| <!-- HTML header for doxygen 1.8.9.1--> |
| <!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 1.8.11"/> |
| <meta name="robots" content="NOINDEX, NOFOLLOW" /> <!-- Prevent indexing by search engines --> |
| <title>Compute Library: utils/GraphUtils.cpp Source File</title> |
| <link href="tabs.css" rel="stylesheet" type="text/css"/> |
| <script type="text/javascript" src="jquery.js"></script> |
| <script type="text/javascript" src="dynsections.js"></script> |
| <link href="navtree.css" rel="stylesheet" type="text/css"/> |
| <script type="text/javascript" src="resize.js"></script> |
| <script type="text/javascript" src="navtreedata.js"></script> |
| <script type="text/javascript" src="navtree.js"></script> |
| <script type="text/javascript"> |
| $(document).ready(initResizable); |
| $(window).load(resizeHeight); |
| </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/javascript"> |
| $(document).ready(function() { init_search(); }); |
| </script> |
| <script type="text/x-mathjax-config"> |
| MathJax.Hub.Config({ |
| extensions: ["tex2jax.js"], |
| jax: ["input/TeX","output/HTML-CSS"], |
| }); |
| </script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> |
| <link href="doxygen.css" rel="stylesheet" type="text/css" /> |
| </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;"> |
| <td style="padding-left: 0.5em;"> |
| <div id="projectname">Compute Library |
|  <span id="projectnumber">18.03</span> |
| </div> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <!-- end header part --> |
| <!-- Generated by Doxygen 1.8.11 --> |
| <script type="text/javascript"> |
| var searchBox = new SearchBox("searchBox", "search",false,'Search'); |
| </script> |
| <div id="navrow1" class="tabs"> |
| <ul class="tablist"> |
| <li><a href="index.xhtml"><span>Main Page</span></a></li> |
| <li><a href="pages.xhtml"><span>Related Pages</span></a></li> |
| <li><a href="namespaces.xhtml"><span>Namespaces</span></a></li> |
| <li><a href="annotated.xhtml"><span>Data Structures</span></a></li> |
| <li class="current"><a href="files.xhtml"><span>Files</span></a></li> |
| <li> |
| <div id="MSearchBox" class="MSearchBoxInactive"> |
| <span class="left"> |
| <img id="MSearchSelect" src="search/mag_sel.png" |
| onmouseover="return searchBox.OnSearchSelectShow()" |
| onmouseout="return searchBox.OnSearchSelectHide()" |
| alt=""/> |
| <input type="text" id="MSearchField" value="Search" accesskey="S" |
| onfocus="searchBox.OnSearchFieldFocus(true)" |
| onblur="searchBox.OnSearchFieldFocus(false)" |
| onkeyup="searchBox.OnSearchFieldChange(event)"/> |
| </span><span class="right"> |
| <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a> |
| </span> |
| </div> |
| </li> |
| </ul> |
| </div> |
| <div id="navrow2" class="tabs2"> |
| <ul class="tablist"> |
| <li><a href="files.xhtml"><span>File List</span></a></li> |
| <li><a href="globals.xhtml"><span>Globals</span></a></li> |
| </ul> |
| </div> |
| </div><!-- top --> |
| <div id="side-nav" class="ui-resizable side-nav-resizable"> |
| <div id="nav-tree"> |
| <div id="nav-tree-contents"> |
| <div id="nav-sync" class="sync"></div> |
| </div> |
| </div> |
| <div id="splitbar" style="-moz-user-select:none;" |
| class="ui-resizable-handle"> |
| </div> |
| </div> |
| <script type="text/javascript"> |
| $(document).ready(function(){initNavTree('_graph_utils_8cpp_source.xhtml','');}); |
| </script> |
| <div id="doc-content"> |
| <!-- window showing the filter options --> |
| <div id="MSearchSelectWindow" |
| onmouseover="return searchBox.OnSearchSelectShow()" |
| onmouseout="return searchBox.OnSearchSelectHide()" |
| onkeydown="return searchBox.OnSearchSelectKey(event)"> |
| </div> |
| |
| <!-- iframe showing the search results (closed by default) --> |
| <div id="MSearchResultsWindow"> |
| <iframe src="javascript:void(0)" frameborder="0" |
| name="MSearchResults" id="MSearchResults"> |
| </iframe> |
| </div> |
| |
| <div class="header"> |
| <div class="headertitle"> |
| <div class="title">GraphUtils.cpp</div> </div> |
| </div><!--header--> |
| <div class="contents"> |
| <a href="_graph_utils_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> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2017-2018 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#include "<a class="code" href="_graph_utils_8h.xhtml">utils/GraphUtils.h</a>"</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="runtime_2_sub_tensor_8h.xhtml">arm_compute/runtime/SubTensor.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="utils_2_utils_8h.xhtml">utils/Utils.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#ifdef ARM_COMPUTE_CL</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "<a class="code" href="_open_c_l_8h.xhtml">arm_compute/core/CL/OpenCL.h</a>"</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="_c_l_tensor_8h.xhtml">arm_compute/runtime/CL/CLTensor.h</a>"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#endif </span><span class="comment">/* ARM_COMPUTE_CL */</span><span class="preprocessor"></span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include <iomanip></span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a>;</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> </div><div class="line"><a name="l00039"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_t_f_preproccessor.xhtml#a1545d087d050b8e9733e7e212df73666"> 39</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_t_f_preproccessor.xhtml#a1545d087d050b8e9733e7e212df73666">TFPreproccessor::preprocess</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> {</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  window.<a class="code" href="classarm__compute_1_1_window.xhtml#a14470b4cb59140a1b6ff3b8f16c89ab6">use_tensor_dimensions</a>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>());</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <a class="code" href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">execute_window_loop</a>(window, [&](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & <span class="keywordtype">id</span>)</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  {</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a> = *<span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span> *<span class="keyword">></span>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a48c7a05cc63f541d732250e39339cee2">ptr_to_element</a>(<span class="keywordtype">id</span>));</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keywordtype">float</span> res = value / 255.f; <span class="comment">// Normalize to [0, 1]</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  res = (res - 0.5f) * 2.f; <span class="comment">// Map to [-1, 1]</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  *<span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span> *<span class="keyword">></span>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a48c7a05cc63f541d732250e39339cee2">ptr_to_element</a>(<span class="keywordtype">id</span>)) = res;</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  });</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> }</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> </div><div class="line"><a name="l00053"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_caffe_preproccessor.xhtml#a60e1354276a6ffd7359634e1ab464cff"> 53</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_caffe_preproccessor.xhtml#a60e1354276a6ffd7359634e1ab464cff">CaffePreproccessor::CaffePreproccessor</a>(std::array<float, 3> mean, <span class="keywordtype">bool</span> bgr)</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  : _mean(mean), _bgr(bgr)</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> {</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">if</span>(_bgr)</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  {</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a28edc8880596d14c099f3c2509efc8b3">std::swap</a>(_mean[0], _mean[2]);</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  }</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> }</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div><div class="line"><a name="l00062"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_caffe_preproccessor.xhtml#a1545d087d050b8e9733e7e212df73666"> 62</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_caffe_preproccessor.xhtml#a1545d087d050b8e9733e7e212df73666">CaffePreproccessor::preprocess</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> {</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  window.<a class="code" href="classarm__compute_1_1_window.xhtml#a14470b4cb59140a1b6ff3b8f16c89ab6">use_tensor_dimensions</a>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>());</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <a class="code" href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">execute_window_loop</a>(window, [&](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & <span class="keywordtype">id</span>)</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  {</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a> = *<span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span> *<span class="keyword">></span>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a48c7a05cc63f541d732250e39339cee2">ptr_to_element</a>(<span class="keywordtype">id</span>)) - _mean[<span class="keywordtype">id</span>.z()];</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  *<span class="keyword">reinterpret_cast<</span><span class="keywordtype">float</span> *<span class="keyword">></span>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a48c7a05cc63f541d732250e39339cee2">ptr_to_element</a>(<span class="keywordtype">id</span>)) = value;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  });</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> }</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_p_p_m_writer.xhtml#a9ea94bf5b7d00ddb836df1b8dcedb93a"> 74</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_p_p_m_writer.xhtml#a9ea94bf5b7d00ddb836df1b8dcedb93a">PPMWriter::PPMWriter</a>(std::string name, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> maximum)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  : _name(std::move(name)), _iterator(0), _maximum(maximum)</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> {</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> }</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_p_p_m_writer.xhtml#ab469d593b4bc92e1d1132a03de0aedca"> 79</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_p_p_m_writer.xhtml#ab469d593b4bc92e1d1132a03de0aedca">PPMWriter::access_tensor</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> {</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  std::stringstream ss;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  ss << _name << _iterator << <span class="stringliteral">".ppm"</span>;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <a class="code" href="namespacearm__compute_1_1utils.xhtml#a301d0b7bfd70f73fc1924f4281938d08">arm_compute::utils::save_to_ppm</a>(tensor, ss.str());</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  _iterator++;</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordflow">if</span>(_maximum == 0)</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  {</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  }</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keywordflow">return</span> _iterator < _maximum;</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> }</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00094"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml#ac5ae9597ba20e5581726743fe7c154b5"> 94</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml#ac5ae9597ba20e5581726743fe7c154b5">DummyAccessor::DummyAccessor</a>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> maximum)</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  : _iterator(0), _maximum(maximum)</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="line"><a name="l00098"></a><span class="lineno"> 98</span> </div><div class="line"><a name="l00099"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca"> 99</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">DummyAccessor::access_tensor</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> {</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <a class="code" href="core_2_error_8h.xhtml#a4103adbb45806b2f2002d44b91d0d206">ARM_COMPUTE_UNUSED</a>(tensor);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keywordtype">bool</span> <a class="code" href="hwc_8hpp.xhtml#a4fef07ab304fc672e0407e7598fb1870">ret</a> = _maximum == 0 || _iterator < _maximum;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordflow">if</span>(_iterator == _maximum)</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  {</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  _iterator = 0;</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  }</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  {</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  _iterator++;</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  }</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keywordflow">return</span> <a class="code" href="hwc_8hpp.xhtml#a4fef07ab304fc672e0407e7598fb1870">ret</a>;</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> }</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> </div><div class="line"><a name="l00114"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_p_p_m_accessor.xhtml#a0d3c4a4f52edede1f0a6d31a8f0b4ffb"> 114</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_p_p_m_accessor.xhtml#a0d3c4a4f52edede1f0a6d31a8f0b4ffb">PPMAccessor::PPMAccessor</a>(std::string ppm_path, <span class="keywordtype">bool</span> bgr, std::unique_ptr<IPreprocessor> preprocessor)</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  : _ppm_path(std::move(ppm_path)), _bgr(bgr), _preprocessor(std::move(preprocessor))</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> {</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> }</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div><div class="line"><a name="l00119"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_p_p_m_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca"> 119</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_p_p_m_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">PPMAccessor::access_tensor</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> {</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <a class="code" href="classarm__compute_1_1utils_1_1_p_p_m_loader.xhtml">utils::PPMLoader</a> ppm;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> </div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="comment">// Open PPM file</span></div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  ppm.<a class="code" href="classarm__compute_1_1utils_1_1_p_p_m_loader.xhtml#a36e58f3e64f3851ebac7a9556b4704ed">open</a>(_ppm_path);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> </div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <a class="code" href="core_2_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(ppm.<a class="code" href="classarm__compute_1_1utils_1_1_p_p_m_loader.xhtml#a7b736ec9a05be5c498760d35a0406ed3">width</a>() != tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) || ppm.<a class="code" href="classarm__compute_1_1utils_1_1_p_p_m_loader.xhtml#aa420a28166e708e3f8b9ecc8e527fc09">height</a>() != tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1),</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="stringliteral">"Failed to load image file: dimensions [%d,%d] not correct, expected [%d,%d]."</span>, ppm.<a class="code" href="classarm__compute_1_1utils_1_1_p_p_m_loader.xhtml#a7b736ec9a05be5c498760d35a0406ed3">width</a>(), ppm.<a class="code" href="classarm__compute_1_1utils_1_1_p_p_m_loader.xhtml#aa420a28166e708e3f8b9ecc8e527fc09">height</a>(), tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0), tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1));</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> </div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="comment">// Fill the tensor with the PPM content (BGR)</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  ppm.<a class="code" href="classarm__compute_1_1utils_1_1_p_p_m_loader.xhtml#a7c3f70e1caee95bb95c62346e130e5ab">fill_planar_tensor</a>(tensor, _bgr);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> </div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="comment">// Preprocess tensor</span></div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keywordflow">if</span>(_preprocessor)</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  {</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  _preprocessor->preprocess(tensor);</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  }</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> </div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> }</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_top_n_predictions_accessor.xhtml#ad12f4e3c945ec4fad9ab6386954a3550"> 141</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_top_n_predictions_accessor.xhtml#ad12f4e3c945ec4fad9ab6386954a3550">TopNPredictionsAccessor::TopNPredictionsAccessor</a>(<span class="keyword">const</span> std::string &labels_path, <span class="keywordtype">size_t</span> top_n, std::ostream &output_stream)</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  : _labels(), _output_stream(output_stream), _top_n(top_n)</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> {</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  _labels.clear();</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> </div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  std::ifstream ifs;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> </div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  {</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  ifs.exceptions(std::ifstream::badbit);</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  ifs.open(labels_path, std::ios::in | std::ios::binary);</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> </div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keywordflow">for</span>(std::string line; !std::getline(ifs, line).fail();)</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  {</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  _labels.emplace_back(line);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  }</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  }</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keywordflow">catch</span>(<span class="keyword">const</span> std::ifstream::failure &e)</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  {</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <a class="code" href="core_2_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Accessing %s: %s"</span>, labels_path.c_str(), e.what());</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  }</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> }</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span> </div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> <span class="keywordtype">void</span> TopNPredictionsAccessor::access_predictions_tensor(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> {</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="comment">// Get the predicted class</span></div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  std::vector<T> classes_prob;</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  std::vector<size_t> index;</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> </div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keyword">const</span> <span class="keyword">auto</span> output_net = <span class="keyword">reinterpret_cast<</span>T *<span class="keyword">></span>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#ab988210662dbd3bf32fd563c7dd1bdbf">buffer</a>() + tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#ad0bd5cc32e7e4c0699eccba91e5de397">offset_first_element_in_bytes</a>());</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_classes = tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> </div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  classes_prob.resize(num_classes);</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  index.resize(num_classes);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> </div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  std::copy(output_net, output_net + num_classes, classes_prob.begin());</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> </div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="comment">// Sort results</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  std::iota(std::begin(index), std::end(index), static_cast<size_t>(0));</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  std::sort(std::begin(index), std::end(index),</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  [&](<span class="keywordtype">size_t</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>, <span class="keywordtype">size_t</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>)</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  {</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keywordflow">return</span> classes_prob[<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>] > classes_prob[<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">b</a>];</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  });</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> </div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  _output_stream << <span class="stringliteral">"---------- Top "</span> << _top_n << <span class="stringliteral">" predictions ----------"</span> << std::endl</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  << std::endl;</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i = 0; i < _top_n; ++i)</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  {</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  _output_stream << std::fixed << std::setprecision(4)</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  << +classes_prob[index.at(i)]</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  << <span class="stringliteral">" - [id = "</span> << index.at(i) << <span class="stringliteral">"]"</span></div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  << <span class="stringliteral">", "</span> << _labels[index.at(i)] << std::endl;</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  }</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> }</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> </div><div class="line"><a name="l00198"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_top_n_predictions_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca"> 198</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_top_n_predictions_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">TopNPredictionsAccessor::access_tensor</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> {</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(&tensor, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <a class="code" href="core_2_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(_labels.size() != tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0));</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> </div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keywordflow">switch</span>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>())</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  {</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>:</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  access_predictions_tensor<uint8_t>(tensor);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  access_predictions_tensor<float>(tensor);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <a class="code" href="core_2_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"NOT SUPPORTED!"</span>);</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  }</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> </div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span> }</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div><div class="line"><a name="l00218"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_random_accessor.xhtml#a47e2e3f731e842dde7baaf69634a9530"> 218</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_random_accessor.xhtml#a47e2e3f731e842dde7baaf69634a9530">RandomAccessor::RandomAccessor</a>(<a class="code" href="classarm__compute_1_1_pixel_value.xhtml">PixelValue</a> lower, <a class="code" href="classarm__compute_1_1_pixel_value.xhtml">PixelValue</a> upper, std::random_device::result_type seed)</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  : _lower(lower), _upper(upper), _seed(seed)</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> {</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> }</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span> </div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> <span class="keywordtype">void</span> RandomAccessor::fill(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor, D &&distribution)</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span> {</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  std::mt19937 gen(_seed);</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> </div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keywordflow">if</span>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a07b929c34ad1dc823d8315876aa403ce">padding</a>().<a class="code" href="structarm__compute_1_1_border_size.xhtml#adffbf97e7b8b64e7cf32f0254cddf3c4">empty</a>() && (<span class="keyword">dynamic_cast<</span><a class="code" href="classarm__compute_1_1_sub_tensor.xhtml">SubTensor</a> *<span class="keyword">></span>(&tensor) == <span class="keyword">nullptr</span>))</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  {</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = 0; <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> < tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a18064e0011c3869d884653e9e7c47b66">total_size</a>(); <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> += tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#aa459796b5489eca8a9160cb5dcf1a103">element_size</a>())</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  {</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keyword">const</span> T <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a> = distribution(gen);</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  *<span class="keyword">reinterpret_cast<</span>T *<span class="keyword">></span>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#ab988210662dbd3bf32fd563c7dd1bdbf">buffer</a>() + <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>) = value;</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  }</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  }</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  {</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="comment">// If tensor has padding accessing tensor elements through execution window.</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  window.<a class="code" href="classarm__compute_1_1_window.xhtml#a14470b4cb59140a1b6ff3b8f16c89ab6">use_tensor_dimensions</a>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>());</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <a class="code" href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">execute_window_loop</a>(window, [&](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & <span class="keywordtype">id</span>)</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  {</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keyword">const</span> T <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a> = distribution(gen);</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  *<span class="keyword">reinterpret_cast<</span>T *<span class="keyword">></span>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a48c7a05cc63f541d732250e39339cee2">ptr_to_element</a>(<span class="keywordtype">id</span>)) = value;</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  });</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  }</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> }</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span> </div><div class="line"><a name="l00250"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_random_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca"> 250</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_random_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">RandomAccessor::access_tensor</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> {</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keywordflow">switch</span>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>())</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  {</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">DataType::U8</a>:</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  {</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  std::uniform_int_distribution<uint8_t> distribution_u8(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><uint8_t>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><uint8_t>());</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  fill<uint8_t>(tensor, distribution_u8);</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  }</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">DataType::S8</a>:</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">DataType::QS8</a>:</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  {</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  std::uniform_int_distribution<int8_t> distribution_s8(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><int8_t>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><int8_t>());</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  fill<int8_t>(tensor, distribution_s8);</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  }</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">DataType::U16</a>:</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  {</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  std::uniform_int_distribution<uint16_t> distribution_u16(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><uint16_t>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><uint16_t>());</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  fill<uint16_t>(tensor, distribution_u16);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keywordflow">break</span>;</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="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">DataType::S16</a>:</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9">DataType::QS16</a>:</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  {</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  std::uniform_int_distribution<int16_t> distribution_s16(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><int16_t>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><int16_t>());</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  fill<int16_t>(tensor, distribution_s16);</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  }</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">DataType::U32</a>:</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  {</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  std::uniform_int_distribution<uint32_t> distribution_u32(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><uint32_t>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><uint32_t>());</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  fill<uint32_t>(tensor, distribution_u32);</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keywordflow">break</span>;</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>:</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  {</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  std::uniform_int_distribution<int32_t> distribution_s32(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><int32_t>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><int32_t>());</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  fill<int32_t>(tensor, distribution_s32);</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keywordflow">break</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">DataType::U64</a>:</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  {</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  std::uniform_int_distribution<uint64_t> distribution_u64(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><uint64_t>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><uint64_t>());</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  fill<uint64_t>(tensor, distribution_u64);</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <span class="keywordflow">break</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="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">DataType::S64</a>:</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  {</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  std::uniform_int_distribution<int64_t> distribution_s64(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><int64_t>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><int64_t>());</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  fill<int64_t>(tensor, distribution_s64);</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  }</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  {</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  std::uniform_real_distribution<float> distribution_f16(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><<span class="keywordtype">float</span>>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><<span class="keywordtype">float</span>>());</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  fill<float>(tensor, distribution_f16);</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  }</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  {</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  std::uniform_real_distribution<float> distribution_f32(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><<span class="keywordtype">float</span>>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><<span class="keywordtype">float</span>>());</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  fill<float>(tensor, distribution_f32);</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="keywordflow">break</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">DataType::F64</a>:</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  {</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  std::uniform_real_distribution<double> distribution_f64(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><<span class="keywordtype">double</span>>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">get</a><<span class="keywordtype">double</span>>());</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  fill<double>(tensor, distribution_f64);</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  }</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <a class="code" href="core_2_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"NOT SUPPORTED!"</span>);</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="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> }</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span> </div><div class="line"><a name="l00328"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader.xhtml#ab708c11aea0f419821cd053e3e7dae89"> 328</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader.xhtml#ab708c11aea0f419821cd053e3e7dae89">NumPyBinLoader::NumPyBinLoader</a>(std::string filename)</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  : _filename(std::move(filename))</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> </div><div class="line"><a name="l00333"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader.xhtml#ab469d593b4bc92e1d1132a03de0aedca"> 333</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader.xhtml#ab469d593b4bc92e1d1132a03de0aedca">NumPyBinLoader::access_tensor</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</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>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> tensor_shape = tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  std::vector<unsigned long> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>;</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> </div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="comment">// Open file</span></div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  std::ifstream stream(_filename, std::ios::in | std::ios::binary);</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <a class="code" href="core_2_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(!stream.good(), <span class="stringliteral">"Failed to load binary data"</span>);</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  std::string <a class="code" href="hwc_8hpp.xhtml#a9d9174de5edba1e56fbfd90dc5e60f75">header</a> = npy::read_header(stream);</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span> </div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <span class="comment">// Parse header</span></div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <span class="keywordtype">bool</span> fortran_order = <span class="keyword">false</span>;</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  std::string typestr;</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  npy::parse_header(header, typestr, fortran_order, shape);</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">// Check if the typestring matches the given one</span></div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  std::string expect_typestr = <a class="code" href="namespacearm__compute_1_1utils.xhtml#a7990fd2b875e2f6d4c1eded54539cb19">arm_compute::utils::get_typestring</a>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>());</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <a class="code" href="core_2_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(typestr != expect_typestr, <span class="stringliteral">"Typestrings mismatch"</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">// Reverse vector in case of non fortran order</span></div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="keywordflow">if</span>(!fortran_order)</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  {</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  std::reverse(shape.begin(), shape.end());</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> </div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="comment">// Correct dimensions (Needs to match TensorShape dimension corrections)</span></div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="keywordflow">if</span>(shape.size() != tensor_shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a0f59f175e7682c7ed5f4ea30ef687834">num_dimensions</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>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = static_cast<int>(shape.size()) - 1; i > 0; --i)</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  {</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keywordflow">if</span>(shape[i] == 1)</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  {</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  shape.pop_back();</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  }</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <span class="keywordflow">else</span></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">break</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>  }</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  }</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>  <span class="comment">// Validate tensor ranks</span></div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <a class="code" href="core_2_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(shape.size() != tensor_shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a0f59f175e7682c7ed5f4ea30ef687834">num_dimensions</a>(), <span class="stringliteral">"Tensor ranks mismatch"</span>);</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span> </div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <span class="comment">// Validate shapes</span></div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i = 0; i < shape.size(); ++i)</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>  <a class="code" href="core_2_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(tensor_shape[i] != shape[i], <span class="stringliteral">"Tensor dimensions mismatch"</span>);</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  }</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span> </div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="comment">// Read data</span></div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <span class="keywordflow">if</span>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a07b929c34ad1dc823d8315876aa403ce">padding</a>().<a class="code" href="structarm__compute_1_1_border_size.xhtml#adffbf97e7b8b64e7cf32f0254cddf3c4">empty</a>() && (<span class="keyword">dynamic_cast<</span><a class="code" href="classarm__compute_1_1_sub_tensor.xhtml">SubTensor</a> *<span class="keyword">></span>(&tensor) == <span class="keyword">nullptr</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>  <span class="comment">// If tensor has no padding read directly from stream.</span></div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  stream.read(reinterpret_cast<char *>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#ab988210662dbd3bf32fd563c7dd1bdbf">buffer</a>()), tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a18064e0011c3869d884653e9e7c47b66">total_size</a>());</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  }</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  {</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="comment">// If tensor has padding accessing tensor elements through execution window.</span></div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  window.<a class="code" href="classarm__compute_1_1_window.xhtml#a14470b4cb59140a1b6ff3b8f16c89ab6">use_tensor_dimensions</a>(tensor_shape);</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span> </div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <a class="code" href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">execute_window_loop</a>(window, [&](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & <span class="keywordtype">id</span>)</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  {</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  stream.read(reinterpret_cast<char *>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a48c7a05cc63f541d732250e39339cee2">ptr_to_element</a>(<span class="keywordtype">id</span>)), tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#aa459796b5489eca8a9160cb5dcf1a103">element_size</a>());</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  });</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  }</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span> }</div><div class="ttc" id="classarm__compute_1_1utils_1_1_p_p_m_loader_xhtml_aa420a28166e708e3f8b9ecc8e527fc09"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_p_p_m_loader.xhtml#aa420a28166e708e3f8b9ecc8e527fc09">arm_compute::utils::PPMLoader::height</a></div><div class="ttdeci">unsigned int height() const </div><div class="ttdoc">Return the height of the currently open PPM file. </div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00482">Utils.h:482</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1utils_xhtml_a301d0b7bfd70f73fc1924f4281938d08"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml#a301d0b7bfd70f73fc1924f4281938d08">arm_compute::utils::save_to_ppm</a></div><div class="ttdeci">void save_to_ppm(T &tensor, const std::string &ppm_filename)</div><div class="ttdoc">Template helper function to save a tensor image to a PPM file. </div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00655">Utils.h:655</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_border_size_xhtml_adffbf97e7b8b64e7cf32f0254cddf3c4"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml#adffbf97e7b8b64e7cf32f0254cddf3c4">arm_compute::BorderSize::empty</a></div><div class="ttdeci">constexpr bool empty() const </div><div class="ttdoc">Check if the entire border is zero. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00256">Types.h:256</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_pixel_value_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pixel_value.xhtml">arm_compute::PixelValue</a></div><div class="ttdoc">Class describing the value of a pixel for any image format. </div><div class="ttdef"><b>Definition:</b> <a href="_pixel_value_8h_source.xhtml#l00034">PixelValue.h:34</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9">arm_compute::DataType::QS16</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a45cde9abb508c62d67c3bb2b9bf566a5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">arm_compute::test::validation::shape</a></div><div class="ttdeci">shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_8cpp_source.xhtml#l00133">Convolution.cpp:133</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_top_n_predictions_accessor_xhtml_ab469d593b4bc92e1d1132a03de0aedca"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_top_n_predictions_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">arm_compute::graph_utils::TopNPredictionsAccessor::access_tensor</a></div><div class="ttdeci">bool access_tensor(ITensor &tensor) override</div><div class="ttdoc">Interface to be implemented to access a given tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00198">GraphUtils.cpp:198</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="hwc_8hpp_xhtml_a9d9174de5edba1e56fbfd90dc5e60f75"><div class="ttname"><a href="hwc_8hpp.xhtml#a9d9174de5edba1e56fbfd90dc5e60f75">header</a></div><div class="ttdeci">union kbase_uk_hwcnt_header header</div><div class="ttdef"><b>Definition:</b> <a href="hwc_8hpp_source.xhtml#l00269">hwc.hpp:269</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">arm_compute::DataType::QS8</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_aac40b7097f2bda9274ae07fa33d15a79"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">arm_compute::test::validation::a</a></div><div class="ttdeci">CLTensor a</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00121">GEMM.cpp:121</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="utils_2_utils_8h_xhtml"><div class="ttname"><a href="utils_2_utils_8h.xhtml">Utils.h</a></div></div> |
| <div class="ttc" id="core_2_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="core_2_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="core_2_error_8h_source.xhtml#l00306">Error.h:306</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">arm_compute::Format::U8</a></div><div class="ttdoc">1 channel, 1 U8 per channel </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="_c_l_tensor_8h_xhtml"><div class="ttname"><a href="_c_l_tensor_8h.xhtml">CLTensor.h</a></div></div> |
| <div class="ttc" id="core_2_error_8h_xhtml_a05b19c75afe9c24200a62b9724734bbd"><div class="ttname"><a href="core_2_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR(...)</div><div class="ttdoc">Print the given message then throw an std::runtime_error. </div><div class="ttdef"><b>Definition:</b> <a href="core_2_error_8h_source.xhtml#l00238">Error.h:238</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="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader_xhtml_ab469d593b4bc92e1d1132a03de0aedca"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader.xhtml#ab469d593b4bc92e1d1132a03de0aedca">arm_compute::graph_utils::NumPyBinLoader::access_tensor</a></div><div class="ttdeci">bool access_tensor(ITensor &tensor) override</div><div class="ttdoc">Interface to be implemented to access a given tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00333">GraphUtils.cpp:333</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_xhtml_a28edc8880596d14c099f3c2509efc8b3"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a28edc8880596d14c099f3c2509efc8b3">arm_compute::test::swap</a></div><div class="ttdeci">void swap(SimpleTensor< U > &tensor1, SimpleTensor< U > &tensor2)</div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00367">SimpleTensor.h:367</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">arm_compute::Format::U16</a></div><div class="ttdoc">1 channel, 1 U16 per channel </div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_caffe_preproccessor_xhtml_a1545d087d050b8e9733e7e212df73666"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_caffe_preproccessor.xhtml#a1545d087d050b8e9733e7e212df73666">arm_compute::graph_utils::CaffePreproccessor::preprocess</a></div><div class="ttdeci">void preprocess(ITensor &tensor) override</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00062">GraphUtils.cpp:62</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_random_accessor_xhtml_a47e2e3f731e842dde7baaf69634a9530"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_random_accessor.xhtml#a47e2e3f731e842dde7baaf69634a9530">arm_compute::graph_utils::RandomAccessor::RandomAccessor</a></div><div class="ttdeci">RandomAccessor(PixelValue lower, PixelValue upper, const std::random_device::result_type seed=0)</div><div class="ttdoc">Constructor. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00218">GraphUtils.cpp:218</a></div></div> |
| <div class="ttc" id="helpers_8h_xhtml_a009469e4d9b8fce3b6d5e97d2077827d"><div class="ttname"><a href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a></div><div class="ttdeci">__global uchar * offset(const Image *img, int x, int y)</div><div class="ttdoc">Get the pointer position of a Image. </div><div class="ttdef"><b>Definition:</b> <a href="helpers_8h_source.xhtml#l00301">helpers.h:301</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml">arm_compute::ITensor</a></div><div class="ttdoc">Interface for NEON tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8h_source.xhtml#l00036">ITensor.h:36</a></div></div> |
| <div class="ttc" id="core_2_error_8h_xhtml_a4103adbb45806b2f2002d44b91d0d206"><div class="ttname"><a href="core_2_error_8h.xhtml#a4103adbb45806b2f2002d44b91d0d206">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(var)</div><div class="ttdoc">To avoid unused variables warnings. </div><div class="ttdef"><b>Definition:</b> <a href="core_2_error_8h_source.xhtml#l00147">Error.h:147</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_a14470b4cb59140a1b6ff3b8f16c89ab6"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a14470b4cb59140a1b6ff3b8f16c89ab6">arm_compute::Window::use_tensor_dimensions</a></div><div class="ttdeci">void use_tensor_dimensions(const TensorShape &shape, size_t first_dimension=Window::DimX)</div><div class="ttdoc">Use the tensor&#39;s dimensions to fill the window dimensions. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00240">Window.inl:240</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="hwc_8hpp_xhtml_a4fef07ab304fc672e0407e7598fb1870"><div class="ttname"><a href="hwc_8hpp.xhtml#a4fef07ab304fc672e0407e7598fb1870">ret</a></div><div class="ttdeci">uint32_t ret</div><div class="ttdef"><b>Definition:</b> <a href="hwc_8hpp_source.xhtml#l00261">hwc.hpp:261</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_p_p_m_accessor_xhtml_a0d3c4a4f52edede1f0a6d31a8f0b4ffb"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_p_p_m_accessor.xhtml#a0d3c4a4f52edede1f0a6d31a8f0b4ffb">arm_compute::graph_utils::PPMAccessor::PPMAccessor</a></div><div class="ttdeci">PPMAccessor(std::string ppm_path, bool bgr=true, std::unique_ptr< IPreprocessor > preprocessor=nullptr)</div><div class="ttdoc">Constructor. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00114">GraphUtils.cpp:114</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="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">arm_compute::DataType::S64</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_sub_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_sub_tensor.xhtml">arm_compute::SubTensor</a></div><div class="ttdoc">Basic implementation of the sub-tensor interface. </div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_sub_tensor_8h_source.xhtml#l00037">SubTensor.h:37</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1utils_xhtml_a7990fd2b875e2f6d4c1eded54539cb19"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml#a7990fd2b875e2f6d4c1eded54539cb19">arm_compute::utils::get_typestring</a></div><div class="ttdeci">std::string get_typestring(DataType data_type)</div><div class="ttdoc">Obtain numpy type string from DataType. </div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00122">Utils.h:122</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_pixel_value_xhtml_a3997ae7153b94a3595d1a33186356a5b"><div class="ttname"><a href="classarm__compute_1_1_pixel_value.xhtml#a3997ae7153b94a3595d1a33186356a5b">arm_compute::PixelValue::get</a></div><div class="ttdeci">void get(uint8_t &v) const </div><div class="ttdoc">Interpret the pixel value as a U8. </div><div class="ttdef"><b>Definition:</b> <a href="_pixel_value_8h_source.xhtml#l00157">PixelValue.h:157</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1utils_1_1_p_p_m_loader_xhtml_a36e58f3e64f3851ebac7a9556b4704ed"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_p_p_m_loader.xhtml#a36e58f3e64f3851ebac7a9556b4704ed">arm_compute::utils::PPMLoader::open</a></div><div class="ttdeci">void open(const std::string &ppm_filename)</div><div class="ttdoc">Open a PPM file and reads its metadata (Width, height) </div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00264">Utils.h:264</a></div></div> |
| <div class="ttc" id="_graph_utils_8h_xhtml"><div class="ttname"><a href="_graph_utils_8h.xhtml">GraphUtils.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_random_accessor_xhtml_ab469d593b4bc92e1d1132a03de0aedca"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_random_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">arm_compute::graph_utils::RandomAccessor::access_tensor</a></div><div class="ttdeci">bool access_tensor(ITensor &tensor) override</div><div class="ttdoc">Interface to be implemented to access a given tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00250">GraphUtils.cpp:250</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a48c7a05cc63f541d732250e39339cee2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a48c7a05cc63f541d732250e39339cee2">arm_compute::ITensor::ptr_to_element</a></div><div class="ttdeci">uint8_t * ptr_to_element(const Coordinates &id) const </div><div class="ttdoc">Return a pointer to the element at the passed coordinates. </div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8h_source.xhtml#l00063">ITensor.h:63</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">arm_compute::Format::U32</a></div><div class="ttdoc">1 channel, 1 U32 per channel </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="namespacearm__compute_xhtml_a6c0dcc38187027dcb89cd9724bc5a823"><div class="ttname"><a href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">arm_compute::execute_window_loop</a></div><div class="ttdeci">void execute_window_loop(const Window &w, L &&lambda_function, Ts &&...iterators)</div><div class="ttdoc">Iterate through the passed window, automatically adjusting the iterators and calling the lambda_funct...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00122">Helpers.inl:122</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_caffe_preproccessor_xhtml_a60e1354276a6ffd7359634e1ab464cff"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_caffe_preproccessor.xhtml#a60e1354276a6ffd7359634e1ab464cff">arm_compute::graph_utils::CaffePreproccessor::CaffePreproccessor</a></div><div class="ttdeci">CaffePreproccessor(std::array< float, 3 > mean=std::array< float, 3 >{{0, 0, 0}}, bool bgr=true)</div><div class="ttdoc">Default Constructor. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00053">GraphUtils.cpp:53</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> |
| <div class="ttc" id="classarm__compute_1_1_coordinates_xhtml"><div class="ttname"><a href="classarm__compute_1_1_coordinates.xhtml">arm_compute::Coordinates</a></div><div class="ttdoc">Coordinates of an item. </div><div class="ttdef"><b>Definition:</b> <a href="_coordinates_8h_source.xhtml#l00037">Coordinates.h:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_ab988210662dbd3bf32fd563c7dd1bdbf"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#ab988210662dbd3bf32fd563c7dd1bdbf">arm_compute::ITensor::buffer</a></div><div class="ttdeci">virtual uint8_t * buffer() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return a pointer to CPU memory. ...</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&#39;s metadata. </div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_aa459796b5489eca8a9160cb5dcf1a103"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#aa459796b5489eca8a9160cb5dcf1a103">arm_compute::ITensorInfo::element_size</a></div><div class="ttdeci">virtual size_t element_size() const =0</div><div class="ttdoc">Element size in bytes calculated as data_size() * num_channels() </div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader_xhtml_ab708c11aea0f419821cd053e3e7dae89"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader.xhtml#ab708c11aea0f419821cd053e3e7dae89">arm_compute::graph_utils::NumPyBinLoader::NumPyBinLoader</a></div><div class="ttdeci">NumPyBinLoader(std::string filename)</div><div class="ttdoc">Default Constructor. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00328">GraphUtils.cpp:328</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a07b929c34ad1dc823d8315876aa403ce"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a07b929c34ad1dc823d8315876aa403ce">arm_compute::ITensorInfo::padding</a></div><div class="ttdeci">virtual PaddingSize padding() const =0</div><div class="ttdoc">Padding of tensor. </div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">arm_compute::Format::S16</a></div><div class="ttdoc">1 channel, 1 S16 per channel </div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_top_n_predictions_accessor_xhtml_ad12f4e3c945ec4fad9ab6386954a3550"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_top_n_predictions_accessor.xhtml#ad12f4e3c945ec4fad9ab6386954a3550">arm_compute::graph_utils::TopNPredictionsAccessor::TopNPredictionsAccessor</a></div><div class="ttdeci">TopNPredictionsAccessor(const std::string &labels_path, size_t top_n=5, std::ostream &output_stream=std::cout)</div><div class="ttdoc">Constructor. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00141">GraphUtils.cpp:141</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_aadf5c9cff86327b96d88d04649d9715e"><div class="ttname"><a href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00701">Validate.h:701</a></div></div> |
| <div class="ttc" id="hwc_8hpp_xhtml_a0f61d63b009d0880a89c843bd50d8d76"><div class="ttname"><a href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a></div><div class="ttdeci">void * value</div><div class="ttdef"><b>Definition:</b> <a href="hwc_8hpp_source.xhtml#l00278">hwc.hpp:278</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_p_p_m_accessor_xhtml_ab469d593b4bc92e1d1132a03de0aedca"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_p_p_m_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">arm_compute::graph_utils::PPMAccessor::access_tensor</a></div><div class="ttdeci">bool access_tensor(ITensor &tensor) override</div><div class="ttdoc">Interface to be implemented to access a given tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00119">GraphUtils.cpp:119</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_ad0bd5cc32e7e4c0699eccba91e5de397"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#ad0bd5cc32e7e4c0699eccba91e5de397">arm_compute::ITensorInfo::offset_first_element_in_bytes</a></div><div class="ttdeci">virtual size_t offset_first_element_in_bytes() const =0</div><div class="ttdoc">The offset from the beginning of the memory allocation to the first element of the tensor...</div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_t_f_preproccessor_xhtml_a1545d087d050b8e9733e7e212df73666"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_t_f_preproccessor.xhtml#a1545d087d050b8e9733e7e212df73666">arm_compute::graph_utils::TFPreproccessor::preprocess</a></div><div class="ttdeci">void preprocess(ITensor &tensor) override</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00039">GraphUtils.cpp:39</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00039">GraphUtils.h:39</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a18064e0011c3869d884653e9e7c47b66"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a18064e0011c3869d884653e9e7c47b66">arm_compute::ITensorInfo::total_size</a></div><div class="ttdeci">virtual size_t total_size() const =0</div><div class="ttdoc">Returns the total size of the tensor in bytes. </div></div> |
| <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a0f59f175e7682c7ed5f4ea30ef687834"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a0f59f175e7682c7ed5f4ea30ef687834">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="core_2_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="core_2_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond,...)</div><div class="ttdef"><b>Definition:</b> <a href="core_2_error_8h_source.xhtml#l00297">Error.h:297</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1utils_1_1_p_p_m_loader_xhtml_a7c3f70e1caee95bb95c62346e130e5ab"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_p_p_m_loader.xhtml#a7c3f70e1caee95bb95c62346e130e5ab">arm_compute::utils::PPMLoader::fill_planar_tensor</a></div><div class="ttdeci">void fill_planar_tensor(T &tensor, bool bgr=false)</div><div class="ttdoc">Fill a tensor with 3 planes (one for each channel) with the content of the currently open PPM file...</div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00399">Utils.h:399</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_p_p_m_writer_xhtml_ab469d593b4bc92e1d1132a03de0aedca"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_p_p_m_writer.xhtml#ab469d593b4bc92e1d1132a03de0aedca">arm_compute::graph_utils::PPMWriter::access_tensor</a></div><div class="ttdeci">bool access_tensor(ITensor &tensor) override</div><div class="ttdoc">Interface to be implemented to access a given tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00079">GraphUtils.cpp:79</a></div></div> |
| <div class="ttc" id="runtime_2_sub_tensor_8h_xhtml"><div class="ttname"><a href="runtime_2_sub_tensor_8h.xhtml">SubTensor.h</a></div></div> |
| <div class="ttc" id="_open_c_l_8h_xhtml"><div class="ttname"><a href="_open_c_l_8h.xhtml">OpenCL.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_dummy_accessor_xhtml_ac5ae9597ba20e5581726743fe7c154b5"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml#ac5ae9597ba20e5581726743fe7c154b5">arm_compute::graph_utils::DummyAccessor::DummyAccessor</a></div><div class="ttdeci">DummyAccessor(unsigned int maximum=1)</div><div class="ttdoc">Constructor. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00094">GraphUtils.cpp:94</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_p_p_m_writer_xhtml_a9ea94bf5b7d00ddb836df1b8dcedb93a"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_p_p_m_writer.xhtml#a9ea94bf5b7d00ddb836df1b8dcedb93a">arm_compute::graph_utils::PPMWriter::PPMWriter</a></div><div class="ttdeci">PPMWriter(std::string name, unsigned int maximum=1)</div><div class="ttdoc">Constructor. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00074">GraphUtils.cpp:74</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_dummy_accessor_xhtml_ab469d593b4bc92e1d1132a03de0aedca"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">arm_compute::graph_utils::DummyAccessor::access_tensor</a></div><div class="ttdeci">bool access_tensor(ITensor &tensor) override</div><div class="ttdoc">Interface to be implemented to access a given tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00099">GraphUtils.cpp:99</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">arm_compute::DataType::F64</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1utils_1_1_p_p_m_loader_xhtml_a7b736ec9a05be5c498760d35a0406ed3"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_p_p_m_loader.xhtml#a7b736ec9a05be5c498760d35a0406ed3">arm_compute::utils::PPMLoader::width</a></div><div class="ttdeci">unsigned int width() const </div><div class="ttdoc">Return the width of the currently open PPM file. </div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00475">Utils.h:475</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7b8004eef325a40dd43eb80755610fff"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">arm_compute::test::validation::b</a></div><div class="ttdeci">CLTensor b</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00122">GEMM.cpp:122</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">arm_compute::DataType::U64</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">arm_compute::DataType::S8</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml">arm_compute::Window</a></div><div class="ttdoc">Describe a multidimensional execution window. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00039">Window.h:39</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1utils_1_1_p_p_m_loader_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_p_p_m_loader.xhtml">arm_compute::utils::PPMLoader</a></div><div class="ttdoc">Class to load the content of a PPM file into an Image. </div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00253">Utils.h:253</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_cbdb8362360e11eafe2fa3bc74cf0ffd.xhtml">utils</a></li><li class="navelem"><a class="el" href="_graph_utils_8cpp.xhtml">GraphUtils.cpp</a></li> |
| <li class="footer">Generated on Fri Mar 2 2018 12:37:55 for Compute Library by |
| <a href="http://www.doxygen.org/index.html"> |
| <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.11 </li> |
| </ul> |
| </div> |
| </body> |
| </html> |
