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| <a href="_tensor_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 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="_raw_tensor_8h.xhtml">RawTensor.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_tensor_cache_8h.xhtml">TensorCache.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="tests_2_utils_8h.xhtml">Utils.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</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="l00032"></a><span class="lineno"> 32</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="l00033"></a><span class="lineno"> 33</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="l00034"></a><span class="lineno"> 34</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="l00035"></a><span class="lineno"> 35</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="l00036"></a><span class="lineno"> 36</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="l00037"></a><span class="lineno"> 37</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="l00038"></a><span class="lineno"> 38</span> </div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="preprocessor">#include <algorithm></span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="preprocessor">#include <cstddef></span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="preprocessor">#include <fstream></span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="preprocessor">#include <random></span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="preprocessor">#include <string></span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="preprocessor">#include <type_traits></span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> {</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="keyword">namespace </span>test</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> {</div><div class="line"><a name="l00056"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml"> 56</a></span> <span class="keyword">class </span><a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml">TensorLibrary</a> final</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> {</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a65b3f12d28af30bbb0d6cf75e7c4c316">TensorLibrary</a>(std::string path);</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a65b3f12d28af30bbb0d6cf75e7c4c316">TensorLibrary</a>(std::string path, std::random_device::result_type <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a4035a1140831801ced5dfa1d9fe6988a">seed</a>);</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  std::random_device::result_type <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a4035a1140831801ced5dfa1d9fe6988a">seed</a>() <span class="keyword">const</span>;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keyword">static</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <span class="keyword">get</span>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &shape, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> data_type, <span class="keywordtype">int</span> num_channels = 1, <span class="keywordtype">int</span> fixed_point_position = 0);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keyword">static</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <span class="keyword">get</span>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &shape, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> </div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<span class="keyword">get</span>(<span class="keyword">const</span> std::string &name) <span class="keyword">const</span>;</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> </div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <span class="keyword">get</span>(<span class="keyword">const</span> std::string &name);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <span class="keyword">get</span>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> data_type, <span class="keywordtype">int</span> num_channels = 1) <span class="keyword">const</span>;</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> </div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<span class="keyword">get</span>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format) <span class="keyword">const</span>;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <span class="keyword">get</span>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format);</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> </div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<span class="keyword">get</span>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <span class="keyword">get</span>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel);</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> </div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<span class="keyword">get</span>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> </div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <span class="keyword">get</span>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> </div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) <span class="keyword">const</span>;</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> </div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> D></div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw, D &&distribution, std::random_device::result_type seed_offset) <span class="keyword">const</span>;</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> </div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(T &&tensor, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format) <span class="keyword">const</span>;</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format) <span class="keyword">const</span>;</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> </div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(T &&tensor, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> </div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</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_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(T &&tensor, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> </div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel) <span class="keyword">const</span>;</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> </div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#abb6f25295592e886976520216187eed7">fill_tensor_uniform</a>(T &&tensor, std::random_device::result_type seed_offset) <span class="keyword">const</span>;</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span> </div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#abb6f25295592e886976520216187eed7">fill_tensor_uniform</a>(T &&tensor, std::random_device::result_type seed_offset, D low, D high) <span class="keyword">const</span>;</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span> </div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#ad85dc4c57a27c44d114c573b9a80bad6">fill_layer_data</a>(T &&tensor, std::string name) <span class="keyword">const</span>;</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> </div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="comment">// Function prototype to convert between image formats.</span></div><div class="line"><a name="l00311"></a><span class="lineno"> 311</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> &src, <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &dst);</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="comment">// Function prototype to extract a channel from an image.</span></div><div class="line"><a name="l00313"></a><span class="lineno"> 313</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> &src, <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &dst);</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="comment">// Function prototype to load an image file.</span></div><div class="line"><a name="l00315"></a><span class="lineno"> 315</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 &path);</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span> </div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keyword">const</span> Converter &get_converter(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> src, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> dst) <span class="keyword">const</span>;</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <span class="keyword">const</span> Converter &get_converter(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> src, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> dst) <span class="keyword">const</span>;</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="keyword">const</span> Converter &get_converter(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> src, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dst) <span class="keyword">const</span>;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="keyword">const</span> Converter &get_converter(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> src, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dst) <span class="keyword">const</span>;</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</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="l00322"></a><span class="lineno"> 322</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="l00323"></a><span class="lineno"> 323</span> </div><div class="line"><a name="l00331"></a><span class="lineno"> 331</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="l00332"></a><span class="lineno"> 332</span> </div><div class="line"><a name="l00344"></a><span class="lineno"> 344</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="l00345"></a><span class="lineno"> 345</span> </div><div class="line"><a name="l00358"></a><span class="lineno"> 358</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="l00359"></a><span class="lineno"> 359</span> </div><div class="line"><a name="l00360"></a><span class="lineno"> 360</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="l00361"></a><span class="lineno"> 361</span>  <span class="keyword">mutable</span> std::mutex _format_lock{};</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="keyword">mutable</span> std::mutex _channel_lock{};</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  std::string _library_path;</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  std::random_device::result_type _seed;</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span> };</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span> </div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00368"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0"> 368</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">TensorLibrary::fill</a>(T &&tensor, D &&distribution, std::random_device::result_type seed_offset)<span class="keyword"> const</span></div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span> <span class="keyword"></span>{</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> d = 0; d < tensor.shape().num_dimensions(); ++d)</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  {</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(d, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(0, tensor.shape()[d], 1));</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  }</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span> </div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  std::mt19937 gen(_seed + seed_offset);</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span> </div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <a class="code" href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">execute_window_loop</a>(window, [&](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & <span class="keywordtype">id</span>)</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  {</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</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="l00381"></a><span class="lineno"> 381</span>  <span class="keyword">const</span> ResultType value = distribution(gen);</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</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="l00383"></a><span class="lineno"> 383</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="l00384"></a><span class="lineno"> 384</span>  });</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span> }</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span> </div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> <span class="keyword">template</span> <<span class="keyword">typename</span> D></div><div class="line"><a name="l00388"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a935d861fcffb0099b001498c494daaf6"> 388</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">TensorLibrary::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="l00389"></a><span class="lineno"> 389</span> <span class="keyword"></span>{</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  std::mt19937 gen(_seed + seed_offset);</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span> </div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = 0; <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> < raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#aac782da1f912bceb5d8ad00c8dc892ac">size</a>(); <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> += raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a448f57f9d6aec61b3d85b898affe4a2e">element_size</a>())</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  {</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</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="l00395"></a><span class="lineno"> 395</span>  <span class="keyword">const</span> ResultType value = distribution(gen);</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</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_raw_tensor.xhtml#a7b53deaf986aa58ffa0090cc241dec64">data</a>() + <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>, value, raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a45cc7b9a37aa9f0e7d479248a27e1f58">data_type</a>());</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  }</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span> }</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span> </div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00401"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a5ee4fc10b84f941236df524f618b96c6"> 401</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">TensorLibrary::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="l00402"></a><span class="lineno"> 402</span> <span class="keyword"></span>{</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw = <span class="keyword">get</span>(name, format);</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> </div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = 0; <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> < raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#aac782da1f912bceb5d8ad00c8dc892ac">size</a>(); <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> += raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a448f57f9d6aec61b3d85b898affe4a2e">element_size</a>())</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  {</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keyword">const</span> <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_raw_tensor.xhtml#a13761831550669f43f4edee978181c46">shape</a>(), <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> / raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a448f57f9d6aec61b3d85b898affe4a2e">element_size</a>());</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span> </div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a9c3b791dba4a4cff3785264b9260e9d5">RawTensor::BufferType</a> *<span class="keyword">const</span> raw_ptr = raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a7b53deaf986aa58ffa0090cc241dec64">data</a>() + <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>;</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="keyword">const</span> <span class="keyword">auto</span> out_ptr = <span class="keyword">static_cast<</span><a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a9c3b791dba4a4cff3785264b9260e9d5">RawTensor::BufferType</a> *<span class="keyword">></span>(tensor(<span class="keywordtype">id</span>));</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  std::copy_n(raw_ptr, raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a448f57f9d6aec61b3d85b898affe4a2e">element_size</a>(), out_ptr);</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  }</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span> }</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span> </div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00416"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a4d049b9a5aba1f30b51f4bd36c3db076"> 416</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">TensorLibrary::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="l00417"></a><span class="lineno"> 417</span> <span class="keyword"></span>{</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(std::forward<T>(tensor), name, <a class="code" href="namespacearm__compute_1_1test.xhtml#aa337ab76176f3c4193642ac6de3a61cf">get_format_for_channel</a>(channel), channel);</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> }</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span> </div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00422"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a36a9ddc6792949fb561cd788e6e31208"> 422</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">TensorLibrary::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="l00423"></a><span class="lineno"> 423</span> <span class="keyword"></span>{</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw = <span class="keyword">get</span>(name, format, channel);</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span> </div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = 0; <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> < raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#aac782da1f912bceb5d8ad00c8dc892ac">size</a>(); <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> += raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a448f57f9d6aec61b3d85b898affe4a2e">element_size</a>())</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  {</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <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_raw_tensor.xhtml#a13761831550669f43f4edee978181c46">shape</a>(), <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> / raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a448f57f9d6aec61b3d85b898affe4a2e">element_size</a>());</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span> </div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a9c3b791dba4a4cff3785264b9260e9d5">RawTensor::BufferType</a> *<span class="keyword">const</span> raw_ptr = raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a7b53deaf986aa58ffa0090cc241dec64">data</a>() + <a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>;</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="keyword">const</span> <span class="keyword">auto</span> out_ptr = <span class="keyword">static_cast<</span><a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a9c3b791dba4a4cff3785264b9260e9d5">RawTensor::BufferType</a> *<span class="keyword">></span>(tensor(<span class="keywordtype">id</span>));</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  std::copy_n(raw_ptr, raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a448f57f9d6aec61b3d85b898affe4a2e">element_size</a>(), out_ptr);</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  }</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span> }</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span> </div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00437"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#abb6f25295592e886976520216187eed7"> 437</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#abb6f25295592e886976520216187eed7">TensorLibrary::fill_tensor_uniform</a>(T &&tensor, std::random_device::result_type seed_offset)<span class="keyword"> const</span></div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span> <span class="keyword"></span>{</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <span class="keywordflow">switch</span>(tensor.data_type())</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  {</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">DataType::U8</a>:</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>  std::uniform_int_distribution<uint8_t> distribution_u8(std::numeric_limits<uint8_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<uint8_t>::max</a>());</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u8, seed_offset);</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  }</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">DataType::S8</a>:</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">DataType::QS8</a>:</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  {</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  std::uniform_int_distribution<int8_t> distribution_s8(std::numeric_limits<int8_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<int8_t>::max</a>());</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s8, seed_offset);</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  }</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">DataType::U16</a>:</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>  std::uniform_int_distribution<uint16_t> distribution_u16(std::numeric_limits<uint16_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<uint16_t>::max</a>());</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u16, seed_offset);</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  }</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">DataType::S16</a>:</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  {</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  std::uniform_int_distribution<int16_t> distribution_s16(std::numeric_limits<int16_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<int16_t>::max</a>());</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s16, seed_offset);</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  }</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">DataType::U32</a>:</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  {</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  std::uniform_int_distribution<uint32_t> distribution_u32(std::numeric_limits<uint32_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<uint32_t>::max</a>());</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u32, seed_offset);</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <span class="keywordflow">break</span>;</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>:</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>  std::uniform_int_distribution<int32_t> distribution_s32(std::numeric_limits<int32_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<int32_t>::max</a>());</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s32, seed_offset);</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  }</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">DataType::U64</a>:</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  {</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  std::uniform_int_distribution<uint64_t> distribution_u64(std::numeric_limits<uint64_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<uint64_t>::max</a>());</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u64, seed_offset);</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  }</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">DataType::S64</a>:</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>  std::uniform_int_distribution<int64_t> distribution_s64(std::numeric_limits<int64_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<int64_t>::max</a>());</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s64, seed_offset);</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  }</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span> <span class="preprocessor">#ifdef ENABLE_FP16</span></div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  {</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  std::uniform_real_distribution<float16_t> distribution_f16(std::numeric_limits<float16_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<float16_t>::max</a>());</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f16, seed_offset);</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  }</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  {</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</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="l00501"></a><span class="lineno"> 501</span>  std::uniform_real_distribution<float> distribution_f32(-1000.f, 1000.f);</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f32, seed_offset);</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  }</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">DataType::F64</a>:</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  {</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <span class="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="l00508"></a><span class="lineno"> 508</span>  std::uniform_real_distribution<double> distribution_f64(-1000.f, 1000.f);</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f64, seed_offset);</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  }</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6abd7ef6d4f35bc7d05c559b65032f15d1">DataType::SIZET</a>:</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  {</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  std::uniform_int_distribution<size_t> distribution_sizet(std::numeric_limits<size_t>::lowest(), <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<size_t>::max</a>());</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_sizet, seed_offset);</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <span class="keywordflow">break</span>;</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>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</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="l00520"></a><span class="lineno"> 520</span>  }</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span> }</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span> </div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00524"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a150d2023c197e197f09f350ede795085"> 524</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#abb6f25295592e886976520216187eed7">TensorLibrary::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="l00525"></a><span class="lineno"> 525</span> <span class="keyword"></span>{</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  <span class="keywordflow">switch</span>(tensor.data_type())</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="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">DataType::U8</a>:</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  {</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <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="l00531"></a><span class="lineno"> 531</span>  std::uniform_int_distribution<uint8_t> distribution_u8(low, high);</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u8, seed_offset);</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  }</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">DataType::S8</a>:</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">DataType::QS8</a>:</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  {</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</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="l00539"></a><span class="lineno"> 539</span>  std::uniform_int_distribution<int8_t> distribution_s8(low, high);</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s8, seed_offset);</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  }</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">DataType::U16</a>:</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  {</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <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="l00546"></a><span class="lineno"> 546</span>  std::uniform_int_distribution<uint16_t> distribution_u16(low, high);</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u16, seed_offset);</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  }</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">DataType::S16</a>:</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  {</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <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="l00553"></a><span class="lineno"> 553</span>  std::uniform_int_distribution<int16_t> distribution_s16(low, high);</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s16, seed_offset);</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  <span class="keywordflow">break</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">DataType::U32</a>:</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  {</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <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="l00560"></a><span class="lineno"> 560</span>  std::uniform_int_distribution<uint32_t> distribution_u32(low, high);</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u32, seed_offset);</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  }</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>:</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>  <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="l00567"></a><span class="lineno"> 567</span>  std::uniform_int_distribution<int32_t> distribution_s32(low, high);</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s32, seed_offset);</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  }</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">DataType::U64</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>  <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="l00574"></a><span class="lineno"> 574</span>  std::uniform_int_distribution<uint64_t> distribution_u64(low, high);</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_u64, seed_offset);</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  }</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">DataType::S64</a>:</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>  <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="l00581"></a><span class="lineno"> 581</span>  std::uniform_int_distribution<int64_t> distribution_s64(low, high);</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_s64, seed_offset);</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  }</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span> <span class="preprocessor">#if ENABLE_FP16</span></div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  {</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!(std::is_same<float16_t, D>::value));</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  std::uniform_real_distribution<float16_t> distribution_f16(low, high);</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f16, seed_offset);</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  }</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</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>  <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="l00597"></a><span class="lineno"> 597</span>  std::uniform_real_distribution<float> distribution_f32(low, high);</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f32, seed_offset);</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <span class="keywordflow">break</span>;</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">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">DataType::F64</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>  <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="l00604"></a><span class="lineno"> 604</span>  std::uniform_real_distribution<double> distribution_f64(low, high);</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_f64, seed_offset);</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  }</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6abd7ef6d4f35bc7d05c559b65032f15d1">DataType::SIZET</a>:</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>  <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="l00611"></a><span class="lineno"> 611</span>  std::uniform_int_distribution<size_t> distribution_sizet(low, high);</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(tensor, distribution_sizet, seed_offset);</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  }</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</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="l00617"></a><span class="lineno"> 617</span>  }</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span> }</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span> </div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00621"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#ad85dc4c57a27c44d114c573b9a80bad6"> 621</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#ad85dc4c57a27c44d114c573b9a80bad6">TensorLibrary::fill_layer_data</a>(T &&tensor, std::string name)<span class="keyword"> const</span></div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span> <span class="keyword"></span>{</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span> <span class="preprocessor">#ifdef _WIN32</span></div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  <span class="keyword">const</span> std::string path_separator(<span class="stringliteral">"\\"</span>);</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span> <span class="preprocessor">#else</span></div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <span class="keyword">const</span> std::string path_separator(<span class="stringliteral">"/"</span>);</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span> <span class="preprocessor">#endif</span></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="keyword">const</span> std::string path = _library_path + path_separator + name;</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span> </div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <span class="comment">// Open file</span></div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  std::ifstream file(path, std::ios::in | std::ios::binary);</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  <span class="keywordflow">if</span>(!file.good())</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  {</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Could not load binary data: "</span> + path);</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  }</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span> </div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> d = 0; d < tensor.shape().num_dimensions(); ++d)</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  {</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(d, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(0, tensor.shape()[d], 1));</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  }</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>  <a class="code" href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">execute_window_loop</a>(window, [&](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & <span class="keywordtype">id</span>)</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  {</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  <span class="keywordtype">float</span> val;</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  file.read(reinterpret_cast<char *>(&val), <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>));</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</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="l00649"></a><span class="lineno"> 649</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a1e6934e95738573214c2ce1d6648d116">store_value_with_data_type</a>(out_ptr, val, tensor.data_type());</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  });</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span> }</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span> } <span class="comment">// namespace test</span></div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span> } <span class="comment">// namespace arm_compute</span></div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span> <span class="preprocessor">#endif</span></div><div class="ttc" id="_error_8h_xhtml_a05b19c75afe9c24200a62b9724734bbd"><div class="ttname"><a href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR(...)</div><div class="ttdoc">Print the given message then throw an std::runtime_error. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00031">Error.h:31</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">Simple tensor object that stores elements in a consecutive chunk of memory. </div><div class="ttdef"><b>Definition:</b> <a href="_raw_tensor_8h_source.xhtml#l00047">RawTensor.h:47</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#l00040">TensorCache.h:40</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#l00038">TensorShape.h:38</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_raw_tensor_xhtml_a448f57f9d6aec61b3d85b898affe4a2e"><div class="ttname"><a href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a448f57f9d6aec61b3d85b898affe4a2e">arm_compute::test::RawTensor::element_size</a></div><div class="ttdeci">size_t element_size() const </div><div class="ttdoc">Size of each element in the tensor in bytes. </div><div class="ttdef"><b>Definition:</b> <a href="_raw_tensor_8cpp_source.xhtml#l00091">RawTensor.cpp:91</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">arm_compute::DataType::QS8</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_tensor_library_xhtml_a65b3f12d28af30bbb0d6cf75e7c4c316"><div class="ttname"><a href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a65b3f12d28af30bbb0d6cf75e7c4c316">arm_compute::test::TensorLibrary::TensorLibrary</a></div><div class="ttdeci">TensorLibrary(std::string path)</div><div class="ttdoc">Initialises the library with a path to the image directory. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_library_8cpp_source.xhtml#l00208">TensorLibrary.cpp:208</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_tensor_library_xhtml_a4035a1140831801ced5dfa1d9fe6988a"><div class="ttname"><a href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a4035a1140831801ced5dfa1d9fe6988a">arm_compute::test::TensorLibrary::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="_tensor_library_8cpp_source.xhtml#l00218">TensorLibrary.cpp:218</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#l00381">Utils.h:381</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">Unknown image format. </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_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F16 per channel </div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_tensor_library_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_tensor_library.xhtml">arm_compute::test::TensorLibrary</a></div><div class="ttdoc">Factory class to create and fill tensors. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_library_8h_source.xhtml#l00056">TensorLibrary.h:56</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#l00124">Error.h:124</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&#39;s dimensions with a start, end and step. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00068">Window.h:68</a></div></div> |
| <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 S16 per channel </div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_raw_tensor_xhtml_a13761831550669f43f4edee978181c46"><div class="ttname"><a href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a13761831550669f43f4edee978181c46">arm_compute::test::RawTensor::shape</a></div><div class="ttdeci">TensorShape shape() const </div><div class="ttdoc">Shape of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_raw_tensor_8cpp_source.xhtml#l00086">RawTensor.cpp:86</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="helpers_8h_xhtml_a009469e4d9b8fce3b6d5e97d2077827d"><div class="ttname"><a href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a></div><div class="ttdeci">__global uchar * offset(const Image *img, int x, int y)</div><div class="ttdoc">Get the pointer position of a Image. </div><div class="ttdef"><b>Definition:</b> <a href="helpers_8h_source.xhtml#l00201">helpers.h:201</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_raw_tensor_xhtml_aac782da1f912bceb5d8ad00c8dc892ac"><div class="ttname"><a href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#aac782da1f912bceb5d8ad00c8dc892ac">arm_compute::test::RawTensor::size</a></div><div class="ttdeci">size_t size() const </div><div class="ttdoc">Total size of the tensor in bytes. </div><div class="ttdef"><b>Definition:</b> <a href="_raw_tensor_8cpp_source.xhtml#l00101">RawTensor.cpp:101</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdef"><b>Definition:</b> <a href="01__library_8dox_source.xhtml#l00001">01_library.dox:1</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 U32 per channel </div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_raw_tensor_xhtml_a9c3b791dba4a4cff3785264b9260e9d5"><div class="ttname"><a href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a9c3b791dba4a4cff3785264b9260e9d5">arm_compute::test::RawTensor::BufferType</a></div><div class="ttdeci">uint8_t BufferType</div><div class="ttdef"><b>Definition:</b> <a href="_raw_tensor_8h_source.xhtml#l00081">RawTensor.h:81</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 U16 per channel </div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">arm_compute::DataType::S64</a></div></div> |
| <div class="ttc" id="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#l00526">Utils.h:526</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> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_tensor_library_xhtml_ad85dc4c57a27c44d114c573b9a80bad6"><div class="ttname"><a href="classarm__compute_1_1test_1_1_tensor_library.xhtml#ad85dc4c57a27c44d114c573b9a80bad6">arm_compute::test::TensorLibrary::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 binary in specified path. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_library_8h_source.xhtml#l00621">TensorLibrary.h:621</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">arm_compute::Format::U32</a></div><div class="ttdoc">1 channel, 1 S32 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#l00304">Types.h:304</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#l00038">Types.h:38</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a6c0dcc38187027dcb89cd9724bc5a823"><div class="ttname"><a href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">arm_compute::execute_window_loop</a></div><div class="ttdeci">void execute_window_loop(const Window &w, L &&lambda_function, Ts &&...iterators)</div><div class="ttdoc">Iterate through the passed window, automatically adjusting the iterators and calling the lambda_funct...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00176">Helpers.inl:176</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_coordinates_xhtml"><div class="ttname"><a href="classarm__compute_1_1_coordinates.xhtml">arm_compute::Coordinates</a></div><div class="ttdoc">Coordinates of an item. </div><div class="ttdef"><b>Definition:</b> <a href="_coordinates_8h_source.xhtml#l00037">Coordinates.h:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_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#l00040">Window.inl:40</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#l00611">Utils.h:611</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="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 U8 per channel </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="_coordinates_8h_xhtml"><div class="ttname"><a href="_coordinates_8h.xhtml">Coordinates.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_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_ad91bb73431b4de1f4946ed949d444849"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">arm_compute::test::fixed_point_arithmetic::detail::max</a></div><div class="ttdeci">fixed_point< T > max(fixed_point< T > x, fixed_point< T > y)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00880">FixedPoint.h:880</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">arm_compute::DataType::F64</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_raw_tensor_xhtml_a7b53deaf986aa58ffa0090cc241dec64"><div class="ttname"><a href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a7b53deaf986aa58ffa0090cc241dec64">arm_compute::test::RawTensor::data</a></div><div class="ttdeci">const BufferType * data() const </div><div class="ttdoc">Constant pointer to the underlying buffer. </div><div class="ttdef"><b>Definition:</b> <a href="_raw_tensor_8cpp_source.xhtml#l00148">RawTensor.cpp:148</a></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> |
| <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#l00060">Types.h:60</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">arm_compute::DataType::S8</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_tensor_library_xhtml_abb6f25295592e886976520216187eed7"><div class="ttname"><a href="classarm__compute_1_1test_1_1_tensor_library.xhtml#abb6f25295592e886976520216187eed7">arm_compute::test::TensorLibrary::fill_tensor_uniform</a></div><div class="ttdeci">void fill_tensor_uniform(T &&tensor, std::random_device::result_type seed_offset) const </div><div class="ttdoc">Fill a tensor with uniform distribution across the range of its type. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_library_8h_source.xhtml#l00437">TensorLibrary.h:437</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml">arm_compute::Window</a></div><div class="ttdoc">Describe a multidimensional execution window. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00039">Window.h:39</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_tensor_library_xhtml_a80a7b5ae084bf22b91bc5f68a06711c0"><div class="ttname"><a href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">arm_compute::test::TensorLibrary::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="_tensor_library_8h_source.xhtml#l00368">TensorLibrary.h:368</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_raw_tensor_xhtml_a45cc7b9a37aa9f0e7d479248a27e1f58"><div class="ttname"><a href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a45cc7b9a37aa9f0e7d479248a27e1f58">arm_compute::test::RawTensor::data_type</a></div><div class="ttdeci">DataType data_type() const </div><div class="ttdoc">Data type of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_raw_tensor_8cpp_source.xhtml#l00112">RawTensor.cpp:112</a></div></div> |
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