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<div class="title">TensorShape.h</div> </div>
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<a href="_tensor_shape_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>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment"> * Copyright (c) 2016-2018 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;<span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;<span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;<span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;<span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;<span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;<span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160;<span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;<span class="preprocessor">#ifndef __ARM_COMPUTE_TENSORSHAPE_H__</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;<span class="preprocessor">#define __ARM_COMPUTE_TENSORSHAPE_H__</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_dimensions_8h.xhtml">arm_compute/core/Dimensions.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_error_8h.xhtml">arm_compute/core/Error.h</a>&quot;</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_utility_8h.xhtml">arm_compute/core/utils/misc/Utility.h</a>&quot;</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;<span class="preprocessor">#include &lt;algorithm&gt;</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160;<span class="preprocessor">#include &lt;array&gt;</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;<span class="preprocessor">#include &lt;functional&gt;</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160;<span class="preprocessor">#include &lt;numeric&gt;</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160;{</div><div class="line"><a name="l00039"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_shape.xhtml"> 39</a></span>&#160;<span class="keyword">class </span><a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1_dimensions.xhtml">Dimensions</a>&lt;size_t&gt;</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160;{</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; <span class="keyword">template</span> &lt;<span class="keyword">typename</span>... Ts&gt;</div><div class="line"><a name="l00047"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_shape.xhtml#a091252b04a1c79d499dd6184f9f5d715"> 47</a></span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a091252b04a1c79d499dd6184f9f5d715">TensorShape</a>(Ts... dims)</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; : <a class="code" href="classarm__compute_1_1_dimensions.xhtml">Dimensions</a>{ dims... }</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; {</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; <span class="comment">// Initialize unspecified dimensions to 1</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; <span class="keywordflow">if</span>(_num_dimensions &gt; 0)</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; {</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; std::fill(_id.begin() + _num_dimensions, _id.end(), 1);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; }</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; <span class="comment">// Correct number dimensions to ignore trailing dimensions of size 1</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; apply_dimension_correction();</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; }</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a091252b04a1c79d499dd6184f9f5d715">TensorShape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a091252b04a1c79d499dd6184f9f5d715">TensorShape</a> &amp;) = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a091252b04a1c79d499dd6184f9f5d715">TensorShape</a> &amp;<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a355b1a84ab7af3b8ef9a6bea1939450a">operator=</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a091252b04a1c79d499dd6184f9f5d715">TensorShape</a> &amp;) = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a091252b04a1c79d499dd6184f9f5d715">TensorShape</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a091252b04a1c79d499dd6184f9f5d715">TensorShape</a> &amp;&amp;) = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a091252b04a1c79d499dd6184f9f5d715">TensorShape</a> &amp;<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a355b1a84ab7af3b8ef9a6bea1939450a">operator=</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a091252b04a1c79d499dd6184f9f5d715">TensorShape</a> &amp;&amp;) = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a29ff524f0e3378fb25a8447bdeed6ba9">~TensorShape</a>() = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160;</div><div class="line"><a name="l00078"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad"> 78</a></span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(<span class="keywordtype">size_t</span> dimension, <span class="keywordtype">size_t</span> value, <span class="keywordtype">bool</span> apply_dim_correction = <span class="keyword">true</span>)</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; {</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; <span class="comment">// Clear entire shape if one dimension is zero</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; <span class="keywordflow">if</span>(value == 0)</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; {</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; _num_dimensions = 0;</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; std::fill(_id.begin(), _id.end(), 0);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; }</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; {</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; <span class="comment">// Make sure all empty dimensions are filled with 1</span></div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; std::fill(_id.begin() + _num_dimensions, _id.end(), 1);</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160;</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; <span class="comment">// Set the specified dimension and increase the number of dimensions if</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; <span class="comment">// necessary</span></div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; <a class="code" href="classarm__compute_1_1_dimensions.xhtml#a982730e6f0da5f9490f59bc5f6bb3f27">Dimensions::set</a>(dimension, value);</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160;</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; <span class="comment">// Correct number dimensions to ignore trailing dimensions of size 1</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; <span class="keywordflow">if</span>(apply_dim_correction)</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; {</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; apply_dimension_correction();</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; }</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; }</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; }</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160;</div><div class="line"><a name="l00110"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_shape.xhtml#acb74edf42335de0dca0da5158b704c4b"> 110</a></span>&#160; <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#acb74edf42335de0dca0da5158b704c4b">remove_dimension</a>(<span class="keywordtype">size_t</span> n)</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; {</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(_num_dimensions &lt; 1);</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(n &gt;= _num_dimensions);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160;</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#ad9000ce99b9ffcec5722cade36d7e757">std::copy</a>(_id.begin() + n + 1, _id.end(), _id.begin() + n);</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160;</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; <span class="comment">// Reduce number of dimensions</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; _num_dimensions--;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160;</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <span class="comment">// Make sure all empty dimensions are filled with 1</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; std::fill(_id.begin() + _num_dimensions, _id.end(), 1);</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; <span class="comment">// Correct number dimensions to ignore trailing dimensions of size 1</span></div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; apply_dimension_correction();</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; }</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160;</div><div class="line"><a name="l00132"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_shape.xhtml#a8e15e87871211f98c2b566137e38ef99"> 132</a></span>&#160; <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a8e15e87871211f98c2b566137e38ef99">collapse</a>(<span class="keywordtype">size_t</span> n, <span class="keywordtype">size_t</span> first = 0)</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; {</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; <a class="code" href="classarm__compute_1_1_dimensions.xhtml#a0c265a91027decdda59e5086c550d0f7">Dimensions::collapse</a>(n, first);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; <span class="comment">// Make sure all empty dimensions are filled with 1</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; std::fill(_id.begin() + _num_dimensions, _id.end(), 1);</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; }</div><div class="line"><a name="l00143"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_shape.xhtml#ab6d90bb06b3c19db6aba94975be64d10"> 143</a></span>&#160; <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#ab6d90bb06b3c19db6aba94975be64d10">shift_right</a>(<span class="keywordtype">size_t</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a75b73e17c4ebe901e44af3b2b9846ab3">step</a>)</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; {</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a75b73e17c4ebe901e44af3b2b9846ab3">step</a> &gt; <a class="code" href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">TensorShape::num_max_dimensions</a> - <a class="code" href="classarm__compute_1_1_dimensions.xhtml#a80a5f2d6e3a697c9aad893a3b4242615">num_dimensions</a>());</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; std::rotate(<a class="code" href="classarm__compute_1_1_dimensions.xhtml#ab2878b67ca384a699c1270900b31290b">begin</a>(), <a class="code" href="classarm__compute_1_1_dimensions.xhtml#ab2878b67ca384a699c1270900b31290b">begin</a>() + <a class="code" href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">TensorShape::num_max_dimensions</a> - <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a75b73e17c4ebe901e44af3b2b9846ab3">step</a>, <a class="code" href="classarm__compute_1_1_dimensions.xhtml#ac684b52c6197edff9cccb3abd1e41f59">end</a>());</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; _num_dimensions += <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a75b73e17c4ebe901e44af3b2b9846ab3">step</a>;</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160;</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160; <span class="comment">// Correct number dimensions to ignore trailing dimensions of size 1</span></div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; apply_dimension_correction();</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; }</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160;</div><div class="line"><a name="l00160"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_shape.xhtml#af0d754a1bb2ae68b5d2f1aacc1794817"> 160</a></span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#af0d754a1bb2ae68b5d2f1aacc1794817">collapsed_from</a>(<span class="keywordtype">size_t</span> start)<span class="keyword"> const</span></div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160;<span class="keyword"> </span>{</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#ad9000ce99b9ffcec5722cade36d7e757">copy</a>(*<span class="keyword">this</span>);</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#ad9000ce99b9ffcec5722cade36d7e757">copy</a>.collapse(<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a80a5f2d6e3a697c9aad893a3b4242615">num_dimensions</a>() - start, start);</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#ad9000ce99b9ffcec5722cade36d7e757">copy</a>;</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; }</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160;</div><div class="line"><a name="l00171"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_shape.xhtml#a0fdcb923dfd4c74858cc2bc326321efb"> 171</a></span>&#160; <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a0fdcb923dfd4c74858cc2bc326321efb">total_size</a>()<span class="keyword"> const</span></div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160;<span class="keyword"> </span>{</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; <span class="keywordflow">return</span> <a class="code" href="accumulate_8cl.xhtml#a00e540076dd545ad59ac7482f8cdf514">std::accumulate</a>(_id.begin(), _id.end(), 1, std::multiplies&lt;size_t&gt;());</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; }</div><div class="line"><a name="l00181"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_shape.xhtml#a99e09337e5b6ef762cd1f2d0bd10c346"> 181</a></span>&#160; <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a99e09337e5b6ef762cd1f2d0bd10c346">total_size_upper</a>(<span class="keywordtype">size_t</span> dimension)<span class="keyword"> const</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160;<span class="keyword"> </span>{</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(dimension &gt;= <a class="code" href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">TensorShape::num_max_dimensions</a>);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; <span class="keywordflow">return</span> <a class="code" href="accumulate_8cl.xhtml#a00e540076dd545ad59ac7482f8cdf514">std::accumulate</a>(_id.begin() + dimension, _id.end(), 1, std::multiplies&lt;size_t&gt;());</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; }</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160;</div><div class="line"><a name="l00193"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_shape.xhtml#a9de66247a88337d636d536a8bf086571"> 193</a></span>&#160; <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9de66247a88337d636d536a8bf086571">total_size_lower</a>(<span class="keywordtype">size_t</span> dimension)<span class="keyword"> const</span></div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160;<span class="keyword"> </span>{</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(dimension &gt; <a class="code" href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">TensorShape::num_max_dimensions</a>);</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; <span class="keywordflow">return</span> <a class="code" href="accumulate_8cl.xhtml#a00e540076dd545ad59ac7482f8cdf514">std::accumulate</a>(_id.begin(), _id.begin() + dimension, 1, std::multiplies&lt;size_t&gt;());</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; }</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; <span class="keyword">template</span> &lt;<span class="keyword">typename</span>... Shapes&gt;</div><div class="line"><a name="l00210"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_shape.xhtml#a244a32cac2f5011bd0fc49700bf3d5de"> 210</a></span>&#160; <span class="keyword">static</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a244a32cac2f5011bd0fc49700bf3d5de">broadcast_shape</a>(<span class="keyword">const</span> Shapes &amp;... shapes)</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; {</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> bc_shape;</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160;</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>&#160; <span class="keyword">auto</span> broadcast = [&amp;bc_shape](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp; other)</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160; {</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160; <span class="keywordflow">if</span>(bc_shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a80a5f2d6e3a697c9aad893a3b4242615">num_dimensions</a>() == 0)</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160; {</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; bc_shape = other;</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160; }</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160; <span class="keywordflow">else</span> <span class="keywordflow">if</span>(other.num_dimensions() != 0)</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160; {</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> d = 0; d &lt; <a class="code" href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">TensorShape::num_max_dimensions</a>; ++d)</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; {</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160; <span class="keyword">const</span> <span class="keywordtype">size_t</span> dim_min = std::min(bc_shape[d], other[d]);</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160; <span class="keyword">const</span> <span class="keywordtype">size_t</span> dim_max = std::max(bc_shape[d], other[d]);</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160;</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160; <span class="keywordflow">if</span>((dim_min != 1) &amp;&amp; (dim_min != dim_max))</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; {</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; bc_shape = <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>{ 0<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a> };</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160; }</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160;</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160; bc_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(d, dim_max);</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160; }</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160; }</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160; };</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>&#160;</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160; <a class="code" href="namespacearm__compute_1_1utility.xhtml#a067ebd28103d827b6ec17032e2344064">utility::for_each</a>(broadcast, shapes...);</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>&#160; <span class="keywordflow">return</span> bc_shape;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160; }</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160;</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160; <span class="keywordtype">void</span> apply_dimension_correction()</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160; {</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = static_cast&lt;int&gt;(_num_dimensions) - 1; i &gt; 0; --i)</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>&#160; {</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160; <span class="keywordflow">if</span>(_id[i] == 1)</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>&#160; {</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160; --_num_dimensions;</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>&#160; }</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160; {</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>&#160; }</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>&#160; }</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>&#160; }</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160;};</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>&#160;}</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>&#160;<span class="preprocessor">#endif </span><span class="comment">/*__ARM_COMPUTE_TENSORSHAPE_H__*/</span><span class="preprocessor"></span></div><div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_ab6d90bb06b3c19db6aba94975be64d10"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#ab6d90bb06b3c19db6aba94975be64d10">arm_compute::TensorShape::shift_right</a></div><div class="ttdeci">void shift_right(size_t step)</div><div class="ttdoc">Shifts right the tensor shape increasing its dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00143">TensorShape.h:143</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_acb74edf42335de0dca0da5158b704c4b"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#acb74edf42335de0dca0da5158b704c4b">arm_compute::TensorShape::remove_dimension</a></div><div class="ttdeci">void remove_dimension(size_t n)</div><div class="ttdoc">Accessor to remove the dimension n from the tensor shape.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00110">TensorShape.h:110</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_af0d754a1bb2ae68b5d2f1aacc1794817"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#af0d754a1bb2ae68b5d2f1aacc1794817">arm_compute::TensorShape::collapsed_from</a></div><div class="ttdeci">TensorShape collapsed_from(size_t start) const</div><div class="ttdoc">Return a copy with collapsed dimensions starting from a given point.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00160">TensorShape.h:160</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a355b1a84ab7af3b8ef9a6bea1939450a"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a355b1a84ab7af3b8ef9a6bea1939450a">arm_compute::TensorShape::operator=</a></div><div class="ttdeci">TensorShape &amp; operator=(const TensorShape &amp;)=default</div><div class="ttdoc">Allow instances of this class to be copied.</div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a244a32cac2f5011bd0fc49700bf3d5de"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a244a32cac2f5011bd0fc49700bf3d5de">arm_compute::TensorShape::broadcast_shape</a></div><div class="ttdeci">static TensorShape broadcast_shape(const Shapes &amp;... shapes)</div><div class="ttdoc">If shapes are broadcast compatible, return the broadcasted shape.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00210">TensorShape.h:210</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00337">Error.h:337</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a99e09337e5b6ef762cd1f2d0bd10c346"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a99e09337e5b6ef762cd1f2d0bd10c346">arm_compute::TensorShape::total_size_upper</a></div><div class="ttdeci">size_t total_size_upper(size_t dimension) const</div><div class="ttdoc">Collapses given dimension and above.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00181">TensorShape.h:181</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_ad9000ce99b9ffcec5722cade36d7e757"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#ad9000ce99b9ffcec5722cade36d7e757">arm_compute::test::validation::reference::copy</a></div><div class="ttdeci">SimpleTensor&lt; T &gt; copy(const SimpleTensor&lt; T &gt; &amp;src, const TensorShape &amp;output_shape)</div><div class="ttdef"><b>Definition:</b> <a href="reference_2_copy_8cpp_source.xhtml#l00037">Copy.cpp:37</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a982730e6f0da5f9490f59bc5f6bb3f27"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a982730e6f0da5f9490f59bc5f6bb3f27">arm_compute::Dimensions::set</a></div><div class="ttdeci">void set(size_t dimension, T value)</div><div class="ttdoc">Accessor to set the value of one of the dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00074">Dimensions.h:74</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a9de66247a88337d636d536a8bf086571"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a9de66247a88337d636d536a8bf086571">arm_compute::TensorShape::total_size_lower</a></div><div class="ttdeci">size_t total_size_lower(size_t dimension) const</div><div class="ttdoc">Compute size of dimensions lower than the given one.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00193">TensorShape.h:193</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a0c265a91027decdda59e5086c550d0f7"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a0c265a91027decdda59e5086c550d0f7">arm_compute::Dimensions::collapse</a></div><div class="ttdeci">void collapse(const size_t n, const size_t first=0)</div><div class="ttdoc">Collapse dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00138">Dimensions.h:138</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2018 ARM Limited.</div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a75b73e17c4ebe901e44af3b2b9846ab3"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a75b73e17c4ebe901e44af3b2b9846ab3">arm_compute::test::validation::step</a></div><div class="ttdeci">const int step</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_select_8cpp_source.xhtml#l00172">Select.cpp:172</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a091252b04a1c79d499dd6184f9f5d715"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a091252b04a1c79d499dd6184f9f5d715">arm_compute::TensorShape::TensorShape</a></div><div class="ttdeci">TensorShape(Ts... dims)</div><div class="ttdoc">Constructor to initialize the tensor shape.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00047">TensorShape.h:47</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">arm_compute::Channel::U</a></div><div class="ttdoc">Cb/U channel.</div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a0fdcb923dfd4c74858cc2bc326321efb"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a0fdcb923dfd4c74858cc2bc326321efb">arm_compute::TensorShape::total_size</a></div><div class="ttdeci">size_t total_size() const</div><div class="ttdoc">Collapses all dimensions to a single linear total size.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00171">TensorShape.h:171</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml">arm_compute::Dimensions</a></div><div class="ttdoc">Dimensions with dimensionality.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00041">Dimensions.h:41</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_ab2878b67ca384a699c1270900b31290b"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#ab2878b67ca384a699c1270900b31290b">arm_compute::Dimensions&lt; size_t &gt;::begin</a></div><div class="ttdeci">std::array&lt; size_t, num_max_dimensions &gt;::iterator begin()</div><div class="ttdoc">Returns a read/write iterator that points to the first element in the dimension array.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00173">Dimensions.h:173</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_1utility_xhtml_a067ebd28103d827b6ec17032e2344064"><div class="ttname"><a href="namespacearm__compute_1_1utility.xhtml#a067ebd28103d827b6ec17032e2344064">arm_compute::utility::for_each</a></div><div class="ttdeci">void for_each(F &amp;&amp;)</div><div class="ttdoc">Base case of for_each.</div><div class="ttdef"><b>Definition:</b> <a href="_utility_8h_source.xhtml#l00093">Utility.h:93</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_ac684b52c6197edff9cccb3abd1e41f59"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#ac684b52c6197edff9cccb3abd1e41f59">arm_compute::Dimensions&lt; size_t &gt;::end</a></div><div class="ttdeci">std::array&lt; size_t, num_max_dimensions &gt;::iterator end()</div><div class="ttdoc">Returns a read/write iterator that points one past the last element in the dimension array.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00197">Dimensions.h:197</a></div></div>
<div class="ttc" id="_dimensions_8h_xhtml"><div class="ttname"><a href="_dimensions_8h.xhtml">Dimensions.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a80a5f2d6e3a697c9aad893a3b4242615"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a80a5f2d6e3a697c9aad893a3b4242615">arm_compute::Dimensions&lt; size_t &gt;::num_dimensions</a></div><div class="ttdeci">unsigned int num_dimensions() const</div><div class="ttdoc">Returns the effective dimensionality of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00122">Dimensions.h:122</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a9c54fb6cea3557692fe7c00c40bb40ad"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">arm_compute::TensorShape::set</a></div><div class="ttdeci">TensorShape &amp; set(size_t dimension, size_t value, bool apply_dim_correction=true)</div><div class="ttdoc">Accessor to set the value of one of the dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00078">TensorShape.h:78</a></div></div>
<div class="ttc" id="accumulate_8cl_xhtml_a00e540076dd545ad59ac7482f8cdf514"><div class="ttname"><a href="accumulate_8cl.xhtml#a00e540076dd545ad59ac7482f8cdf514">accumulate</a></div><div class="ttdeci">__kernel void accumulate(__global uchar *input_ptr, uint input_stride_x, uint input_step_x, uint input_stride_y, uint input_step_y, uint input_offset_first_element_in_bytes, __global uchar *accu_ptr, uint accu_stride_x, uint accu_step_x, uint accu_stride_y, uint accu_step_y, uint accu_offset_first_element_in_bytes)</div><div class="ttdoc">This function accumulates an input image into output image.</div><div class="ttdef"><b>Definition:</b> <a href="accumulate_8cl_source.xhtml#l00041">accumulate.cl:41</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a29ff524f0e3378fb25a8447bdeed6ba9"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a29ff524f0e3378fb25a8447bdeed6ba9">arm_compute::TensorShape::~TensorShape</a></div><div class="ttdeci">~TensorShape()=default</div><div class="ttdoc">Default destructor.</div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a1b67d5b720119d50faa286c774579ecc"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">arm_compute::Dimensions&lt; size_t &gt;::num_max_dimensions</a></div><div class="ttdeci">static constexpr size_t num_max_dimensions</div><div class="ttdoc">Number of dimensions the tensor has.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00045">Dimensions.h:45</a></div></div>
<div class="ttc" id="_utility_8h_xhtml"><div class="ttname"><a href="_utility_8h.xhtml">Utility.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a8e15e87871211f98c2b566137e38ef99"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a8e15e87871211f98c2b566137e38ef99">arm_compute::TensorShape::collapse</a></div><div class="ttdeci">void collapse(size_t n, size_t first=0)</div><div class="ttdoc">Collapse the first n dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00132">TensorShape.h:132</a></div></div>
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