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| <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) 2017-2019 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 "<a class="code" href="_random_8h.xhtml">arm_compute/core/utils/misc/Random.h</a>"</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include "libnpy/npy.hpp"</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor">#include "<a class="code" href="_raw_tensor_8h.xhtml">tests/RawTensor.h</a>"</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#include "<a class="code" href="_tensor_cache_8h.xhtml">tests/TensorCache.h</a>"</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</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="l00039"></a><span class="lineno"> 39</span> <span class="preprocessor">#include "<a class="code" href="_exceptions_8h.xhtml">tests/framework/Exceptions.h</a>"</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="preprocessor">#include <algorithm></span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="preprocessor">#include <cstddef></span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="preprocessor">#include <fstream></span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="preprocessor">#include <random></span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="preprocessor">#include <string></span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="preprocessor">#include <type_traits></span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="preprocessor">#include <vector></span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> {</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="keyword">namespace </span>test</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> {</div><div class="line"><a name="l00059"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml"> 59</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="l00060"></a><span class="lineno"> 60</span> {</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00062"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a51733f705cc27b63c4be127eb50639c4"> 62</a></span>  <span class="keyword">using</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a51733f705cc27b63c4be127eb50639c4">RangePair</a> = std::pair<float, float>;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</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#ac4cb5f95f1d720ef0cc94b74152cf50b">path</a>, std::random_device::result_type <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a0a20fa200643e1e3aa4004375d9188f1">seed</a>);</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  std::string <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac4cb5f95f1d720ef0cc94b74152cf50b">path</a>() <span class="keyword">const</span>;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  std::random_device::result_type <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a0a20fa200643e1e3aa4004375d9188f1">seed</a>() <span class="keyword">const</span>;</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</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="l00092"></a><span class="lineno"> 92</span> </div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#aa6db5c08c0540fb3c0b8292861342a73">get</a>(<span class="keyword">const</span> std::string &name) <span class="keyword">const</span>;</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> </div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#aa6db5c08c0540fb3c0b8292861342a73">get</a>(<span class="keyword">const</span> std::string &name);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#aa6db5c08c0540fb3c0b8292861342a73">get</a>(<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="l00119"></a><span class="lineno"> 119</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#aa6db5c08c0540fb3c0b8292861342a73">get</a>(<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="l00129"></a><span class="lineno"> 129</span> </div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#aa6db5c08c0540fb3c0b8292861342a73">get</a>(<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="l00139"></a><span class="lineno"> 139</span> </div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#aa6db5c08c0540fb3c0b8292861342a73">get</a>(<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="l00152"></a><span class="lineno"> 152</span> </div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#aa6db5c08c0540fb3c0b8292861342a73">get</a>(<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="l00165"></a><span class="lineno"> 165</span> </div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#aa6db5c08c0540fb3c0b8292861342a73">get</a>(<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="l00176"></a><span class="lineno"> 176</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#aa6db5c08c0540fb3c0b8292861342a73">get</a>(<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="l00187"></a><span class="lineno"> 187</span> </div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ae139c07ceb794ec059efb92aa4c6fd9d">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="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) <span class="keyword">const</span>;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> </div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a6327330393eee13d12234267ab5d19d4">fill_boxes</a>(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) <span class="keyword">const</span>;</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> </div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> D></div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">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="l00225"></a><span class="lineno"> 225</span> </div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">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="l00238"></a><span class="lineno"> 238</span> </div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">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="l00250"></a><span class="lineno"> 250</span> </div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">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="l00266"></a><span class="lineno"> 266</span> </div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">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="l00281"></a><span class="lineno"> 281</span> </div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">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="l00296"></a><span class="lineno"> 296</span> </div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">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="l00310"></a><span class="lineno"> 310</span> </div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">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="l00321"></a><span class="lineno"> 321</span> </div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a6bdf347bae60f8b5b4303776cfc48d68">fill_tensor_uniform</a>(T &&tensor, std::random_device::result_type seed_offset) <span class="keyword">const</span>;</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span> </div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a6bdf347bae60f8b5b4303776cfc48d68">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="l00341"></a><span class="lineno"> 341</span> </div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a7fd005cb98921bb25824629d659ea79d">fill_tensor_uniform_ranged</a>(T &&tensor,</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  std::random_device::result_type seed_offset,</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="keyword">const</span> std::vector<AssetsLibrary::RangePair> &excluded_range_pairs) <span class="keyword">const</span>;</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span> </div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a5580270336a3055bb7477b227563ccf0">fill_layer_data</a>(T &&tensor, std::string name) <span class="keyword">const</span>;</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span> </div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#aa13fcfba9d7f0433db83255bd1f0638a">fill_tensor_value</a>(T &&tensor, D value) <span class="keyword">const</span>;</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span> </div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <span class="comment">// Function prototype to convert between image formats.</span></div><div class="line"><a name="l00385"></a><span class="lineno"> 385</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#ae9d2dc29c2789c253406f9b304cc75a8">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="l00386"></a><span class="lineno"> 386</span>  <span class="comment">// Function prototype to extract a channel from an image.</span></div><div class="line"><a name="l00387"></a><span class="lineno"> 387</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#ae9d2dc29c2789c253406f9b304cc75a8">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="l00388"></a><span class="lineno"> 388</span>  <span class="comment">// Function prototype to load an image file.</span></div><div class="line"><a name="l00389"></a><span class="lineno"> 389</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#ac4cb5f95f1d720ef0cc94b74152cf50b">path</a>);</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> </div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="keyword">const</span> Converter &get_converter(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae9d2dc29c2789c253406f9b304cc75a8">src</a>, <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="l00392"></a><span class="lineno"> 392</span>  <span class="keyword">const</span> Converter &get_converter(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae9d2dc29c2789c253406f9b304cc75a8">src</a>, <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="l00393"></a><span class="lineno"> 393</span>  <span class="keyword">const</span> Converter &get_converter(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae9d2dc29c2789c253406f9b304cc75a8">src</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</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="l00394"></a><span class="lineno"> 394</span>  <span class="keyword">const</span> Converter &get_converter(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae9d2dc29c2789c253406f9b304cc75a8">src</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</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="l00395"></a><span class="lineno"> 395</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="l00396"></a><span class="lineno"> 396</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="l00397"></a><span class="lineno"> 397</span> </div><div class="line"><a name="l00405"></a><span class="lineno"> 405</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="l00406"></a><span class="lineno"> 406</span> </div><div class="line"><a name="l00418"></a><span class="lineno"> 418</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="l00419"></a><span class="lineno"> 419</span> </div><div class="line"><a name="l00432"></a><span class="lineno"> 432</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="l00433"></a><span class="lineno"> 433</span> </div><div class="line"><a name="l00434"></a><span class="lineno"> 434</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="l00435"></a><span class="lineno"> 435</span>  <span class="keyword">mutable</span> <a class="code" href="namespacearm__compute.xhtml#acded863dbfdd730829d4188d67eefcf0">arm_compute::Mutex</a> _format_lock{};</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <span class="keyword">mutable</span> <a class="code" href="namespacearm__compute.xhtml#acded863dbfdd730829d4188d67eefcf0">arm_compute::Mutex</a> _channel_lock{};</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <span class="keyword">const</span> std::string _library_path;</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  std::random_device::result_type _seed;</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span> };</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span> </div><div class="line"><a name="l00441"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1detail.xhtml"> 441</a></span> <span class="keyword">namespace </span>detail</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span> {</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00444"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1detail.xhtml#a8f88ff91d07f88724302b134e4cd7eac"> 444</a></span> <span class="keyword">inline</span> std::vector<std::pair<T, T>> <a class="code" href="namespacearm__compute_1_1test_1_1detail.xhtml#a8f88ff91d07f88724302b134e4cd7eac">convert_range_pair</a>(<span class="keyword">const</span> std::vector<AssetsLibrary::RangePair> &excluded_range_pairs)</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span> {</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  std::vector<std::pair<T, T>> converted;</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  std::transform(excluded_range_pairs.begin(),</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  excluded_range_pairs.end(),</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  std::back_inserter(converted),</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  [](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a51733f705cc27b63c4be127eb50639c4">AssetsLibrary::RangePair</a> & p)</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="keywordflow">return</span> std::pair<T, T>(static_cast<T>(p.first), static_cast<T>(p.second));</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  });</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <span class="keywordflow">return</span> converted;</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span> }</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span> } <span class="comment">// namespace detail</span></div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> </div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00459"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ae139c07ceb794ec059efb92aa4c6fd9d"> 459</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ae139c07ceb794ec059efb92aa4c6fd9d">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="l00460"></a><span class="lineno"> 460</span> <span class="keyword"></span>{</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</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="l00462"></a><span class="lineno"> 462</span> </div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</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="l00465"></a><span class="lineno"> 465</span>  <span class="keywordflow">if</span>(tensor.shape().num_dimensions() > 1)</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>  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="l00468"></a><span class="lineno"> 468</span>  }</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span> </div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  std::mt19937 gen(_seed);</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span> </div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <a class="code" href="namespacearm__compute.xhtml#a5002bf7ec46d52971f9526e94172cfee">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="l00473"></a><span class="lineno"> 473</span>  {</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</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="l00475"></a><span class="lineno"> 475</span> </div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <span class="comment">// If outside of valid region</span></div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <span class="keywordflow">if</span>(<span class="keywordtype">id</span>.x() < 0 || <span class="keywordtype">id</span>.x() >= static_cast<int>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.x()) || <span class="keywordtype">id</span>.y() < 0 || <span class="keywordtype">id</span>.y() >= static_cast<int>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.y()))</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="keyword">using</span> ResultType = <span class="keyword">typename</span> std::remove_reference<D>::type::result_type;</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <span class="keyword">const</span> ResultType value = distribution(gen);</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</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="l00482"></a><span class="lineno"> 482</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="l00483"></a><span class="lineno"> 483</span>  }</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> }</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> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00488"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a6327330393eee13d12234267ab5d19d4"> 488</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a6327330393eee13d12234267ab5d19d4">AssetsLibrary::fill_boxes</a>(T &&tensor, D &&distribution, std::random_device::result_type seed_offset)<span class="keyword"> const</span></div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span> <span class="keyword"></span>{</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</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="l00491"></a><span class="lineno"> 491</span>  std::mt19937 gen(_seed + seed_offset);</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</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="l00493"></a><span class="lineno"> 493</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> num_boxes = tensor.num_elements() / 4;</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <span class="comment">// Iterate over all elements</span></div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  std::uniform_real_distribution<> size_dist(0.f, 1.f);</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> element_idx = 0; element_idx < num_boxes * 4; element_idx += 4)</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  {</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <span class="keyword">const</span> ResultType delta = size_dist(gen);</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  <span class="keyword">const</span> ResultType <a class="code" href="_asymm_helpers_8cpp.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a> = size_dist(gen);</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <span class="keyword">const</span> ResultType left = distribution(gen);</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <span class="keyword">const</span> ResultType top = distribution(gen);</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  <span class="keyword">const</span> ResultType right = left + delta;</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <span class="keyword">const</span> ResultType bottom = top + <a class="code" href="_asymm_helpers_8cpp.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a>;</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  <span class="keyword">const</span> std::tuple<ResultType, ResultType, ResultType, ResultType> box(left, top, right, bottom);</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> x1 = <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="l00506"></a><span class="lineno"> 506</span>  <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> y1 = <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 + 1);</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> x2 = <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 + 2);</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> y2 = <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 + 3);</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  ResultType &target_value_x1 = reinterpret_cast<ResultType *>(tensor(x1))[0];</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  ResultType &target_value_y1 = reinterpret_cast<ResultType *>(tensor(y1))[0];</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  ResultType &target_value_x2 = reinterpret_cast<ResultType *>(tensor(x2))[0];</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  ResultType &target_value_y2 = reinterpret_cast<ResultType *>(tensor(y2))[0];</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a1e6934e95738573214c2ce1d6648d116">store_value_with_data_type</a>(&target_value_x1, std::get<0>(box), tensor.data_type());</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a1e6934e95738573214c2ce1d6648d116">store_value_with_data_type</a>(&target_value_y1, std::get<1>(box), tensor.data_type());</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a1e6934e95738573214c2ce1d6648d116">store_value_with_data_type</a>(&target_value_x2, std::get<2>(box), tensor.data_type());</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a1e6934e95738573214c2ce1d6648d116">store_value_with_data_type</a>(&target_value_y2, std::get<3>(box), tensor.data_type());</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  }</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ae139c07ceb794ec059efb92aa4c6fd9d">fill_borders_with_garbage</a>(tensor, distribution, seed_offset);</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span> }</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span> </div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00522"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423"> 522</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">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="l00523"></a><span class="lineno"> 523</span> <span class="keyword"></span>{</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</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="l00525"></a><span class="lineno"> 525</span> </div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  std::mt19937 gen(_seed + seed_offset);</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span> </div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  <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="l00529"></a><span class="lineno"> 529</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="l00530"></a><span class="lineno"> 530</span> </div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="keywordflow">if</span>(is_nhwc)</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="comment">// Ensure that the equivalent tensors will be filled for both data layouts</span></div><div class="line"><a name="l00534"></a><span class="lineno"> 534</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>(1U, 2U, 0U));</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  }</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span> </div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="comment">// Iterate over all elements</span></div><div class="line"><a name="l00538"></a><span class="lineno"> 538</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="l00539"></a><span class="lineno"> 539</span>  {</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</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="l00541"></a><span class="lineno"> 541</span> </div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keywordflow">if</span>(is_nhwc)</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  {</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <span class="comment">// Write in the correct id for permuted shapes</span></div><div class="line"><a name="l00545"></a><span class="lineno"> 545</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>(2U, 0U, 1U));</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  }</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="comment">// Iterate over all channels</span></div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> channel = 0; channel < tensor.num_channels(); ++channel)</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  {</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  <span class="keyword">const</span> ResultType value = distribution(gen);</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  ResultType &target_value = reinterpret_cast<ResultType *>(tensor(<span class="keywordtype">id</span>))[channel];</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span> </div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a1e6934e95738573214c2ce1d6648d116">store_value_with_data_type</a>(&target_value, value, tensor.data_type());</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  }</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  }</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span> </div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ae139c07ceb794ec059efb92aa4c6fd9d">fill_borders_with_garbage</a>(tensor, distribution, seed_offset);</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span> }</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span> </div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span> <span class="keyword">template</span> <<span class="keyword">typename</span> D></div><div class="line"><a name="l00562"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a72642223d82ac5d32582d66ba180fdfc"> 562</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">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="l00563"></a><span class="lineno"> 563</span> <span class="keyword"></span>{</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  std::mt19937 gen(_seed + seed_offset);</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">for</span>(<span class="keywordtype">size_t</span> <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = 0; <a class="code" href="src_2core_2_c_l_2cl__kernels_2_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="src_2core_2_c_l_2cl__kernels_2_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="l00567"></a><span class="lineno"> 567</span>  {</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</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="l00569"></a><span class="lineno"> 569</span>  <span class="keyword">const</span> ResultType value = distribution(gen);</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span> </div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <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#a4ae7e1f6885eb47c11062cc74e6a6e19">data</a>() + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_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="l00572"></a><span class="lineno"> 572</span>  }</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span> }</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> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00576"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#afcd66ee0f0a1ad59ad21a4d548f83c21"> 576</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">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="l00577"></a><span class="lineno"> 577</span> <span class="keyword"></span>{</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw = <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#aa6db5c08c0540fb3c0b8292861342a73">get</a>(name, format);</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span> </div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = 0; <a class="code" href="src_2core_2_c_l_2cl__kernels_2_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="src_2core_2_c_l_2cl__kernels_2_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="l00581"></a><span class="lineno"> 581</span>  {</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</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="src_2core_2_c_l_2cl__kernels_2_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="l00583"></a><span class="lineno"> 583</span> </div><div class="line"><a name="l00584"></a><span class="lineno"> 584</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#a4ae7e1f6885eb47c11062cc74e6a6e19">data</a>() + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>;</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <span class="keyword">const</span> <span class="keyword">auto</span> out_ptr = static_cast<RawTensor::value_type *>(tensor(<span class="keywordtype">id</span>));</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</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="l00587"></a><span class="lineno"> 587</span>  }</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span> }</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span> </div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00591"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#aac6c6e6cbcb3d9e0330282f29a6a5e02"> 591</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">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="l00592"></a><span class="lineno"> 592</span> <span class="keyword"></span>{</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">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="l00594"></a><span class="lineno"> 594</span> }</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span> </div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00597"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ab8fd0d2de26c842de05a6d15b28b518a"> 597</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">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="l00598"></a><span class="lineno"> 598</span> <span class="keyword"></span>{</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw = <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#aa6db5c08c0540fb3c0b8292861342a73">get</a>(name, format, channel);</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span> </div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = 0; <a class="code" href="src_2core_2_c_l_2cl__kernels_2_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="src_2core_2_c_l_2cl__kernels_2_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="l00602"></a><span class="lineno"> 602</span>  {</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</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="src_2core_2_c_l_2cl__kernels_2_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="l00604"></a><span class="lineno"> 604</span> </div><div class="line"><a name="l00605"></a><span class="lineno"> 605</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#a4ae7e1f6885eb47c11062cc74e6a6e19">data</a>() + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>;</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <span class="keyword">const</span> <span class="keyword">auto</span> out_ptr = static_cast<RawTensor::value_type *>(tensor(<span class="keywordtype">id</span>));</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</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="l00608"></a><span class="lineno"> 608</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> </div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00612"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a05e6db8fe58b0d75a552c226477a344e"> 612</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">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="l00613"></a><span class="lineno"> 613</span> <span class="keyword"></span>{</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = 0; <a class="code" href="src_2core_2_c_l_2cl__kernels_2_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="src_2core_2_c_l_2cl__kernels_2_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="l00615"></a><span class="lineno"> 615</span>  {</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</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="src_2core_2_c_l_2cl__kernels_2_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="l00617"></a><span class="lineno"> 617</span> </div><div class="line"><a name="l00618"></a><span class="lineno"> 618</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#a4ae7e1f6885eb47c11062cc74e6a6e19">data</a>() + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>;</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <span class="keyword">const</span> <span class="keyword">auto</span> out_ptr = static_cast<RawTensor::value_type *>(tensor(<span class="keywordtype">id</span>));</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</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="l00621"></a><span class="lineno"> 621</span>  }</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</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="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00625"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a6bdf347bae60f8b5b4303776cfc48d68"> 625</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a6bdf347bae60f8b5b4303776cfc48d68">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="l00626"></a><span class="lineno"> 626</span> <span class="keyword"></span>{</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  <span class="keywordflow">switch</span>(tensor.data_type())</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  {</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">DataType::U8</a>:</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>:</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  {</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  std::uniform_int_distribution<uint8_t> distribution_u8(<a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a73e352c61baaf9c1178da2d30105b04e">std::numeric_limits<uint8_t>::lowest</a>(), std::numeric_limits<uint8_t>::max());</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_u8, seed_offset);</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  }</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">DataType::S8</a>:</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  {</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  std::uniform_int_distribution<int8_t> distribution_s8(<a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a73e352c61baaf9c1178da2d30105b04e">std::numeric_limits<int8_t>::lowest</a>(), std::numeric_limits<int8_t>::max());</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_s8, seed_offset);</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <span class="keywordflow">break</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="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">DataType::U16</a>:</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  {</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  std::uniform_int_distribution<uint16_t> distribution_u16(<a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a73e352c61baaf9c1178da2d30105b04e">std::numeric_limits<uint16_t>::lowest</a>(), std::numeric_limits<uint16_t>::max());</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_u16, seed_offset);</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  }</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#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">DataType::S16</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>  std::uniform_int_distribution<int16_t> distribution_s16(<a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a73e352c61baaf9c1178da2d30105b04e">std::numeric_limits<int16_t>::lowest</a>(), std::numeric_limits<int16_t>::max());</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_s16, seed_offset);</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  }</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">DataType::U32</a>:</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  {</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  std::uniform_int_distribution<uint32_t> distribution_u32(<a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a73e352c61baaf9c1178da2d30105b04e">std::numeric_limits<uint32_t>::lowest</a>(), std::numeric_limits<uint32_t>::max());</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_u32, seed_offset);</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  }</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>:</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  {</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  std::uniform_int_distribution<int32_t> distribution_s32(<a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a73e352c61baaf9c1178da2d30105b04e">std::numeric_limits<int32_t>::lowest</a>(), std::numeric_limits<int32_t>::max());</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_s32, seed_offset);</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  }</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">DataType::U64</a>:</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  {</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  std::uniform_int_distribution<uint64_t> distribution_u64(<a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a73e352c61baaf9c1178da2d30105b04e">std::numeric_limits<uint64_t>::lowest</a>(), std::numeric_limits<uint64_t>::max());</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_u64, seed_offset);</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  }</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">DataType::S64</a>:</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  {</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  std::uniform_int_distribution<int64_t> distribution_s64(<a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a73e352c61baaf9c1178da2d30105b04e">std::numeric_limits<int64_t>::lowest</a>(), std::numeric_limits<int64_t>::max());</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#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_s64, 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#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</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>  <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="l00681"></a><span class="lineno"> 681</span>  std::uniform_real_distribution<float> distribution_f16(-100.f, 100.f);</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#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_f16, 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#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</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>  <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="l00688"></a><span class="lineno"> 688</span>  std::uniform_real_distribution<float> distribution_f32(-1000.f, 1000.f);</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#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_f32, 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#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">DataType::F64</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>  <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="l00695"></a><span class="lineno"> 695</span>  std::uniform_real_distribution<double> distribution_f64(-1000.f, 1000.f);</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#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_f64, 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#ad8ed01ff3ff33333d8e19db4d2818bb6abd7ef6d4f35bc7d05c559b65032f15d1">DataType::SIZET</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>  std::uniform_int_distribution<size_t> distribution_sizet(<a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a73e352c61baaf9c1178da2d30105b04e">std::numeric_limits<size_t>::lowest</a>(), std::numeric_limits<size_t>::max());</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_sizet, seed_offset);</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  }</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</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="l00707"></a><span class="lineno"> 707</span>  }</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span> }</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span> </div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00711"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a7fd005cb98921bb25824629d659ea79d"> 711</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a7fd005cb98921bb25824629d659ea79d">AssetsLibrary::fill_tensor_uniform_ranged</a>(T &&tensor,</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  std::random_device::result_type seed_offset,</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  <span class="keyword">const</span> std::vector<AssetsLibrary::RangePair> &excluded_range_pairs)<span class="keyword"> const</span></div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span> <span class="keyword"></span>{</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1utils_1_1random.xhtml">arm_compute::utils::random</a>;</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span> </div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  <span class="keywordflow">switch</span>(tensor.data_type())</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#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">DataType::U8</a>:</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>:</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  {</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  <span class="keyword">const</span> <span class="keyword">auto</span> converted_pairs = detail::convert_range_pair<uint8_t>(excluded_range_pairs);</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <a class="code" href="classarm__compute_1_1utils_1_1random_1_1_ranged_uniform_distribution.xhtml">RangedUniformDistribution<uint8_t></a> distribution_u8(<a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a73e352c61baaf9c1178da2d30105b04e">std::numeric_limits<uint8_t>::lowest</a>(),</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  std::numeric_limits<uint8_t>::max(),</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  converted_pairs);</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_u8, seed_offset);</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  }</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">DataType::S8</a>:</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  {</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  <span class="keyword">const</span> <span class="keyword">auto</span> converted_pairs = detail::convert_range_pair<int8_t>(excluded_range_pairs);</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  <a class="code" href="classarm__compute_1_1utils_1_1random_1_1_ranged_uniform_distribution.xhtml">RangedUniformDistribution<int8_t></a> distribution_s8(<a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a73e352c61baaf9c1178da2d30105b04e">std::numeric_limits<int8_t>::lowest</a>(),</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  std::numeric_limits<int8_t>::max(),</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  converted_pairs);</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_s8, seed_offset);</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  <span class="keywordflow">break</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="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">DataType::U16</a>:</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  {</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  <span class="keyword">const</span> <span class="keyword">auto</span> converted_pairs = detail::convert_range_pair<uint16_t>(excluded_range_pairs);</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  <a class="code" href="classarm__compute_1_1utils_1_1random_1_1_ranged_uniform_distribution.xhtml">RangedUniformDistribution<uint16_t></a> distribution_u16(<a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a73e352c61baaf9c1178da2d30105b04e">std::numeric_limits<uint16_t>::lowest</a>(),</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  std::numeric_limits<uint16_t>::max(),</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  converted_pairs);</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_u16, seed_offset);</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  }</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">DataType::S16</a>:</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  {</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  <span class="keyword">const</span> <span class="keyword">auto</span> converted_pairs = detail::convert_range_pair<int16_t>(excluded_range_pairs);</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <a class="code" href="classarm__compute_1_1utils_1_1random_1_1_ranged_uniform_distribution.xhtml">RangedUniformDistribution<int16_t></a> distribution_s16(<a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a73e352c61baaf9c1178da2d30105b04e">std::numeric_limits<int16_t>::lowest</a>(),</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  std::numeric_limits<int16_t>::max(),</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  converted_pairs);</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_s16, seed_offset);</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  <span class="keywordflow">break</span>;</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">DataType::U32</a>:</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="keyword">const</span> <span class="keyword">auto</span> converted_pairs = detail::convert_range_pair<uint32_t>(excluded_range_pairs);</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  <a class="code" href="classarm__compute_1_1utils_1_1random_1_1_ranged_uniform_distribution.xhtml">RangedUniformDistribution<uint32_t></a> distribution_u32(<a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a73e352c61baaf9c1178da2d30105b04e">std::numeric_limits<uint32_t>::lowest</a>(),</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  std::numeric_limits<uint32_t>::max(),</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  converted_pairs);</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_u32, seed_offset);</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  }</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>:</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="keyword">const</span> <span class="keyword">auto</span> converted_pairs = detail::convert_range_pair<int32_t>(excluded_range_pairs);</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  <a class="code" href="classarm__compute_1_1utils_1_1random_1_1_ranged_uniform_distribution.xhtml">RangedUniformDistribution<int32_t></a> distribution_s32(<a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a73e352c61baaf9c1178da2d30105b04e">std::numeric_limits<int32_t>::lowest</a>(),</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  std::numeric_limits<int32_t>::max(),</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  converted_pairs);</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_s32, seed_offset);</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  }</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</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="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="l00777"></a><span class="lineno"> 777</span>  <span class="keyword">const</span> <span class="keyword">auto</span> converted_pairs = detail::convert_range_pair<float>(excluded_range_pairs);</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  <a class="code" href="classarm__compute_1_1utils_1_1random_1_1_ranged_uniform_distribution.xhtml">RangedUniformDistribution<float></a> distribution_f16(-100.f, 100.f, converted_pairs);</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_f16, seed_offset);</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  <span class="keywordflow">break</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</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">// It doesn't make sense to check [-inf, inf], so hard code it to a big number</span></div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  <span class="keyword">const</span> <span class="keyword">auto</span> converted_pairs = detail::convert_range_pair<float>(excluded_range_pairs);</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  <a class="code" href="classarm__compute_1_1utils_1_1random_1_1_ranged_uniform_distribution.xhtml">RangedUniformDistribution<float></a> distribution_f32(-1000.f, 1000.f, converted_pairs);</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_f32, seed_offset);</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  <span class="keywordflow">break</span>;</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">default</span>:</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</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="l00792"></a><span class="lineno"> 792</span>  }</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span> }</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span> </div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00796"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ab419bdd4d1b71e56517cbd99428e3740"> 796</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a6bdf347bae60f8b5b4303776cfc48d68">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="l00797"></a><span class="lineno"> 797</span> <span class="keyword"></span>{</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  <span class="keywordflow">switch</span>(tensor.data_type())</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">DataType::U8</a>:</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>:</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>  <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="l00804"></a><span class="lineno"> 804</span>  std::uniform_int_distribution<uint8_t> distribution_u8(low, high);</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_u8, seed_offset);</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  <span class="keywordflow">break</span>;</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="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">DataType::S8</a>:</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  {</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</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="l00811"></a><span class="lineno"> 811</span>  std::uniform_int_distribution<int8_t> distribution_s8(low, high);</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_s8, seed_offset);</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  }</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">DataType::U16</a>:</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  {</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</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="l00818"></a><span class="lineno"> 818</span>  std::uniform_int_distribution<uint16_t> distribution_u16(low, high);</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_u16, seed_offset);</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  }</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">DataType::S16</a>:</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  {</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</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="l00825"></a><span class="lineno"> 825</span>  std::uniform_int_distribution<int16_t> distribution_s16(low, high);</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_s16, seed_offset);</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  }</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">DataType::U32</a>:</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  {</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</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="l00832"></a><span class="lineno"> 832</span>  std::uniform_int_distribution<uint32_t> distribution_u32(low, high);</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_u32, seed_offset);</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  }</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>:</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  {</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</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="l00839"></a><span class="lineno"> 839</span>  std::uniform_int_distribution<int32_t> distribution_s32(low, high);</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_s32, seed_offset);</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  }</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">DataType::U64</a>:</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  {</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</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="l00846"></a><span class="lineno"> 846</span>  std::uniform_int_distribution<uint64_t> distribution_u64(low, high);</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_u64, seed_offset);</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  }</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">DataType::S64</a>:</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  {</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</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="l00853"></a><span class="lineno"> 853</span>  std::uniform_int_distribution<int64_t> distribution_s64(low, high);</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_s64, seed_offset);</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  }</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  {</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  std::uniform_real_distribution<float> distribution_f16(low, high);</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_f16, seed_offset);</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  }</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  {</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</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="l00866"></a><span class="lineno"> 866</span>  std::uniform_real_distribution<float> distribution_f32(low, high);</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_f32, seed_offset);</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  }</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">DataType::F64</a>:</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  {</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</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="l00873"></a><span class="lineno"> 873</span>  std::uniform_real_distribution<double> distribution_f64(low, high);</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_f64, seed_offset);</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  }</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6abd7ef6d4f35bc7d05c559b65032f15d1">DataType::SIZET</a>:</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  {</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</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="l00880"></a><span class="lineno"> 880</span>  std::uniform_int_distribution<size_t> distribution_sizet(low, high);</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">fill</a>(tensor, distribution_sizet, seed_offset);</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  }</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</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="l00886"></a><span class="lineno"> 886</span>  }</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span> }</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span> </div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00890"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a5580270336a3055bb7477b227563ccf0"> 890</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a5580270336a3055bb7477b227563ccf0">AssetsLibrary::fill_layer_data</a>(T &&tensor, std::string name)<span class="keyword"> const</span></div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span> <span class="keyword"></span>{</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span> <span class="preprocessor">#ifdef _WIN32</span></div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  <span class="keyword">const</span> std::string path_separator(<span class="stringliteral">"\\"</span>);</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span> <span class="preprocessor">#else </span><span class="comment">/* _WIN32 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  <span class="keyword">const</span> std::string path_separator(<span class="stringliteral">"/"</span>);</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span> <span class="preprocessor">#endif </span><span class="comment">/* _WIN32 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  <span class="keyword">const</span> std::string <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac4cb5f95f1d720ef0cc94b74152cf50b">path</a> = _library_path + path_separator + name;</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span> </div><div class="line"><a name="l00899"></a><span class="lineno"> 899</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="l00900"></a><span class="lineno"> 900</span> </div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  <span class="comment">// Open file</span></div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  std::ifstream stream(<a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac4cb5f95f1d720ef0cc94b74152cf50b">path</a>, std::ios::in | std::ios::binary);</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  <span class="keywordflow">if</span>(!stream.good())</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  {</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</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> + <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac4cb5f95f1d720ef0cc94b74152cf50b">path</a>);</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  }</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  std::string header = npy::read_header(stream);</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span> </div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  <span class="comment">// Parse header</span></div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  <span class="keywordtype">bool</span> fortran_order = <span class="keyword">false</span>;</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  std::string typestr;</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  npy::parse_header(header, typestr, fortran_order, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>);</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span> </div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  <span class="comment">// Check if the typestring matches the given one</span></div><div class="line"><a name="l00915"></a><span class="lineno"> 915</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="l00916"></a><span class="lineno"> 916</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="l00917"></a><span class="lineno"> 917</span> </div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  <span class="comment">// Validate tensor shape</span></div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.size() != tensor.shape().num_dimensions(), <span class="stringliteral">"Tensor ranks mismatch"</span>);</div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  <span class="keywordflow">if</span>(fortran_order)</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  {</div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i = 0; i < <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.size(); ++i)</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  {</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(tensor.shape()[i] != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>[i], <span class="stringliteral">"Tensor dimensions mismatch"</span>);</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  }</div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  }</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  {</div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i = 0; i < <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.size(); ++i)</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  {</div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(tensor.shape()[i] != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>[<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.size() - i - 1], <span class="stringliteral">"Tensor dimensions mismatch"</span>);</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  }</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  }</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span> </div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  <span class="comment">// Read data</span></div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  <span class="keywordflow">if</span>(tensor.padding().empty())</div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  {</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  <span class="comment">// If tensor has no padding read directly from stream.</span></div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  stream.read(reinterpret_cast<char *>(tensor.data()), tensor.size());</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  }</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  {</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  <span class="comment">// If tensor has padding accessing tensor elements through execution window.</span></div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</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="l00946"></a><span class="lineno"> 946</span> </div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  <a class="code" href="namespacearm__compute.xhtml#a5002bf7ec46d52971f9526e94172cfee">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="l00948"></a><span class="lineno"> 948</span>  {</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  stream.read(reinterpret_cast<char *>(tensor(<span class="keywordtype">id</span>)), tensor.element_size());</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  });</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  }</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span> }</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span> </div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00955"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_assets_library.xhtml#aa13fcfba9d7f0433db83255bd1f0638a"> 955</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#aa13fcfba9d7f0433db83255bd1f0638a">AssetsLibrary::fill_tensor_value</a>(T &&tensor, D value)<span class="keyword"> const</span></div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span> <span class="keyword"></span>{</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  <a class="code" href="classarm__compute_1_1test_1_1_assets_library.xhtml#a6bdf347bae60f8b5b4303776cfc48d68">fill_tensor_uniform</a>(tensor, 0, value, value);</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span> }</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span> } <span class="comment">// namespace test</span></div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span> } <span class="comment">// namespace arm_compute</span></div><div class="line"><a name="l00961"></a><span class="lineno"> 961</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="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_a009469e4d9b8fce3b6d5e97d2077827d"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_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="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00312">helpers.h:312</a></div></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#l00261">Error.h:261</a></div></div> |
| <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#l00393">Types.h:393</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a45cde9abb508c62d67c3bb2b9bf566a5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">arm_compute::test::validation::shape</a></div><div class="ttdeci">shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_absolute_difference_8cpp_source.xhtml#l00097">AbsoluteDifference.cpp:97</a></div></div> |
| <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#l00327">SimpleTensor.h:327</a></div></div> |
| <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> |
| <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#l00042">TensorCache.h:42</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1utils_1_1random_1_1_ranged_uniform_distribution_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1random_1_1_ranged_uniform_distribution.xhtml">arm_compute::utils::random::RangedUniformDistribution</a></div><div class="ttdoc">Uniform distribution within a given number of sub-ranges.</div><div class="ttdef"><b>Definition:</b> <a href="_random_8h_source.xhtml#l00043">Random.h:43</a></div></div> |
| <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#l00339">SimpleTensor.h:339</a></div></div> |
| <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#l00313">Types.h:313</a></div></div> |
| <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#l00059">AssetsLibrary.h:59</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_ac4cb5f95f1d720ef0cc94b74152cf50b"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac4cb5f95f1d720ef0cc94b74152cf50b">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> |
| <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#l00147">Utils.h:147</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">arm_compute::Format::U8</a></div><div class="ttdoc">1 channel, 1 U8 per channel</div></div> |
| <div class="ttc" id="_asymm_helpers_8cpp_xhtml_a552dc3787d7ea1675f3e4e8993501d58"><div class="ttname"><a href="_asymm_helpers_8cpp.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a></div><div class="ttdeci">constexpr float epsilon</div><div class="ttdef"><b>Definition:</b> <a href="_asymm_helpers_8cpp_source.xhtml#l00033">AsymmHelpers.cpp:33</a></div></div> |
| <div class="ttc" id="_window_8h_xhtml"><div class="ttname"><a href="_window_8h.xhtml">Window.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1detail_xhtml_a8f88ff91d07f88724302b134e4cd7eac"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1detail.xhtml#a8f88ff91d07f88724302b134e4cd7eac">arm_compute::test::detail::convert_range_pair</a></div><div class="ttdeci">std::vector< std::pair< T, T > > convert_range_pair(const std::vector< AssetsLibrary::RangePair > &excluded_range_pairs)</div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8h_source.xhtml#l00444">AssetsLibrary.h:444</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel</div></div> |
| <div class="ttc" id="classarm__compute_1_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#l00358">SimpleTensor.h:358</a></div></div> |
| <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> |
| <div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00337">Error.h:337</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_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'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> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">arm_compute::Format::U16</a></div><div class="ttdoc">1 channel, 1 U16 per channel</div></div> |
| <div class="ttc" id="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#l00395">Types.h:395</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_acded863dbfdd730829d4188d67eefcf0"><div class="ttname"><a href="namespacearm__compute.xhtml#acded863dbfdd730829d4188d67eefcf0">arm_compute::Mutex</a></div><div class="ttdeci">std::mutex Mutex</div><div class="ttdoc">Wrapper of Mutex data-object.</div><div class="ttdef"><b>Definition:</b> <a href="_mutex_8h_source.xhtml#l00033">Mutex.h:33</a></div></div> |
| <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#l00321">SimpleTensor.h:321</a></div></div> |
| <div class="ttc" id="_tensor_info_8h_xhtml"><div class="ttname"><a href="_tensor_info_8h.xhtml">TensorInfo.h</a></div></div> |
| <div class="ttc" id="_random_8h_xhtml"><div class="ttname"><a href="_random_8h.xhtml">Random.h</a></div></div> |
| <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#l00461">AssetsLibrary.cpp:461</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_aa6db5c08c0540fb3c0b8292861342a73"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#aa6db5c08c0540fb3c0b8292861342a73">arm_compute::test::AssetsLibrary::get</a></div><div class="ttdeci">const RawTensor & get(const std::string &name) const</div><div class="ttdoc">Provides a constant raw tensor for the specified image.</div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8cpp_source.xhtml#l00466">AssetsLibrary.cpp:466</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_a51733f705cc27b63c4be127eb50639c4"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#a51733f705cc27b63c4be127eb50639c4">arm_compute::test::AssetsLibrary::RangePair</a></div><div class="ttdeci">std::pair< float, float > RangePair</div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8h_source.xhtml#l00062">AssetsLibrary.h:62</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1support_1_1cpp11_xhtml_a73e352c61baaf9c1178da2d30105b04e"><div class="ttname"><a href="namespacearm__compute_1_1support_1_1cpp11.xhtml#a73e352c61baaf9c1178da2d30105b04e">arm_compute::support::cpp11::lowest</a></div><div class="ttdeci">T lowest()</div><div class="ttdef"><b>Definition:</b> <a href="_toolchain_support_8h_source.xhtml#l00320">ToolchainSupport.h:320</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_a14470b4cb59140a1b6ff3b8f16c89ab6"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a14470b4cb59140a1b6ff3b8f16c89ab6">arm_compute::Window::use_tensor_dimensions</a></div><div class="ttdeci">void use_tensor_dimensions(const TensorShape &shape, size_t first_dimension=Window::DimX)</div><div class="ttdoc">Use the tensor's dimensions to fill the window dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00243">Window.inl:243</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_a6327330393eee13d12234267ab5d19d4"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#a6327330393eee13d12234267ab5d19d4">arm_compute::test::AssetsLibrary::fill_boxes</a></div><div class="ttdeci">void fill_boxes(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const</div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8h_source.xhtml#l00488">AssetsLibrary.h:488</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2018 ARM Limited.</div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::Format::F16</a></div><div class="ttdoc">1 channel, 1 F16 per channel</div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_aa13fcfba9d7f0433db83255bd1f0638a"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#aa13fcfba9d7f0433db83255bd1f0638a">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#l00955">AssetsLibrary.h:955</a></div></div> |
| <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#l00570">Helpers.h:570</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::Format::S32</a></div><div class="ttdoc">1 channel, 1 S32 per channel</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">arm_compute::DataType::S64</a></div><div class="ttdoc">signed 64-bit number</div></div> |
| <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_convolution_layer_8cpp_source.xhtml#l00174">ConvolutionLayer.cpp:174</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_a7fd005cb98921bb25824629d659ea79d"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#a7fd005cb98921bb25824629d659ea79d">arm_compute::test::AssetsLibrary::fill_tensor_uniform_ranged</a></div><div class="ttdeci">void fill_tensor_uniform_ranged(T &&tensor, std::random_device::result_type seed_offset, const std::vector< AssetsLibrary::RangePair > &excluded_range_pairs) const</div><div class="ttdoc">Fill a tensor with uniform distribution across the specified range.</div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8h_source.xhtml#l00711">AssetsLibrary.h:711</a></div></div> |
| <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#l00349">Utils.h:349</a></div></div> |
| <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> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">arm_compute::Format::U32</a></div><div class="ttdoc">1 channel, 1 U32 per channel</div></div> |
| <div class="ttc" id="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#l00505">Types.h:505</a></div></div> |
| <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#l00052">Types.h:52</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::DataType::QASYMM8</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number</div></div> |
| <div class="ttc" id="classarm__compute_1_1_coordinates_xhtml"><div class="ttname"><a href="classarm__compute_1_1_coordinates.xhtml">arm_compute::Coordinates</a></div><div class="ttdoc">Coordinates of an item.</div><div class="ttdef"><b>Definition:</b> <a href="_coordinates_8h_source.xhtml#l00037">Coordinates.h:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_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#l00104">SimpleTensor.h:104</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_a5580270336a3055bb7477b227563ccf0"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#a5580270336a3055bb7477b227563ccf0">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#l00890">AssetsLibrary.h:890</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_ac186c860429337d470bccc138ed84423"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#ac186c860429337d470bccc138ed84423">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#l00522">AssetsLibrary.h:522</a></div></div> |
| <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> |
| <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_absolute_difference_8cpp_source.xhtml#l00102">AbsoluteDifference.cpp:102</a></div></div> |
| <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> |
| <div class="ttc" id="_error_8h_xhtml"><div class="ttname"><a href="_error_8h.xhtml">Error.h</a></div></div> |
| <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#l00448">Utils.h:448</a></div></div> |
| <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#l00396">Types.h:396</a></div></div> |
| <div class="ttc" id="_tensor_cache_8h_xhtml"><div class="ttname"><a href="_tensor_cache_8h.xhtml">TensorCache.h</a></div></div> |
| <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#l00394">Types.h:394</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ae9d2dc29c2789c253406f9b304cc75a8"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ae9d2dc29c2789c253406f9b304cc75a8">arm_compute::test::validation::src</a></div><div class="ttdeci">cast configure & src</div><div class="ttdef"><b>Definition:</b> <a href="_cast_8cpp_source.xhtml#l00169">Cast.cpp:169</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">arm_compute::Format::S16</a></div><div class="ttdoc">1 channel, 1 S16 per channel</div></div> |
| <div class="ttc" id="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#l00725">Utils.h:725</a></div></div> |
| <div class="ttc" id="tests_2_utils_8h_xhtml"><div class="ttname"><a href="tests_2_utils_8h.xhtml">Utils.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_a6bdf347bae60f8b5b4303776cfc48d68"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#a6bdf347bae60f8b5b4303776cfc48d68">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.</div><div class="ttdef"><b>Definition:</b> <a href="_assets_library_8h_source.xhtml#l00625">AssetsLibrary.h:625</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1utils_1_1random_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1utils_1_1random.xhtml">arm_compute::utils::random</a></div><div class="ttdef"><b>Definition:</b> <a href="_random_8h_source.xhtml#l00036">Random.h:36</a></div></div> |
| <div class="ttc" id="_coordinates_8h_xhtml"><div class="ttname"><a href="_coordinates_8h.xhtml">Coordinates.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">arm_compute::DataLayout::NHWC</a></div><div class="ttdoc">Num samples, height, width, channels.</div></div> |
| <div class="ttc" id="_exceptions_8h_xhtml"><div class="ttname"><a href="_exceptions_8h.xhtml">Exceptions.h</a></div></div> |
| <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> |
| <div class="ttc" id="namespacearm__compute_xhtml_a5002bf7ec46d52971f9526e94172cfee"><div class="ttname"><a href="namespacearm__compute.xhtml#a5002bf7ec46d52971f9526e94172cfee">arm_compute::execute_window_loop</a></div><div class="ttdeci">void execute_window_loop(const Window &w, L &&lambda_function, Ts &&... iterators)</div><div class="ttdoc">Iterate through the passed window, automatically adjusting the iterators and calling the lambda_funct...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00122">Helpers.inl:122</a></div></div> |
| <div class="ttc" id="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> |
| <div class="ttc" id="_raw_tensor_8h_xhtml"><div class="ttname"><a href="_raw_tensor_8h.xhtml">RawTensor.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">arm_compute::DataType::U64</a></div><div class="ttdoc">unsigned 64-bit number</div></div> |
| <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> |
| <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#l00074">Types.h:74</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">arm_compute::DataType::S8</a></div><div class="ttdoc">signed 8-bit number</div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_ae139c07ceb794ec059efb92aa4c6fd9d"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#ae139c07ceb794ec059efb92aa4c6fd9d">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#l00459">AssetsLibrary.h:459</a></div></div> |
| <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> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml">arm_compute::Window</a></div><div class="ttdoc">Describe a multidimensional execution window.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00039">Window.h:39</a></div></div> |
| <div class="ttc" id="_tensor_shape_8h_xhtml"><div class="ttname"><a href="_tensor_shape_8h.xhtml">TensorShape.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_assets_library_xhtml_a0a20fa200643e1e3aa4004375d9188f1"><div class="ttname"><a href="classarm__compute_1_1test_1_1_assets_library.xhtml#a0a20fa200643e1e3aa4004375d9188f1">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> |
| <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#l00328">Error.h:328</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a4ae7e1f6885eb47c11062cc74e6a6e19"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a4ae7e1f6885eb47c11062cc74e6a6e19">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#l00419">SimpleTensor.h:419</a></div></div> |
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