">Error.h:337</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_locally_connected_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::CLLocallyConnectedLayer::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="_c_l_locally_connected_layer_8cpp_source.xhtml#l00191">CLLocallyConnectedLayer.cpp:191</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml">arm_compute::ITensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_info_8h_source.xhtml#l00040">ITensorInfo.h:40</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_a4083de30daebd6bdee6b35d9c8262108"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#a4083de30daebd6bdee6b35d9c8262108">arm_compute::CLTensor::allocator</a></div><div class="ttdeci">CLTensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00055">CLTensor.cpp:55</a></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#l00327">Error.h:327</a></div></div>
<div class="ttc" id="classarm__compute_1_1_status_xhtml"><div class="ttname"><a href="classarm__compute_1_1_status.xhtml">arm_compute::Status</a></div><div class="ttdoc">Status class.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00052">Error.h:52</a></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#l00244">Error.h:244</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_allocator_xhtml_af36143939a43fa124312e395975091ed"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">arm_compute::ITensorAllocator::init</a></div><div class="ttdeci">void init(const TensorInfo &amp;input, size_t alignment=0)</div><div class="ttdoc">Initialize a tensor based on the passed TensorInfo.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_allocator_8cpp_source.xhtml#l00038">ITensorAllocator.cpp:38</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2018 ARM Limited.</div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_col2_im_kernel_xhtml_a1877ad9505323d75f1a301f30a528ab4"><div class="ttname"><a href="classarm__compute_1_1_c_l_col2_im_kernel.xhtml#a1877ad9505323d75f1a301f30a528ab4">arm_compute::CLCol2ImKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &amp;convolved_dims, unsigned int num_groups=1)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLCol2ImKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_col2_im_kernel_8cpp_source.xhtml#l00134">CLCol2ImKernel.cpp:134</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a9a3e72153aeb3ed212e9c3698774e881"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">arm_compute::TensorInfo::data_type</a></div><div class="ttdeci">DataType data_type() const override</div><div class="ttdoc">Data type used for each element of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00256">TensorInfo.h:256</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="arm__compute_2core_2_utils_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_utils_8h.xhtml">Utils.h</a></div></div>
<div class="ttc" id="_c_l_scheduler_8h_xhtml"><div class="ttname"><a href="_c_l_scheduler_8h.xhtml">CLScheduler.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_im2_col_kernel_xhtml_a4e256965ba7798ffe1358469be661e5a"><div class="ttname"><a href="classarm__compute_1_1_c_l_im2_col_kernel.xhtml#a4e256965ba7798ffe1358469be661e5a">arm_compute::CLIm2ColKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &amp;kernel_dims, const PadStrideInfo &amp;conv_info, bool has_bias, const Size2D &amp;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 CLIm2ColKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_im2_col_kernel_8cpp_source.xhtml#l00342">CLIm2ColKernel.cpp:342</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a6dc630a6ae9cc063b3924bcea8dee9d6"><div class="ttname"><a href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(...)</div><div class="ttdoc">To avoid unused variables warnings.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00160">Error.h:160</a></div></div>
<div class="ttc" id="classarm__compute_1_1_memory_group_base_xhtml_ac1f67376afb7822f262a0174ef4a3104"><div class="ttname"><a href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">arm_compute::MemoryGroupBase::manage</a></div><div class="ttdeci">void manage(TensorType *obj)</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_base_8h_source.xhtml#l00102">MemoryGroupBase.h:102</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 &amp; tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor.</div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_weights_reshape_kernel_xhtml_a32630202628891fb30458ef84581ad2a"><div class="ttname"><a href="classarm__compute_1_1_c_l_weights_reshape_kernel.xhtml#a32630202628891fb30458ef84581ad2a">arm_compute::CLWeightsReshapeKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, const ICLTensor *biases, ICLTensor *output, 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="_c_l_weights_reshape_kernel_8cpp_source.xhtml#l00078">CLWeightsReshapeKernel.cpp:78</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a86084036bd3851575ef871ad5bf079a7"><div class="ttname"><a href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(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#l00214">Error.h:214</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a0e95dc1e53c361348314873b168ae237"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">arm_compute::ITensor::info</a></div><div class="ttdeci">virtual ITensorInfo * info() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_locally_connected_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CLLocallyConnectedLayer::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="_c_l_locally_connected_layer_8cpp_source.xhtml#l00175">CLLocallyConnectedLayer.cpp:175</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_locally_connected_layer_xhtml_a69b25e30cdc636553317bc9cf6f8315b"><div class="ttname"><a href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml#a69b25e30cdc636553317bc9cf6f8315b">arm_compute::CLLocallyConnectedLayer::CLLocallyConnectedLayer</a></div><div class="ttdeci">CLLocallyConnectedLayer(std::shared_ptr&lt; IMemoryManager &gt; memory_manager=nullptr)</div><div class="ttdoc">Default constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_locally_connected_layer_8cpp_source.xhtml#l00080">CLLocallyConnectedLayer.cpp:80</a></div></div>
<div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00676">Types.h:676</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a9aeced5a5128f60a31ea3e327a45ee21"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">arm_compute::test::validation::has_bias</a></div><div class="ttdeci">const bool has_bias</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00147">Im2Col.cpp:147</a></div></div>
<div class="ttc" id="_c_l_locally_connected_layer_8h_xhtml"><div class="ttname"><a href="_c_l_locally_connected_layer_8h.xhtml">CLLocallyConnectedLayer.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_ae1a643e517f50bf0392fb6516dd7cf67"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#ae1a643e517f50bf0392fb6516dd7cf67">arm_compute::CLScheduler::enqueue</a></div><div class="ttdeci">void enqueue(ICLKernel &amp;kernel, bool flush=true)</div><div class="ttdoc">Schedule the execution of the passed kernel if possible.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8cpp_source.xhtml#l00095">CLScheduler.cpp:95</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_ad381d1aed28b4b1e1f5a710633934580"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#ad381d1aed28b4b1e1f5a710633934580">arm_compute::CLScheduler::queue</a></div><div class="ttdeci">cl::CommandQueue &amp; queue()</div><div class="ttdoc">Accessor for the associated CL command queue.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8h_source.xhtml#l00102">CLScheduler.h:102</a></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="classarm__compute_1_1_c_l_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::CLTensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of OpenCL memory.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_allocator_8cpp_source.xhtml#l00119">CLTensorAllocator.cpp:119</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="classarm__compute_1_1_memory_group_resource_scope_xhtml"><div class="ttname"><a href="classarm__compute_1_1_memory_group_resource_scope.xhtml">arm_compute::MemoryGroupResourceScope</a></div><div class="ttdoc">Memory group resources scope handling class.</div><div class="ttdef"><b>Definition:</b> <a href="_i_memory_group_8h_source.xhtml#l00046">IMemoryGroup.h:46</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_c_l_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_c_l_tensor.xhtml">arm_compute::ICLTensor</a></div><div class="ttdoc">Interface for OpenCL tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_l_tensor_8h_source.xhtml#l00042">ICLTensor.h:42</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_locally_connected_matrix_multiply_kernel_xhtml_a0d647d83c8512fa95fa9adb8fb3e0cab"><div class="ttname"><a href="classarm__compute_1_1_c_l_locally_connected_matrix_multiply_kernel.xhtml#a0d647d83c8512fa95fa9adb8fb3e0cab">arm_compute::CLLocallyConnectedMatrixMultiplyKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLLocallyConnectedMatrix...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_locally_connected_matrix_multiply_kernel_8cpp_source.xhtml#l00121">CLLocallyConnectedMatrixMultiplyKernel.cpp:121</a></div></div>
<div class="ttc" id="classarm__compute_1_1_size2_d_xhtml"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml">arm_compute::Size2D</a></div><div class="ttdoc">Class for specifying the size of an image or rectangle.</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00034">Size2D.h:34</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a80a5f2d6e3a697c9aad893a3b4242615"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a80a5f2d6e3a697c9aad893a3b4242615">arm_compute::Dimensions::num_dimensions</a></div><div class="ttdeci">unsigned int num_dimensions() const</div><div class="ttdoc">Returns the effective dimensionality of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00122">Dimensions.h:122</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">ConvolutionLayer.cpp:180</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 &amp; 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="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a6c2059df991a75abef4eb643510c9544"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a6c2059df991a75abef4eb643510c9544">arm_compute::CLScheduler::tune_kernel_static</a></div><div class="ttdeci">void tune_kernel_static(ICLKernel &amp;kernel)</div><div class="ttdoc">Tunes OpenCL kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8h_source.xhtml#l00172">CLScheduler.h:172</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_locally_connected_layer_xhtml_a0e2cfb6fb263bd6f761756c816574345"><div class="ttname"><a href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml#a0e2cfb6fb263bd6f761756c816574345">arm_compute::CLLocallyConnectedLayer::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &amp;conv_info)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_locally_connected_layer_8cpp_source.xhtml#l00130">CLLocallyConnectedLayer.cpp:130</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_col2_im_kernel_xhtml_a109e7bd51a51a6aecaa2402a06deb3a7"><div class="ttname"><a href="classarm__compute_1_1_c_l_col2_im_kernel.xhtml#a109e7bd51a51a6aecaa2402a06deb3a7">arm_compute::CLCol2ImKernel::configure</a></div><div class="ttdeci">void configure(const ICLTensor *input, ICLTensor *output, const Size2D &amp;convolved_dims, 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="_c_l_col2_im_kernel_8cpp_source.xhtml#l00091">CLCol2ImKernel.cpp:91</a></div></div>
<div class="ttc" id="_validate_8h_xhtml"><div class="ttname"><a href="_validate_8h.xhtml">Validate.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_locally_connected_layer_xhtml_a126f91e344585b85ae09a7b76e68c520"><div class="ttname"><a href="classarm__compute_1_1_c_l_locally_connected_layer.xhtml#a126f91e344585b85ae09a7b76e68c520">arm_compute::CLLocallyConnectedLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &amp;conv_info)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CLLocallyConnectedLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_locally_connected_layer_8cpp_source.xhtml#l00086">CLLocallyConnectedLayer.cpp:86</a></div></div>
</div><!-- fragment --></div><!-- contents -->
</div><!-- doc-content -->
<!-- start footer part -->
<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
<ul>
<li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_bf9f26469d00835ba20ff8d80ee5a804.xhtml">runtime</a></li><li class="navelem"><a class="el" href="dir_43c3fdbf778d1fd99e2e38f09fddd920.xhtml">CL</a></li><li class="navelem"><a class="el" href="dir_0304d3529340c629ae0050036d07056a.xhtml">functions</a></li><li class="navelem"><a class="el" href="_c_l_locally_connected_layer_8cpp.xhtml">CLLocallyConnectedLayer.cpp</a></li>
<li class="footer">Generated on Mon Sep 2 2019 11:47:25 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>