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120 <a href="_assets_library_8h.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> <span class="preprocessor">#ifndef __ARM_COMPUTE_TEST_TENSOR_LIBRARY_H__</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#define __ARM_COMPUTE_TEST_TENSOR_LIBRARY_H__</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="_coordinates_8h.xhtml">arm_compute/core/Coordinates.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_error_8h.xhtml">arm_compute/core/Error.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_helpers_8h.xhtml">arm_compute/core/Helpers.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="_tensor_info_8h.xhtml">arm_compute/core/TensorInfo.h</a>"</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "<a class="code" href="_tensor_shape_8h.xhtml">arm_compute/core/TensorShape.h</a>"</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_types_8h.xhtml">arm_compute/core/Types.h</a>"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include "<a class="code" href="_window_8h.xhtml">arm_compute/core/Window.h</a>"</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include "libnpy/npy.hpp"</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include "<a class="code" href="_raw_tensor_8h.xhtml">tests/RawTensor.h</a>"</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor">#include "<a class="code" href="_tensor_cache_8h.xhtml">tests/TensorCache.h</a>"</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#include "<a class="code" href="tests_2_utils_8h.xhtml">tests/Utils.h</a>"</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="preprocessor">#include "<a class="code" href="_exceptions_8h.xhtml">tests/framework/Exceptions.h</a>"</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="preprocessor">#include <algorithm></span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="preprocessor">#include <cstddef></span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="preprocessor">#include <fstream></span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="preprocessor">#include <random></span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="preprocessor">#include <string></span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="preprocessor">#include <type_traits></span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="preprocessor">#include <vector></span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> {</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="keyword">namespace </span>test</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> {</div><div class="line"><a name="l00058"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml"> 58</a></span> <span class="keyword">class </span><a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml">AssetsLibrary</a> final</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> <span class="keyword">public</span>:</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#adb53338108890e6b7354e16a1e9ae716">AssetsLibrary</a>(std::string <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9b67b266207227062c7a2961ef85293a">path</a>, std::random_device::result_type <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a4035a1140831801ced5dfa1d9fe6988a">seed</a>);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  std::string <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9b67b266207227062c7a2961ef85293a">path</a>() <span class="keyword">const</span>;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  std::random_device::result_type <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a4035a1140831801ced5dfa1d9fe6988a">seed</a>() <span class="keyword">const</span>;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#acc474b96886b5fd500460c7b25dc84fa">get_image_shape</a>(<span class="keyword">const</span> std::string &name);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> </div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<span class="keyword">get</span>(<span class="keyword">const</span> std::string &name) <span class="keyword">const</span>;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> </div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <span class="keyword">get</span>(<span class="keyword">const</span> std::string &name);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <span class="keyword">get</span>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ad7f431e3446fddcd9b6b9f93c4c14">data_type</a>, <span class="keywordtype">int</span> num_channels = 1) <span class="keyword">const</span>;</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<span class="keyword">get</span>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format) <span class="keyword">const</span>;</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> </div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <span class="keyword">get</span>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> </div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<span class="keyword">get</span>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <span class="keyword">get</span>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel);</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> </div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<span class="keyword">get</span>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> </div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <span class="keyword">get</span>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel);</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> </div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9a6d9b68649fb7444ca36b5c651dfdda">fill_borders_with_garbage</a>(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) <span class="keyword">const</span>;</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> </div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) <span class="keyword">const</span>;</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> </div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> D></div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw, D &&distribution, std::random_device::result_type seed_offset) <span class="keyword">const</span>;</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(T &&tensor, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format) <span class="keyword">const</span>;</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format) <span class="keyword">const</span>;</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> </div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(T &&tensor, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> </div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> </div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(T &&tensor, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> </div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> </div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(T &&tensor, <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> raw) <span class="keyword">const</span>;</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span> </div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#abb6f25295592e886976520216187eed7">fill_tensor_uniform</a>(T &&tensor, std::random_device::result_type seed_offset) <span class="keyword">const</span>;</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span> </div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#abb6f25295592e886976520216187eed7">fill_tensor_uniform</a>(T &&tensor, std::random_device::result_type seed_offset, D low, D high) <span class="keyword">const</span>;</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span> </div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ad85dc4c57a27c44d114c573b9a80bad6">fill_layer_data</a>(T &&tensor, std::string name) <span class="keyword">const</span>;</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> </div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a0457532b61fe412895684d12d08f5d0f">fill_tensor_value</a>(T &&tensor, D value) <span class="keyword">const</span>;</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> <span class="keyword">private</span>:</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="comment">// Function prototype to convert between image formats.</span></div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <span class="keyword">using</span> Converter = void (*)(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>);</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="comment">// Function prototype to extract a channel from an image.</span></div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <span class="keyword">using</span> Extractor = void (*)(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>);</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="comment">// Function prototype to load an image file.</span></div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="keyword">using</span> Loader = <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> (*)(<span class="keyword">const</span> std::string &<a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9b67b266207227062c7a2961ef85293a">path</a>);</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>  <span class="keyword">const</span> Converter &get_converter(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> src, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>) <span class="keyword">const</span>;</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="keyword">const</span> Converter &get_converter(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> src, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> dst) <span class="keyword">const</span>;</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="keyword">const</span> Converter &get_converter(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> src, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dst) <span class="keyword">const</span>;</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <span class="keyword">const</span> Converter &get_converter(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> src, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dst) <span class="keyword">const</span>;</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <span class="keyword">const</span> Extractor &get_extractor(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a>) <span class="keyword">const</span>;</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="keyword">const</span> Loader &get_loader(<span class="keyword">const</span> std::string &extension) <span class="keyword">const</span>;</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span> </div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> load_image(<span class="keyword">const</span> std::string &name) <span class="keyword">const</span>;</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> </div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &find_or_create_raw_tensor(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format) <span class="keyword">const</span>;</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span> </div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &find_or_create_raw_tensor(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span> </div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <span class="keyword">mutable</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_cache.xhtml">TensorCache</a> _cache{};</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <span class="keyword">mutable</span> std::mutex _format_lock{};</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="keyword">mutable</span> std::mutex _channel_lock{};</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keyword">const</span> std::string _library_path;</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  std::random_device::result_type _seed;</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span> };</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span> </div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00424"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9a6d9b68649fb7444ca36b5c651dfdda"> 424</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9a6d9b68649fb7444ca36b5c651dfdda">AssetsLibrary::fill_borders_with_garbage</a>(T &&tensor, D &&distribution, std::random_device::result_type seed_offset)<span class="keyword"> const</span></div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span> <span class="keyword"></span>{</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_border_size.xhtml">PaddingSize</a> padding_size = tensor.padding();</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span> </div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(0, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(-padding_size.<a class="code" href="structarm__compute_1_1_border_size.xhtml#a05374b750b0fc472c34ee61e6f028bba">left</a>, tensor.shape()[0] + padding_size.<a class="code" href="structarm__compute_1_1_border_size.xhtml#a78b0fed184c642b78f32fd34b228a5f9">right</a>, 1));</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="keywordflow">if</span>(tensor.shape().num_dimensions() > 1)</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  {</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(1, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(-padding_size.<a class="code" href="structarm__compute_1_1_border_size.xhtml#a3fdd42ea34070a54e696b3adc28c4be3">top</a>, tensor.shape()[1] + padding_size.<a class="code" href="structarm__compute_1_1_border_size.xhtml#a802ffcf1b49237efe5be8a314d3f3869">bottom</a>, 1));</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  }</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span> </div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  std::mt19937 gen(_seed);</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span> </div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <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="l00438"></a><span class="lineno"> 438</span>  {</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a> = tensor.shape();</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span> </div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="comment">// If outside of valid region</span></div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <span class="keywordflow">if</span>(<span class="keywordtype">id</span>.x() < 0 || <span class="keywordtype">id</span>.x() >= static_cast<int>(shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#afb5cd37bb08f1029691590372e6330f0">x</a>()) || <span class="keywordtype">id</span>.y() < 0 || <span class="keywordtype">id</span>.y() >= <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a691c9cb93365c2e33f3429de43244098">y</a>()))</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  {</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <span class="keyword">using</span> ResultType = <span class="keyword">typename</span> std::remove_reference<D>::type::result_type;</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <span class="keyword">const</span> ResultType value = distribution(gen);</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <span class="keywordtype">void</span> *<span class="keyword">const</span> out_ptr = tensor(<span class="keywordtype">id</span>);</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a1e6934e95738573214c2ce1d6648d116">store_value_with_data_type</a>(out_ptr, value, tensor.data_type());</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  }</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  });</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span> }</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span> </div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00453"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0"> 453</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">AssetsLibrary::fill</a>(T &&tensor, D &&distribution, std::random_device::result_type seed_offset)<span class="keyword"> const</span></div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span> <span class="keyword"></span>{</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keyword">using</span> ResultType = <span class="keyword">typename</span> std::remove_reference<D>::type::result_type;</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span> </div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  std::mt19937 gen(_seed + seed_offset);</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span> </div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_nhwc = tensor.data_layout() == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>(tensor.shape());</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span> </div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keywordflow">if</span>(is_nhwc)</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  {</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="comment">// Ensure that the equivalent tensors will be filled for both data layouts</span></div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <a class="code" href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">permute</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>, <a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">PermutationVector</a>(1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 2<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 0<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  }</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span> </div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <span class="comment">// Iterate over all elements</span></div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> element_idx = 0; element_idx < tensor.num_elements(); ++element_idx)</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  {</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> <span class="keywordtype">id</span> = <a class="code" href="namespacearm__compute_1_1test.xhtml#a24d8c0391cfa38e78969b6ad97c0ff09">index2coord</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>, element_idx);</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span> </div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <span class="keywordflow">if</span>(is_nhwc)</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  {</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <span class="comment">// Write in the correct id for permuted shapes</span></div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <a class="code" href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">permute</a>(<span class="keywordtype">id</span>, <a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">PermutationVector</a>(2<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 0<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>));</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  }</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span> </div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="comment">// Iterate over all channels</span></div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> channel = 0; channel < tensor.num_channels(); ++channel)</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  {</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <span class="keyword">const</span> ResultType value = distribution(gen);</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  ResultType &target_value = <span class="keyword">reinterpret_cast<</span>ResultType *const<span class="keyword">></span>(tensor(<span class="keywordtype">id</span>))[channel];</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span> </div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a1e6934e95738573214c2ce1d6648d116">store_value_with_data_type</a>(&target_value, value, tensor.data_type());</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  }</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  }</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span> </div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9a6d9b68649fb7444ca36b5c651dfdda">fill_borders_with_garbage</a>(tensor, distribution, seed_offset);</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span> }</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span> </div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span> <span class="keyword">template</span> <<span class="keyword">typename</span> D></div><div class="line"><a name="l00493"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a935d861fcffb0099b001498c494daaf6"> 493</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">AssetsLibrary::fill</a>(<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw, D &&distribution, std::random_device::result_type seed_offset)<span class="keyword"> const</span></div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span> <span class="keyword"></span>{</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  std::mt19937 gen(_seed + seed_offset);</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span> </div><div class="line"><a name="l00497"></a><span class="lineno"> 497</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> < raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#ad7701a09a964eab360a8e51fa7ad2c16">size</a>(); <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> += raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>())</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  {</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  <span class="keyword">using</span> ResultType = <span class="keyword">typename</span> std::remove_reference<D>::type::result_type;</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <span class="keyword">const</span> ResultType value = distribution(gen);</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a1e6934e95738573214c2ce1d6648d116">store_value_with_data_type</a>(raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a0c52a8f0085b55d907af7210ef2069d0">data</a>() + <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>, value, raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>());</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  }</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span> }</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span> </div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00506"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a5ee4fc10b84f941236df524f618b96c6"> 506</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">AssetsLibrary::fill</a>(T &&tensor, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format)<span class="keyword"> const</span></div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span> <span class="keyword"></span>{</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw = <span class="keyword">get</span>(name, format);</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span> </div><div class="line"><a name="l00510"></a><span class="lineno"> 510</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> < raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#ad7701a09a964eab360a8e51fa7ad2c16">size</a>(); <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> += raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>())</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  {</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> <span class="keywordtype">id</span> = <a class="code" href="namespacearm__compute_1_1test.xhtml#a24d8c0391cfa38e78969b6ad97c0ff09">index2coord</a>(raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>(), <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> / raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>());</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span> </div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#afb9ded5f49336ae503bb9f2035ea902b">RawTensor::value_type</a> *<span class="keyword">const</span> raw_ptr = raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a0c52a8f0085b55d907af7210ef2069d0">data</a>() + <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>;</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <span class="keyword">const</span> <span class="keyword">auto</span> out_ptr = <span class="keyword">static_cast<</span><a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#afb9ded5f49336ae503bb9f2035ea902b">RawTensor::value_type</a> *<span class="keyword">></span>(tensor(<span class="keywordtype">id</span>));</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  std::copy_n(raw_ptr, raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>(), out_ptr);</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  }</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> }</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span> </div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00521"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a4d049b9a5aba1f30b51f4bd36c3db076"> 521</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">AssetsLibrary::fill</a>(T &&tensor, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel)<span class="keyword"> const</span></div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span> <span class="keyword"></span>{</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(std::forward<T>(tensor), name, <a class="code" href="namespacearm__compute_1_1test.xhtml#aa337ab76176f3c4193642ac6de3a61cf">get_format_for_channel</a>(channel), channel);</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span> }</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span> </div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00527"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a36a9ddc6792949fb561cd788e6e31208"> 527</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">AssetsLibrary::fill</a>(T &&tensor, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel)<span class="keyword"> const</span></div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span> <span class="keyword"></span>{</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw = <span class="keyword">get</span>(name, format, channel);</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span> </div><div class="line"><a name="l00531"></a><span class="lineno"> 531</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> < raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#ad7701a09a964eab360a8e51fa7ad2c16">size</a>(); <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> += raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>())</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  {</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> <span class="keywordtype">id</span> = <a class="code" href="namespacearm__compute_1_1test.xhtml#a24d8c0391cfa38e78969b6ad97c0ff09">index2coord</a>(raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>(), <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> / raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>());</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span> </div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#afb9ded5f49336ae503bb9f2035ea902b">RawTensor::value_type</a> *<span class="keyword">const</span> raw_ptr = raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a0c52a8f0085b55d907af7210ef2069d0">data</a>() + <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>;</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  <span class="keyword">const</span> <span class="keyword">auto</span> out_ptr = <span class="keyword">static_cast<</span><a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#afb9ded5f49336ae503bb9f2035ea902b">RawTensor::value_type</a> *<span class="keyword">></span>(tensor(<span class="keywordtype">id</span>));</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  std::copy_n(raw_ptr, raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>(), out_ptr);</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  }</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span> }</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span> </div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00542"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a7eb916e4bf718e682ae89cb99aa348f9"> 542</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">AssetsLibrary::fill</a>(T &&tensor, <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> raw)<span class="keyword"> const</span></div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span> <span class="keyword"></span>{</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</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> < raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#ad7701a09a964eab360a8e51fa7ad2c16">size</a>(); <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> += raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>())</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  {</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> <span class="keywordtype">id</span> = <a class="code" href="namespacearm__compute_1_1test.xhtml#a24d8c0391cfa38e78969b6ad97c0ff09">index2coord</a>(raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>(), <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> / raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>());</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span> </div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#afb9ded5f49336ae503bb9f2035ea902b">RawTensor::value_type</a> *<span class="keyword">const</span> raw_ptr = raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a0c52a8f0085b55d907af7210ef2069d0">data</a>() + <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>;</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <span class="keyword">const</span> <span class="keyword">auto</span> out_ptr = <span class="keyword">static_cast<</span><a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#afb9ded5f49336ae503bb9f2035ea902b">RawTensor::value_type</a> *<span class="keyword">></span>(tensor(<span class="keywordtype">id</span>));</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  std::copy_n(raw_ptr, raw.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>(), out_ptr);</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  }</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span> }</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span> </div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00555"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#abb6f25295592e886976520216187eed7"> 555</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#abb6f25295592e886976520216187eed7">AssetsLibrary::fill_tensor_uniform</a>(T &&tensor, std::random_device::result_type seed_offset)<span class="keyword"> const</span></div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span> <span class="keyword"></span>{</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <span class="keywordflow">switch</span>(tensor.data_type())</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  {</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">DataType::U8</a>:</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>:</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  {</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  std::uniform_int_distribution<uint8_t> distribution_u8(std::numeric_limits<uint8_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<uint8_t>::max</a>());</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u8, seed_offset);</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  }</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">DataType::S8</a>:</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">DataType::QS8</a>:</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  {</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  std::uniform_int_distribution<int8_t> distribution_s8(std::numeric_limits<int8_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<int8_t>::max</a>());</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s8, seed_offset);</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  }</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">DataType::U16</a>:</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  {</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  std::uniform_int_distribution<uint16_t> distribution_u16(std::numeric_limits<uint16_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<uint16_t>::max</a>());</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u16, seed_offset);</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  }</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">DataType::S16</a>:</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9">DataType::QS16</a>:</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  {</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  std::uniform_int_distribution<int16_t> distribution_s16(std::numeric_limits<int16_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<int16_t>::max</a>());</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s16, seed_offset);</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  }</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">DataType::U32</a>:</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  {</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  std::uniform_int_distribution<uint32_t> distribution_u32(std::numeric_limits<uint32_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<uint32_t>::max</a>());</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u32, seed_offset);</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  }</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>:</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  {</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  std::uniform_int_distribution<int32_t> distribution_s32(std::numeric_limits<int32_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<int32_t>::max</a>());</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s32, seed_offset);</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  }</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">DataType::U64</a>:</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  {</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  std::uniform_int_distribution<uint64_t> distribution_u64(std::numeric_limits<uint64_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<uint64_t>::max</a>());</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u64, seed_offset);</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  }</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">DataType::S64</a>:</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  {</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  std::uniform_int_distribution<int64_t> distribution_s64(std::numeric_limits<int64_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<int64_t>::max</a>());</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s64, seed_offset);</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  }</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  {</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  <span class="comment">// It doesn't make sense to check [-inf, inf], so hard code it to a big number</span></div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  std::uniform_real_distribution<float> distribution_f16(-100.f, 100.f);</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f16, seed_offset);</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  }</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  {</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <span class="comment">// It doesn't make sense to check [-inf, inf], so hard code it to a big number</span></div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  std::uniform_real_distribution<float> distribution_f32(-1000.f, 1000.f);</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f32, seed_offset);</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  }</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">DataType::F64</a>:</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  {</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <span class="comment">// It doesn't make sense to check [-inf, inf], so hard code it to a big number</span></div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  std::uniform_real_distribution<double> distribution_f64(-1000.f, 1000.f);</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f64, seed_offset);</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  }</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6abd7ef6d4f35bc7d05c559b65032f15d1">DataType::SIZET</a>:</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  {</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  std::uniform_int_distribution<size_t> distribution_sizet(std::numeric_limits<size_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<size_t>::max</a>());</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_sizet, seed_offset);</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  }</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"NOT SUPPORTED!"</span>);</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  }</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span> }</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span> </div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00643"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a150d2023c197e197f09f350ede795085"> 643</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#abb6f25295592e886976520216187eed7">AssetsLibrary::fill_tensor_uniform</a>(T &&tensor, std::random_device::result_type seed_offset, D low, D high)<span class="keyword"> const</span></div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span> <span class="keyword"></span>{</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  <span class="keywordflow">switch</span>(tensor.data_type())</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  {</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">DataType::U8</a>:</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>:</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  {</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same<uint8_t, D>::value));</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  std::uniform_int_distribution<uint8_t> distribution_u8(low, high);</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u8, seed_offset);</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  }</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">DataType::S8</a>:</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">DataType::QS8</a>:</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  {</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same<int8_t, D>::value));</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  std::uniform_int_distribution<int8_t> distribution_s8(low, high);</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s8, seed_offset);</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  }</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">DataType::U16</a>:</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  {</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same<uint16_t, D>::value));</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  std::uniform_int_distribution<uint16_t> distribution_u16(low, high);</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u16, seed_offset);</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  }</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">DataType::S16</a>:</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9">DataType::QS16</a>:</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  {</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same<int16_t, D>::value));</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  std::uniform_int_distribution<int16_t> distribution_s16(low, high);</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s16, seed_offset);</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  }</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">DataType::U32</a>:</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  {</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same<uint32_t, D>::value));</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  std::uniform_int_distribution<uint32_t> distribution_u32(low, high);</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u32, seed_offset);</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  }</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>:</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  {</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same<int32_t, D>::value));</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  std::uniform_int_distribution<int32_t> distribution_s32(low, high);</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s32, seed_offset);</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  }</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">DataType::U64</a>:</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  {</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same<uint64_t, D>::value));</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  std::uniform_int_distribution<uint64_t> distribution_u64(low, high);</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u64, seed_offset);</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  }</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">DataType::S64</a>:</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  {</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same<int64_t, D>::value));</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  std::uniform_int_distribution<int64_t> distribution_s64(low, high);</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s64, seed_offset);</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  }</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  {</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  std::uniform_real_distribution<float> distribution_f16(low, high);</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f16, seed_offset);</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  }</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  {</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same<float, D>::value));</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  std::uniform_real_distribution<float> distribution_f32(low, high);</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f32, seed_offset);</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  }</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">DataType::F64</a>:</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  {</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same<double, D>::value));</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  std::uniform_real_distribution<double> distribution_f64(low, high);</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f64, seed_offset);</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  }</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6abd7ef6d4f35bc7d05c559b65032f15d1">DataType::SIZET</a>:</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  {</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same<size_t, D>::value));</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  std::uniform_int_distribution<size_t> distribution_sizet(low, high);</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_sizet, seed_offset);</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  }</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"NOT SUPPORTED!"</span>);</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  }</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span> }</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span> </div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00739"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ad85dc4c57a27c44d114c573b9a80bad6"> 739</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ad85dc4c57a27c44d114c573b9a80bad6">AssetsLibrary::fill_layer_data</a>(T &&tensor, std::string name)<span class="keyword"> const</span></div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span> <span class="keyword"></span>{</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span> <span class="preprocessor">#ifdef _WIN32</span></div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  <span class="keyword">const</span> std::string path_separator(<span class="stringliteral">"\\"</span>);</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span> <span class="preprocessor">#else </span><span class="comment">/* _WIN32 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  <span class="keyword">const</span> std::string path_separator(<span class="stringliteral">"/"</span>);</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span> <span class="preprocessor">#endif </span><span class="comment">/* _WIN32 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  <span class="keyword">const</span> std::string <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9b67b266207227062c7a2961ef85293a">path</a> = _library_path + path_separator + name;</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span> </div><div class="line"><a name="l00748"></a><span class="lineno"> 748</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="l00749"></a><span class="lineno"> 749</span> </div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <span class="comment">// Open file</span></div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  std::ifstream stream(path, std::ios::in | std::ios::binary);</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  <span class="keywordflow">if</span>(!stream.good())</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  {</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  <span class="keywordflow">throw</span> <a class="code" href="classarm__compute_1_1test_1_1framework_1_1_file_not_found.xhtml">framework::FileNotFound</a>(<span class="stringliteral">"Could not load npy file: "</span> + path);</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  }</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  std::string header = npy::read_header(stream);</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span> </div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  <span class="comment">// Parse header</span></div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  <span class="keywordtype">bool</span> fortran_order = <span class="keyword">false</span>;</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  std::string typestr;</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  npy::parse_header(header, typestr, fortran_order, shape);</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span> </div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  <span class="comment">// Check if the typestring matches the given one</span></div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  std::string expect_typestr = <a class="code" href="namespacearm__compute_1_1test.xhtml#ae47155d6186155ec4da9295764b3c05a">get_typestring</a>(tensor.data_type());</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  <a class="code" href="_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="l00766"></a><span class="lineno"> 766</span> </div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  <span class="comment">// Validate tensor shape</span></div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(shape.size() != tensor.shape().num_dimensions(), <span class="stringliteral">"Tensor ranks mismatch"</span>);</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  <span class="keywordflow">if</span>(fortran_order)</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  {</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i = 0; i < shape.size(); ++i)</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  {</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  <a class="code" href="_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="l00774"></a><span class="lineno"> 774</span>  }</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  }</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  {</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i = 0; i < shape.size(); ++i)</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  {</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(tensor.shape()[i] != shape[shape.size() - i - 1], <span class="stringliteral">"Tensor dimensions mismatch"</span>);</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  }</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  }</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span> </div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  <span class="comment">// Read data</span></div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  <span class="keywordflow">if</span>(tensor.padding().empty())</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  {</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  <span class="comment">// If tensor has no padding read directly from stream.</span></div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  stream.read(reinterpret_cast<char *>(tensor.data()), tensor.size());</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  }</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  {</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  <span class="comment">// If tensor has padding accessing tensor elements through execution window.</span></div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</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="l00795"></a><span class="lineno"> 795</span> </div><div class="line"><a name="l00796"></a><span class="lineno"> 796</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="l00797"></a><span class="lineno"> 797</span>  {</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  stream.read(reinterpret_cast<char *>(tensor(<span class="keywordtype">id</span>)), tensor.element_size());</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  });</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  }</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span> }</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span> </div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00804"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a0457532b61fe412895684d12d08f5d0f"> 804</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a0457532b61fe412895684d12d08f5d0f">AssetsLibrary::fill_tensor_value</a>(T &&tensor, D value)<span class="keyword"> const</span></div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span> <span class="keyword"></span>{</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#abb6f25295592e886976520216187eed7">fill_tensor_uniform</a>(tensor, 0, value, value);</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span> }</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span> } <span class="comment">// namespace test</span></div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span> } <span class="comment">// namespace arm_compute</span></div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span> <span class="preprocessor">#endif </span><span class="comment">/* __ARM_COMPUTE_TEST_TENSOR_LIBRARY_H__ */</span><span class="preprocessor"></span></div><div class="ttc" id="_error_8h_xhtml_a05b19c75afe9c24200a62b9724734bbd"><div class="ttname"><a href="_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="_error_8h_source.xhtml#l00260">Error.h:260</a></div></div>
121 <div class="ttc" id="structarm__compute_1_1_border_size_xhtml_a3fdd42ea34070a54e696b3adc28c4be3"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml#a3fdd42ea34070a54e696b3adc28c4be3">arm_compute::BorderSize::top</a></div><div class="ttdeci">unsigned int top</div><div class="ttdoc">top of the border </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00371">Types.h:371</a></div></div>
122 <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9">arm_compute::DataType::QS16</a></div><div class="ttdoc">quantized, symmetric fixed-point 16-bit number </div></div>
123 <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>
124 <div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a62b67b578f684c4d516843c9dea86a23"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a62b67b578f684c4d516843c9dea86a23">arm_compute::test::SimpleTensor::element_size</a></div><div class="ttdeci">size_t element_size() const override</div><div class="ttdoc">Size of each element in the tensor in bytes. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00300">SimpleTensor.h:300</a></div></div>
125 <div class="ttc" id="classarm__compute_1_1test_1_1_raw_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">arm_compute::test::RawTensor</a></div><div class="ttdoc">Subclass of SimpleTensor using uint8_t as value type. </div><div class="ttdef"><b>Definition:</b> <a href="_raw_tensor_8h_source.xhtml#l00038">RawTensor.h:38</a></div></div>
126 <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_abb6f25295592e886976520216187eed7"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#abb6f25295592e886976520216187eed7">arm_compute::test::AssetsLibrary::fill_tensor_uniform</a></div><div class="ttdeci">void fill_tensor_uniform(T &&tensor, std::random_device::result_type seed_offset) const </div><div class="ttdoc">Fill a tensor with uniform distribution across the range of its type. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8h_source.xhtml#l00555">AssetsLibrary.h:555</a></div></div>
127 <div class="ttc" id="classarm__compute_1_1test_1_1_tensor_cache_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_tensor_cache.xhtml">arm_compute::test::TensorCache</a></div><div class="ttdoc">Stores RawTensor categorised by the image they are created from including name, format and channel...</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_cache_8h_source.xhtml#l00040">TensorCache.h:40</a></div></div>
128 <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>
129 <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_a80a7b5ae084bf22b91bc5f68a06711c0"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">arm_compute::test::AssetsLibrary::fill</a></div><div class="ttdeci">void fill(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const </div><div class="ttdoc">Fills the specified tensor with random values drawn from distribution. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8h_source.xhtml#l00453">AssetsLibrary.h:453</a></div></div>
130 <div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_ad7701a09a964eab360a8e51fa7ad2c16"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#ad7701a09a964eab360a8e51fa7ad2c16">arm_compute::test::SimpleTensor::size</a></div><div class="ttdeci">size_t size() const override</div><div class="ttdoc">Total size of the tensor in bytes. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00318">SimpleTensor.h:318</a></div></div>
131 <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">arm_compute::DataType::QS8</a></div><div class="ttdoc">quantized, symmetric fixed-point 8-bit number </div></div>
132 <div class="ttc" id="structarm__compute_1_1_border_size_xhtml"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml">arm_compute::BorderSize</a></div><div class="ttdoc">Container for 2D border size. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00291">Types.h:291</a></div></div>
133 <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml">arm_compute::test::AssetsLibrary</a></div><div class="ttdoc">Factory class to create and fill tensors. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8h_source.xhtml#l00058">AssetsLibrary.h:58</a></div></div>
134 <div class="ttc" id="namespacearm__compute_1_1test_xhtml_aa337ab76176f3c4193642ac6de3a61cf"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#aa337ab76176f3c4193642ac6de3a61cf">arm_compute::test::get_format_for_channel</a></div><div class="ttdeci">Format get_format_for_channel(Channel channel)</div><div class="ttdoc">Look up the format corresponding to a channel. </div><div class="ttdef"><b>Definition:</b> <a href="tests_2_utils_8h_source.xhtml#l00148">Utils.h:148</a></div></div>
135 <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>
136 <div class="ttc" id="_window_8h_xhtml"><div class="ttname"><a href="_window_8h.xhtml">Window.h</a></div></div>
137 <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>
138 <div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a9a3e72153aeb3ed212e9c3698774e881"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">arm_compute::test::SimpleTensor::data_type</a></div><div class="ttdeci">DataType data_type() const override</div><div class="ttdoc">Data type of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00337">SimpleTensor.h:337</a></div></div>
139 <div class="ttc" id="namespacearm__compute_xhtml_a33e65be485104e2e9e69fca551d6f492"><div class="ttname"><a href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">arm_compute::PermutationVector</a></div><div class="ttdeci">Strides PermutationVector</div><div class="ttdoc">Permutation vector. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00047">Types.h:47</a></div></div>
140 <div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00328">Error.h:328</a></div></div>
141 <div class="ttc" id="classarm__compute_1_1_window_1_1_dimension_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window_1_1_dimension.xhtml">arm_compute::Window::Dimension</a></div><div class="ttdoc">Describe one of the image&#39;s dimensions with a start, end and step. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00068">Window.h:68</a></div></div>
142 <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>
143 <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_a4035a1140831801ced5dfa1d9fe6988a"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#a4035a1140831801ced5dfa1d9fe6988a">arm_compute::test::AssetsLibrary::seed</a></div><div class="ttdeci">std::random_device::result_type seed() const </div><div class="ttdoc">Seed that is used to fill tensors with random values. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8cpp_source.xhtml#l00260">AssetsLibrary.cpp:260</a></div></div>
144 <div class="ttc" id="structarm__compute_1_1_border_size_xhtml_a802ffcf1b49237efe5be8a314d3f3869"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml#a802ffcf1b49237efe5be8a314d3f3869">arm_compute::BorderSize::bottom</a></div><div class="ttdeci">unsigned int bottom</div><div class="ttdoc">bottom of the border </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00373">Types.h:373</a></div></div>
145 <div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_aba5871b3e4a65d057ec1c28fce8b00ba"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">arm_compute::test::SimpleTensor::shape</a></div><div class="ttdeci">TensorShape shape() const override</div><div class="ttdoc">Shape of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00294">SimpleTensor.h:294</a></div></div>
146 <div class="ttc" id="_tensor_info_8h_xhtml"><div class="ttname"><a href="_tensor_info_8h.xhtml">TensorInfo.h</a></div></div>
147 <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#l00303">helpers.h:303</a></div></div>
148 <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_acc474b96886b5fd500460c7b25dc84fa"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#acc474b96886b5fd500460c7b25dc84fa">arm_compute::test::AssetsLibrary::get_image_shape</a></div><div class="ttdeci">TensorShape get_image_shape(const std::string &name)</div><div class="ttdoc">Provides a tensor shape for the specified image. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8cpp_source.xhtml#l00458">AssetsLibrary.cpp:458</a></div></div>
149 <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>
150 <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">This file contains all available output stages for GEMMLowp on OpenCL. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00001">00_introduction.dox:1</a></div></div>
151 <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_a9b67b266207227062c7a2961ef85293a"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9b67b266207227062c7a2961ef85293a">arm_compute::test::AssetsLibrary::path</a></div><div class="ttdeci">std::string path() const </div><div class="ttdoc">Path to assets directory used to initialise library. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8cpp_source.xhtml#l00255">AssetsLibrary.cpp:255</a></div></div>
152 <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>
153 <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_a9a6d9b68649fb7444ca36b5c651dfdda"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#a9a6d9b68649fb7444ca36b5c651dfdda">arm_compute::test::AssetsLibrary::fill_borders_with_garbage</a></div><div class="ttdeci">void fill_borders_with_garbage(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const </div><div class="ttdoc">Puts garbage values all around the tensor for testing purposes. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8h_source.xhtml#l00424">AssetsLibrary.h:424</a></div></div>
154 <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_afb5cd37bb08f1029691590372e6330f0"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#afb5cd37bb08f1029691590372e6330f0">arm_compute::Dimensions::x</a></div><div class="ttdeci">T x() const </div><div class="ttdoc">Alias to access the size of the first dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00081">Dimensions.h:81</a></div></div>
155 <div class="ttc" id="namespacearm__compute_xhtml_a21c3e11887f3acf9284ca763372c7da0"><div class="ttname"><a href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">arm_compute::permute</a></div><div class="ttdeci">void permute(Dimensions< T > &dimensions, const PermutationVector &perm)</div><div class="ttdoc">Permutes given Dimensions according to a permutation vector. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_helpers_8h_source.xhtml#l00536">Helpers.h:536</a></div></div>
156 <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>
157 <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">arm_compute::DataType::S64</a></div><div class="ttdoc">signed 64-bit number </div></div>
158 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ac2ad7f431e3446fddcd9b6b9f93c4c14"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ad7f431e3446fddcd9b6b9f93c4c14">arm_compute::test::validation::data_type</a></div><div class="ttdeci">data_type</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00116">GEMM.cpp:116</a></div></div>
159 <div class="ttc" id="namespacearm__compute_1_1test_xhtml_a1e6934e95738573214c2ce1d6648d116"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a1e6934e95738573214c2ce1d6648d116">arm_compute::test::store_value_with_data_type</a></div><div class="ttdeci">void store_value_with_data_type(void *ptr, T value, DataType data_type)</div><div class="ttdoc">Write the value after casting the pointer according to data_type. </div><div class="ttdef"><b>Definition:</b> <a href="tests_2_utils_8h_source.xhtml#l00318">Utils.h:318</a></div></div>
160 <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6abd7ef6d4f35bc7d05c559b65032f15d1"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6abd7ef6d4f35bc7d05c559b65032f15d1">arm_compute::DataType::SIZET</a></div><div class="ttdoc">size_t </div></div>
161 <div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a0c52a8f0085b55d907af7210ef2069d0"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a0c52a8f0085b55d907af7210ef2069d0">arm_compute::test::SimpleTensor::data</a></div><div class="ttdeci">const T * data() const </div><div class="ttdoc">Constant pointer to the underlying buffer. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00398">SimpleTensor.h:398</a></div></div>
162 <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>
163 <div class="ttc" id="namespacearm__compute_xhtml_a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">arm_compute::Channel::U</a></div><div class="ttdoc">Cb/U channel. </div></div>
164 <div class="ttc" id="namespacearm__compute_xhtml_a1ce9b523fd4f3b5bbcadcd796183455a"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">arm_compute::Channel</a></div><div class="ttdeci">Channel</div><div class="ttdoc">Available channels. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00481">Types.h:481</a></div></div>
165 <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">arm_compute::Format</a></div><div class="ttdeci">Format</div><div class="ttdoc">Image colour formats. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00050">Types.h:50</a></div></div>
166 <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>
167 <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::DataType::QASYMM8</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number </div></div>
168 <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>
169 <div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_afb9ded5f49336ae503bb9f2035ea902b"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#afb9ded5f49336ae503bb9f2035ea902b">arm_compute::test::SimpleTensor< uint8_t >::value_type</a></div><div class="ttdeci">uint8_t value_type</div><div class="ttdoc">Tensor value type. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00107">SimpleTensor.h:107</a></div></div>
170 <div class="ttc" id="classarm__compute_1_1test_1_1framework_1_1_file_not_found_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1framework_1_1_file_not_found.xhtml">arm_compute::test::framework::FileNotFound</a></div><div class="ttdoc">Error class for when some external assets are missing. </div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8h_source.xhtml#l00067">Exceptions.h:67</a></div></div>
171 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_adbf67dcee294e673cf796f1ed8aeb6a4"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">arm_compute::test::validation::dst</a></div><div class="ttdeci">CLTensor dst</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_8cpp_source.xhtml#l00137">Convolution.cpp:137</a></div></div>
172 <div class="ttc" id="classarm__compute_1_1_window_xhtml_acd3d2bba51cb84d34dd7656ad2375a6e"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">arm_compute::Window::set</a></div><div class="ttdeci">void set(size_t dimension, const Dimension &dim)</div><div class="ttdoc">Set the values of a given dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00041">Window.inl:41</a></div></div>
173 <div class="ttc" id="_error_8h_xhtml"><div class="ttname"><a href="_error_8h.xhtml">Error.h</a></div></div>
174 <div class="ttc" id="namespacearm__compute_1_1test_xhtml_a24d8c0391cfa38e78969b6ad97c0ff09"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a24d8c0391cfa38e78969b6ad97c0ff09">arm_compute::test::index2coord</a></div><div class="ttdeci">Coordinates index2coord(const TensorShape &shape, int index)</div><div class="ttdoc">Convert a linear index into n-dimensional coordinates. </div><div class="ttdef"><b>Definition:</b> <a href="tests_2_utils_8h_source.xhtml#l00419">Utils.h:419</a></div></div>
175 <div class="ttc" id="structarm__compute_1_1_border_size_xhtml_a05374b750b0fc472c34ee61e6f028bba"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml#a05374b750b0fc472c34ee61e6f028bba">arm_compute::BorderSize::left</a></div><div class="ttdeci">unsigned int left</div><div class="ttdoc">left of the border </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00374">Types.h:374</a></div></div>
176 <div class="ttc" id="_tensor_cache_8h_xhtml"><div class="ttname"><a href="_tensor_cache_8h.xhtml">TensorCache.h</a></div></div>
177 <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_a0457532b61fe412895684d12d08f5d0f"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#a0457532b61fe412895684d12d08f5d0f">arm_compute::test::AssetsLibrary::fill_tensor_value</a></div><div class="ttdeci">void fill_tensor_value(T &&tensor, D value) const </div><div class="ttdoc">Fill a tensor with a constant value. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8h_source.xhtml#l00804">AssetsLibrary.h:804</a></div></div>
178 <div class="ttc" id="structarm__compute_1_1_border_size_xhtml_a78b0fed184c642b78f32fd34b228a5f9"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml#a78b0fed184c642b78f32fd34b228a5f9">arm_compute::BorderSize::right</a></div><div class="ttdeci">unsigned int right</div><div class="ttdoc">right of the border </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00372">Types.h:372</a></div></div>
179 <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>
180 <div class="ttc" id="namespacearm__compute_1_1test_xhtml_ae47155d6186155ec4da9295764b3c05a"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#ae47155d6186155ec4da9295764b3c05a">arm_compute::test::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="tests_2_utils_8h_source.xhtml#l00688">Utils.h:688</a></div></div>
181 <div class="ttc" id="tests_2_utils_8h_xhtml"><div class="ttname"><a href="tests_2_utils_8h.xhtml">Utils.h</a></div></div>
182 <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_ad85dc4c57a27c44d114c573b9a80bad6"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#ad85dc4c57a27c44d114c573b9a80bad6">arm_compute::test::AssetsLibrary::fill_layer_data</a></div><div class="ttdeci">void fill_layer_data(T &&tensor, std::string name) const </div><div class="ttdoc">Fills the specified tensor with data loaded from .npy (numpy binary) in specified path...</div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8h_source.xhtml#l00739">AssetsLibrary.h:739</a></div></div>
183 <div class="ttc" id="_coordinates_8h_xhtml"><div class="ttname"><a href="_coordinates_8h.xhtml">Coordinates.h</a></div></div>
184 <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a691c9cb93365c2e33f3429de43244098"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a691c9cb93365c2e33f3429de43244098">arm_compute::Dimensions::y</a></div><div class="ttdeci">T y() const </div><div class="ttdoc">Alias to access the size of the second dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00086">Dimensions.h:86</a></div></div>
185 <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">arm_compute::DataLayout::NHWC</a></div><div class="ttdoc">Num samples, height, width, channels. </div></div>
186 <div class="ttc" id="_exceptions_8h_xhtml"><div class="ttname"><a href="_exceptions_8h.xhtml">Exceptions.h</a></div></div>
187 <div class="ttc" id="arm__compute_2core_2_helpers_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_helpers_8h.xhtml">Helpers.h</a></div></div>
188 <div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_ad91bb73431b4de1f4946ed949d444849"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">arm_compute::test::fixed_point_arithmetic::detail::max</a></div><div class="ttdeci">fixed_point< T > max(fixed_point< T > x, fixed_point< T > y)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00902">FixedPoint.h:902</a></div></div>
189 <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">arm_compute::DataType::F64</a></div><div class="ttdoc">64-bit floating-point number </div></div>
190 <div class="ttc" id="_raw_tensor_8h_xhtml"><div class="ttname"><a href="_raw_tensor_8h.xhtml">RawTensor.h</a></div></div>
191 <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">arm_compute::DataType::U64</a></div><div class="ttdoc">unsigned 64-bit number </div></div>
192 <div class="ttc" id="arm__compute_2core_2_types_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_types_8h.xhtml">Types.h</a></div></div>
193 <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdoc">Available data types. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00072">Types.h:72</a></div></div>
194 <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">arm_compute::DataType::S8</a></div><div class="ttdoc">signed 8-bit number </div></div>
195 <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_adb53338108890e6b7354e16a1e9ae716"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#adb53338108890e6b7354e16a1e9ae716">arm_compute::test::AssetsLibrary::AssetsLibrary</a></div><div class="ttdeci">AssetsLibrary(std::string path, std::random_device::result_type seed)</div><div class="ttdoc">Initialises the library with a path to the assets directory. </div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8cpp_source.xhtml#l00249">AssetsLibrary.cpp:249</a></div></div>
196 <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>
197 <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a6743f0a130e8311e6f5b1a23df102472"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">arm_compute::test::validation::src</a></div><div class="ttdeci">convolution configure & src</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_8cpp_source.xhtml#l00147">Convolution.cpp:147</a></div></div>
198 <div class="ttc" id="_tensor_shape_8h_xhtml"><div class="ttname"><a href="_tensor_shape_8h.xhtml">TensorShape.h</a></div></div>
199 <div class="ttc" id="_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="_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="_error_8h_source.xhtml#l00319">Error.h:319</a></div></div>
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