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| <a href="_tensor_info_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) 2016-2019 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#ifndef ARM_COMPUTE_TENSORINFO_H</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#define ARM_COMPUTE_TENSORINFO_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="_i_tensor_info_8h.xhtml">arm_compute/core/ITensorInfo.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> </div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="_i_tensor_info_8h.xhtml">ITensorInfo.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</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="l00031"></a><span class="lineno"> 31</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="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="_strides_8h.xhtml">arm_compute/core/Strides.h</a>"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</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="l00034"></a><span class="lineno"> 34</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="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_utils_8h.xhtml">arm_compute/core/Utils.h</a>"</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#include <cstddef></span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="preprocessor">#include <memory></span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> {</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="keyword">class </span>HOGInfo;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="comment"></span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="comment">/** Store the tensor's metadata */</span></div><div class="line"><a name="l00045"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml"> 45</a></span> <span class="keyword">class </span><a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> final : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> {</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="keyword">public</span>:<span class="comment"></span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="comment"> /** Default constructor */</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a21c2ae9fa438faf42669dadda628080c">TensorInfo</a>();<span class="comment"></span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="comment"> /** Default destructor */</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a4eb5913c3ce5fe2bcbaafd8c9224d384">~TensorInfo</a>() = <span class="keywordflow">default</span>;<span class="comment"></span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="comment"> /** Allow instances of this class to be copy constructed */</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a21c2ae9fa438faf42669dadda628080c">TensorInfo</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>);<span class="comment"></span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> <span class="comment"> /** Allow instances of this class to be copy constructed */</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a21c2ae9fa438faf42669dadda628080c">TensorInfo</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> &) = <span class="keywordflow">default</span>;<span class="comment"></span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> <span class="comment"> /** Allow instances of this class to be copied */</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#adcf156ba30ff118c28690671e83ea06b">operator=</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> &) = <span class="keywordflow">default</span>;<span class="comment"></span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> <span class="comment"> /** Allow instances of this class to be move constructed */</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a21c2ae9fa438faf42669dadda628080c">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> &&) = <span class="keywordflow">default</span>;<span class="comment"></span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> <span class="comment"> /** Allow instances of this class to be moved */</span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#adcf156ba30ff118c28690671e83ea06b">operator=</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> &&) = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> <span class="comment"></span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> <span class="comment"> /** Construct a tensor info with a format.</span></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> <span class="comment"> *</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> <span class="comment"> * Can be used for automatic derivation of the shape by the function.</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> <span class="comment"> *</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> <span class="comment"> * @param[in] format Format of the tensor.</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> <span class="comment"> */</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a21c2ae9fa438faf42669dadda628080c">TensorInfo</a>(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ac4b36cc1e56b0b7e579bb4b7196490db">format</a>);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> <span class="comment"></span></div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> <span class="comment"> /** 2D tensor constructor</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> <span class="comment"> *</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> <span class="comment"> * @param[in] width Width of the 2D tensor</span></div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> <span class="comment"> * @param[in] height Height of the 2D tensor</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> <span class="comment"> * @param[in] format Single plane format of the tensor.</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> <span class="comment"> */</span></div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a21c2ae9fa438faf42669dadda628080c">TensorInfo</a>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ac4b36cc1e56b0b7e579bb4b7196490db">format</a>);<span class="comment"></span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> <span class="comment"> /** Constructor</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> <span class="comment"> *</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <span class="comment"> * @param[in] tensor_shape It specifies the size for each dimension of the tensor in number of elements.</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <span class="comment"> * @param[in] format Single plane format of the tensor.</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> <span class="comment"> */</span></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a21c2ae9fa438faf42669dadda628080c">TensorInfo</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ac4b36cc1e56b0b7e579bb4b7196490db">format</a>);</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> <span class="comment"></span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> <span class="comment"> /** Construct a tensor info with a data type and number of channels.</span></div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> <span class="comment"> *</span></div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> <span class="comment"> * Can be used for automatic derivation of the shape by the function.</span></div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> <span class="comment"> *</span></div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> <span class="comment"> * @param[in] num_channels It indicates the number of channels for each tensor element</span></div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> <span class="comment"> * @param[in] data_type Data type to use for each tensor element</span></div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> <span class="comment"> */</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a21c2ae9fa438faf42669dadda628080c">TensorInfo</a>(<span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a6f13b974eeb528acde66de8d9b3fd95c">num_channels</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> <span class="comment"></span></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> <span class="comment"> /** Constructor</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> <span class="comment"> *</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> <span class="comment"> * @param[in] tensor_shape It specifies the size for each dimension of the tensor in number of elements.</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> <span class="comment"> * @param[in] num_channels It indicates the number of channels for each tensor element</span></div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> <span class="comment"> * @param[in] data_type Data type to use for each tensor element</span></div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> <span class="comment"> */</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a21c2ae9fa438faf42669dadda628080c">TensorInfo</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>, <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a6f13b974eeb528acde66de8d9b3fd95c">num_channels</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> <span class="comment"></span></div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> <span class="comment"> /** Constructor</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> <span class="comment"> *</span></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> <span class="comment"> * @param[in] tensor_shape It specifies the size for each dimension of the tensor in number of elements.</span></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> <span class="comment"> * @param[in] num_channels It indicates the number of channels for each tensor element</span></div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> <span class="comment"> * @param[in] data_type Data type to use for each tensor element</span></div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> <span class="comment"> * @param[in] data_layout The data layout setting for the tensor data.</span></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> <span class="comment"> */</span></div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a21c2ae9fa438faf42669dadda628080c">TensorInfo</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>, <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a6f13b974eeb528acde66de8d9b3fd95c">num_channels</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a5f63b63606dbbbe54474e6e970a6738c">data_layout</a>);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> <span class="comment"></span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> <span class="comment"> /** Constructor</span></div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> <span class="comment"> *</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> <span class="comment"> * @param[in] tensor_shape It specifies the size for each dimension of the tensor in number of elements.</span></div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> <span class="comment"> * @param[in] num_channels It indicates the number of channels for each tensor element</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> <span class="comment"> * @param[in] data_type Data type to use for each tensor element</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> <span class="comment"> * @param[in] quantization_info The quantization settings for the tensor data.</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> <span class="comment"> */</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a21c2ae9fa438faf42669dadda628080c">TensorInfo</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>, <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a6f13b974eeb528acde66de8d9b3fd95c">num_channels</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>, <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ac74736e3863207232a23b7181c1d0f44">quantization_info</a>);</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> <span class="comment"></span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> <span class="comment"> /** Constructor</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> <span class="comment"> *</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> <span class="comment"> * @param[in] hog_info HOG's metadata used to allocate normalized HOG space</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> <span class="comment"> * @param[in] width Width of the 2D tensor where the HOG descriptor will be computed on</span></div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> <span class="comment"> * @param[in] height Height of the 2D tensor where the HOG descriptor will be computed on</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> <span class="comment"> */</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a21c2ae9fa438faf42669dadda628080c">TensorInfo</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_h_o_g_info.xhtml">HOGInfo</a> &hog_info, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height);</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> <span class="comment"></span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> <span class="comment"> /** Initialize the tensor info with just a format.</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> <span class="comment"> *</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> <span class="comment"> * Can be used for automatic derivation of the shape by the function.</span></div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> <span class="comment"> *</span></div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> <span class="comment"> * @param[in] format Single plane format of the tensor.</span></div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> <span class="comment"> */</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ad6b64f33be1e66dcf7612483ffb8fd63">init</a>(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ac4b36cc1e56b0b7e579bb4b7196490db">format</a>);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> <span class="comment"></span></div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> <span class="comment"> /** Initialize the metadata structure with the given parameters</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> <span class="comment"> *</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> <span class="comment"> * @param[in] tensor_shape Size for each dimension of the tensor in number of elements.</span></div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> <span class="comment"> * @param[in] format Single plane format of the tensor.</span></div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> <span class="comment"> */</span></div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ad6b64f33be1e66dcf7612483ffb8fd63">init</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ac4b36cc1e56b0b7e579bb4b7196490db">format</a>);<span class="comment"></span></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> <span class="comment"> /** Initialize the metadata structure with the given parameters</span></div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> <span class="comment"> *</span></div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> <span class="comment"> * @param[in] tensor_shape Size for each dimension of the tensor in number of elements.</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> <span class="comment"> * @param[in] format Single plane format of the tensor.</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> <span class="comment"> * @param[in] strides_in_bytes Stride in bytes for accessing each dimension of the tensor.</span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> <span class="comment"> * @param[in] offset_first_element_in_bytes Offset in bytes from the beginning of memory allocation to access the first element.</span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="comment"> * @param[in] total_size_in_bytes Size in bytes of the memory allocation (including the offset to the first element).</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> <span class="comment"> */</span></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ad6b64f33be1e66dcf7612483ffb8fd63">init</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ac4b36cc1e56b0b7e579bb4b7196490db">format</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_strides.xhtml">Strides</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a5f1ca9d674346287cae57a6c5b5c24ec">strides_in_bytes</a>, <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ab54246abe670b06f5624add7e7022904">offset_first_element_in_bytes</a>, <span class="keywordtype">size_t</span> total_size_in_bytes);</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> <span class="comment"></span></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> <span class="comment"> /** Initialize the tensor info with just a format.</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> <span class="comment"> *</span></div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> <span class="comment"> * Can be used for automatic derivation of the shape by the function.</span></div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> <span class="comment"> *</span></div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> <span class="comment"> * @param[in] num_channels Desired number of channels for each tensor element.</span></div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> <span class="comment"> * @param[in] data_type Data type to use for each tensor element.</span></div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> <span class="comment"> */</span></div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ad6b64f33be1e66dcf7612483ffb8fd63">init</a>(<span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a6f13b974eeb528acde66de8d9b3fd95c">num_channels</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>);</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> <span class="comment"></span></div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> <span class="comment"> /** Initialize the metadata structure with the given parameters</span></div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> <span class="comment"> *</span></div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span> <span class="comment"> * @param[in] tensor_shape Size for each dimension of the tensor in number of elements.</span></div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> <span class="comment"> * @param[in] num_channels Desired number of channels for each tensor element.</span></div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> <span class="comment"> * @param[in] data_type Data type to use for each tensor element.</span></div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> <span class="comment"> */</span></div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ad6b64f33be1e66dcf7612483ffb8fd63">init</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>, <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a6f13b974eeb528acde66de8d9b3fd95c">num_channels</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>);</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> <span class="comment"></span></div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> <span class="comment"> /** Initialize the metadata structure with the given parameters</span></div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> <span class="comment"> *</span></div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> <span class="comment"> * @param[in] tensor_shape Size for each dimension of the tensor in number of elements.</span></div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> <span class="comment"> * @param[in] num_channels Desired number of channels for each tensor element.</span></div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> <span class="comment"> * @param[in] data_type Data type to use for each tensor element.</span></div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> <span class="comment"> * @param[in] strides_in_bytes Stride in bytes for accessing each dimension of the tensor.</span></div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> <span class="comment"> * @param[in] offset_first_element_in_bytes Offset in bytes from the beginning of memory allocation to access the first element.</span></div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> <span class="comment"> * @param[in] total_size_in_bytes Size in bytes of the memory allocation (including the offset to the first element).</span></div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> <span class="comment"> */</span></div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ad6b64f33be1e66dcf7612483ffb8fd63">init</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>, <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a6f13b974eeb528acde66de8d9b3fd95c">num_channels</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_strides.xhtml">Strides</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a5f1ca9d674346287cae57a6c5b5c24ec">strides_in_bytes</a>, <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ab54246abe670b06f5624add7e7022904">offset_first_element_in_bytes</a>,</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keywordtype">size_t</span> total_size_in_bytes);<span class="comment"></span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> <span class="comment"> /** Initialize the metadata structure for the given HOG's metadata</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> <span class="comment"> *</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> <span class="comment"> * @param[in] hog_info HOG's metadata used to allocate normalized HOG space</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> <span class="comment"> * @param[in] width Width of the 2D tensor where the HOG descriptor will be computed on</span></div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> <span class="comment"> * @param[in] height Height of the 2D tensor where the HOG descriptor will be computed on</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> <span class="comment"> */</span></div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ad6b64f33be1e66dcf7612483ffb8fd63">init</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_h_o_g_info.xhtml">HOGInfo</a> &hog_info, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height);<span class="comment"></span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> <span class="comment"> /** Initialize the metadata structure for the given tensor shape and single-plane format, (Padding is automatically calculated)</span></div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> <span class="comment"> *</span></div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> <span class="comment"> * @note The padding used by this method is really conservative so that the tensor can be used for most functions.</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> <span class="comment"> *</span></div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> <span class="comment"> * @param[in] tensor_shape It specifies the size for each dimension of the tensor in number of elements</span></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> <span class="comment"> * @param[in] format Single plane format of the image.</span></div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> <span class="comment"> *</span></div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> <span class="comment"> * @return Total allocation size including padding in bytes.</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> <span class="comment"> */</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a4b7391b7025befbe44b743723feb4a9b">init_auto_padding</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ac4b36cc1e56b0b7e579bb4b7196490db">format</a>);<span class="comment"></span></div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> <span class="comment"> /** Initialize the metadata structure for the given tensor shape, number of channels and</span></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> <span class="comment"> * data type. (Padding is automatically calculated)</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> <span class="comment"> *</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> <span class="comment"> * @note The padding used by this method is really conservative so that the tensor can be used for most functions.</span></div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> <span class="comment"> *</span></div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> <span class="comment"> * @param[in] tensor_shape It specifies the size for each dimension of the tensor in number of elements</span></div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span> <span class="comment"> * @param[in] num_channels It indicates the number of channels for each tensor element</span></div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span> <span class="comment"> * @param[in] data_type Data type to use for each tensor element</span></div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> <span class="comment"> *</span></div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> <span class="comment"> * @return Total allocation size including padding in bytes.</span></div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> <span class="comment"> */</span></div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a4b7391b7025befbe44b743723feb4a9b">init_auto_padding</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>, <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a6f13b974eeb528acde66de8d9b3fd95c">num_channels</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>);<span class="comment"></span></div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> <span class="comment"> /** Initialize the metadata structure for the given HOG's metadata</span></div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> <span class="comment"> *</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> <span class="comment"> * @note init_auto_padding will be used for the tensor initialization.</span></div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> <span class="comment"> *</span></div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span> <span class="comment"> * @param[in] hog_info HOG's metadata used to allocate normalized HOG space</span></div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> <span class="comment"> * @param[in] width Width of the 2D tensor where the HOG descriptor will be computed on</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> <span class="comment"> * @param[in] height Height of the 2D tensor where the HOG descriptor will be computed on</span></div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span> <span class="comment"> *</span></div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> <span class="comment"> * @return Total allocation size including padding in bytes.</span></div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> <span class="comment"> */</span></div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a4b7391b7025befbe44b743723feb4a9b">init_auto_padding</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_h_o_g_info.xhtml">HOGInfo</a> &hog_info, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height);</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> </div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="comment">// Inherited methods overridden:</span></div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  std::unique_ptr<ITensorInfo> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#afbc359bde9be72a6edab175978e56662">clone</a>() <span class="keyword">const override</span>;</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a0a9053e6c4729ac5201b58068050c319">set_data_type</a>(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>) <span class="keyword">override</span>;</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ad03af3eeb6f3666d6282ca689c1b2ce8">set_num_channels</a>(<span class="keywordtype">int</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a6f13b974eeb528acde66de8d9b3fd95c">num_channels</a>) <span class="keyword">override</span>;</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a523a68398c1a8161daa4238c15e065fa">set_format</a>(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ac4b36cc1e56b0b7e579bb4b7196490db">format</a>) <span class="keyword">override</span>;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a2d7e8b8e05c3318b2d90c40d781745d2">set_tensor_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>) <span class="keyword">override</span>;</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a78839e7861ba8ffed52ca55da2745761">set_quantization_info</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ac74736e3863207232a23b7181c1d0f44">quantization_info</a>) <span class="keyword">override</span>;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a70b6e1495b94818cce4981dbac6bdd66">set_data_layout</a>(<span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a5f63b63606dbbbe54474e6e970a6738c">data_layout</a>) <span class="keyword">override</span>;</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a5a80b3a6ae624417617d6a8d3209add5">reset_padding</a>() <span class="keyword">override</span>;</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a907f837b924945ad1981c8fe8eca61e4">auto_padding</a>() <span class="keyword">override</span>;</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#af53d8203ecc37896ca4579d1ee3fdffc">extend_padding</a>(<span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_border_size.xhtml">PaddingSize</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a79e20eacb1e963e24a21ebd7369effd7">padding</a>) <span class="keyword">override</span>;</div><div class="line"><a name="l00232"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97"> 232</a></span>  <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(<span class="keywordtype">size_t</span> index)<span class="keyword"> const override</span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">return</span> _tensor_shape[index];</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  }</div><div class="line"><a name="l00236"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#a650247f9a828d1ef60135b01f8f77765"> 236</a></span>  <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a650247f9a828d1ef60135b01f8f77765">dimension</a>(<a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02">DataLayoutDimension</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>)<span class="keyword"> const override</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(_data_layout, <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>);</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  }</div><div class="line"><a name="l00240"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#a5f1ca9d674346287cae57a6c5b5c24ec"> 240</a></span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_strides.xhtml">Strides</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a5f1ca9d674346287cae57a6c5b5c24ec">strides_in_bytes</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keywordflow">return</span> _strides_in_bytes;</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  }</div><div class="line"><a name="l00244"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#ab54246abe670b06f5624add7e7022904"> 244</a></span>  <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ab54246abe670b06f5624add7e7022904">offset_first_element_in_bytes</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keywordflow">return</span> _offset_first_element_in_bytes;</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  }</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  int32_t <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a7888888b7f18215ae83fd3660d38eccb">offset_element_in_bytes</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> &pos) <span class="keyword">const override</span>;</div><div class="line"><a name="l00249"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#a62b67b578f684c4d516843c9dea86a23"> 249</a></span>  <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a62b67b578f684c4d516843c9dea86a23">element_size</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute.xhtml#abb7e0f23a4f2e63f39433f158dad47ab">data_size_from_type</a>(_data_type) * _num_channels;</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  }</div><div class="line"><a name="l00253"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#a13622133d9b41900a6a3e8f89e59a78b"> 253</a></span>  <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a13622133d9b41900a6a3e8f89e59a78b">num_dimensions</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="keywordflow">return</span> _tensor_shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a80a5f2d6e3a697c9aad893a3b4242615">num_dimensions</a>();</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  }</div><div class="line"><a name="l00257"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#a6f13b974eeb528acde66de8d9b3fd95c"> 257</a></span>  <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a6f13b974eeb528acde66de8d9b3fd95c">num_channels</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keywordflow">return</span> _num_channels;</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  }</div><div class="line"><a name="l00261"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e"> 261</a></span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">tensor_shape</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="keywordflow">return</span> _tensor_shape;</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  }</div><div class="line"><a name="l00265"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881"> 265</a></span>  <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keywordflow">return</span> _data_type;</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  }</div><div class="line"><a name="l00269"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#ac4b36cc1e56b0b7e579bb4b7196490db"> 269</a></span>  <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ac4b36cc1e56b0b7e579bb4b7196490db">format</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keywordflow">return</span> _format;</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  }</div><div class="line"><a name="l00273"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#af398466b602a02b42d8df19fb66a6c60"> 273</a></span>  <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#af398466b602a02b42d8df19fb66a6c60">total_size</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keywordflow">return</span> _total_size;</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  }</div><div class="line"><a name="l00277"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#a79e20eacb1e963e24a21ebd7369effd7"> 277</a></span>  <a class="code" href="structarm__compute_1_1_border_size.xhtml">PaddingSize</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a79e20eacb1e963e24a21ebd7369effd7">padding</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keywordflow">return</span> _padding;</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  }</div><div class="line"><a name="l00281"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#aa2ca251f99c56767719e991a26371603"> 281</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#aa2ca251f99c56767719e991a26371603">has_padding</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="keywordflow">return</span> !_padding.<a class="code" href="structarm__compute_1_1_border_size.xhtml#afaafdfc441c2433c70959e3dfe46fd97">empty</a>();</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  }</div><div class="line"><a name="l00285"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#aaee6555ace43b03173844b1a228a3fc3"> 285</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#aaee6555ace43b03173844b1a228a3fc3">is_resizable</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="keywordflow">return</span> _is_resizable;</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  }</div><div class="line"><a name="l00289"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#a7e890c9c5d4143d64a83b4ac19f4d3e4"> 289</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a7e890c9c5d4143d64a83b4ac19f4d3e4">is_dynamic</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="keywordflow">return</span> _is_dynamic;</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  }</div><div class="line"><a name="l00293"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#a78bf77b2d9b959259f77a32b9a412184"> 293</a></span>  <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a78bf77b2d9b959259f77a32b9a412184">set_is_resizable</a>(<span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#aaee6555ace43b03173844b1a228a3fc3">is_resizable</a>)<span class="keyword"> override</span></div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  _is_resizable = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#aaee6555ace43b03173844b1a228a3fc3">is_resizable</a>;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  }</div><div class="line"><a name="l00298"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#a3028bed2da89f8932312b1203723cb66"> 298</a></span>  <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a3028bed2da89f8932312b1203723cb66">set_is_dynamic</a>(<span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a7e890c9c5d4143d64a83b4ac19f4d3e4">is_dynamic</a>)<span class="keyword"> override</span></div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  _is_dynamic = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a7e890c9c5d4143d64a83b4ac19f4d3e4">is_dynamic</a>;</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  }</div><div class="line"><a name="l00303"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#a3c20d908342087484d883574d55dd482"> 303</a></span>  <a class="code" href="structarm__compute_1_1_valid_region.xhtml">ValidRegion</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a3c20d908342087484d883574d55dd482">valid_region</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keywordflow">return</span> _valid_region;</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  }</div><div class="line"><a name="l00307"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#a43e45363267b6bce4bb6770febe9ce11"> 307</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a43e45363267b6bce4bb6770febe9ce11">set_valid_region</a>(<span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_valid_region.xhtml">ValidRegion</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a3c20d908342087484d883574d55dd482">valid_region</a>)<span class="keyword"> override</span></div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  _valid_region = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a3c20d908342087484d883574d55dd482">valid_region</a>;</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  }</div><div class="line"><a name="l00311"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#ac74736e3863207232a23b7181c1d0f44"> 311</a></span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#ac74736e3863207232a23b7181c1d0f44">quantization_info</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keywordflow">return</span> _quantization_info;</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  }</div><div class="line"><a name="l00315"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_info.xhtml#a5f63b63606dbbbe54474e6e970a6738c"> 315</a></span>  <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a5f63b63606dbbbe54474e6e970a6738c">data_layout</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keywordflow">return</span> _data_layout;</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  }</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span> </div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> <span class="keyword">private</span>:<span class="comment"></span></div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span> <span class="comment"> /** Calculates strides, offset and total size resulting from the specified padding around the XY plane.</span></div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span> <span class="comment"> *</span></div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> <span class="comment"> * @param[in] padding Padding around the XY plane in elements.</span></div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span> <span class="comment"> */</span></div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  std::tuple<Strides, size_t, size_t> calculate_padding_requirements(<span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_border_size.xhtml">PaddingSize</a> &<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a79e20eacb1e963e24a21ebd7369effd7">padding</a>);</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> </div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <span class="keywordtype">size_t</span> _total_size;</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="keywordtype">size_t</span> _offset_first_element_in_bytes;</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <a class="code" href="classarm__compute_1_1_strides.xhtml">Strides</a> _strides_in_bytes;</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  <span class="keywordtype">size_t</span> _num_channels;</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> _tensor_shape;</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> _data_type;</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> _format;</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="keywordtype">bool</span> _is_resizable;</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <span class="keywordtype">bool</span> _is_dynamic;</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <a class="code" href="structarm__compute_1_1_valid_region.xhtml">ValidRegion</a> _valid_region;</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <a class="code" href="structarm__compute_1_1_border_size.xhtml">PaddingSize</a> _padding;</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a> _quantization_info;</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> _data_layout;</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span> };</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span> } <span class="comment">// namespace arm_compute</span></div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span> <span class="preprocessor">#endif </span><span class="comment">/*ARM_COMPUTE_TENSORINFO_H */</span><span class="preprocessor"></span></div><div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a523a68398c1a8161daa4238c15e065fa"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a523a68398c1a8161daa4238c15e065fa">arm_compute::TensorInfo::set_format</a></div><div class="ttdeci">ITensorInfo & set_format(Format format) override</div><div class="ttdoc">Set the format of an already initialized tensor.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00333">TensorInfo.cpp:333</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a45cde9abb508c62d67c3bb2b9bf566a5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">arm_compute::test::validation::shape</a></div><div class="ttdeci">shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_absolute_difference_8cpp_source.xhtml#l00097">AbsoluteDifference.cpp:97</a></div></div> |
| <div class="ttc" id="classarm__compute_1_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_info_xhtml_a4b7391b7025befbe44b743723feb4a9b"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a4b7391b7025befbe44b743723feb4a9b">arm_compute::TensorInfo::init_auto_padding</a></div><div class="ttdeci">size_t init_auto_padding(const TensorShape &tensor_shape, Format format)</div><div class="ttdoc">Initialize the metadata structure for the given tensor shape and single-plane format,...</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00181">TensorInfo.cpp:181</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_aa2ca251f99c56767719e991a26371603"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#aa2ca251f99c56767719e991a26371603">arm_compute::TensorInfo::has_padding</a></div><div class="ttdeci">bool has_padding() const override</div><div class="ttdoc">Checks if the tensor has been allocated with padding or not.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00281">TensorInfo.h:281</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a43e45363267b6bce4bb6770febe9ce11"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a43e45363267b6bce4bb6770febe9ce11">arm_compute::TensorInfo::set_valid_region</a></div><div class="ttdeci">void set_valid_region(const ValidRegion &valid_region) override</div><div class="ttdoc">Set the valid region of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00307">TensorInfo.h:307</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_afbc359bde9be72a6edab175978e56662"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#afbc359bde9be72a6edab175978e56662">arm_compute::TensorInfo::clone</a></div><div class="ttdeci">std::unique_ptr< ITensorInfo > clone() const override</div><div class="ttdoc">Provide a clone of the current object of class T.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00314">TensorInfo.cpp:314</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_aaee6555ace43b03173844b1a228a3fc3"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#aaee6555ace43b03173844b1a228a3fc3">arm_compute::TensorInfo::is_resizable</a></div><div class="ttdeci">bool is_resizable() const override</div><div class="ttdoc">Flag indicating whether the size of the tensor can be changed.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00285">TensorInfo.h:285</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_border_size_xhtml"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml">arm_compute::BorderSize</a></div><div class="ttdoc">Container for 2D border size.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00269">Types.h:269</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a3c20d908342087484d883574d55dd482"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a3c20d908342087484d883574d55dd482">arm_compute::TensorInfo::valid_region</a></div><div class="ttdeci">ValidRegion valid_region() const override</div><div class="ttdoc">Valid region of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00303">TensorInfo.h:303</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a6f13b974eeb528acde66de8d9b3fd95c"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a6f13b974eeb528acde66de8d9b3fd95c">arm_compute::TensorInfo::num_channels</a></div><div class="ttdeci">size_t num_channels() const override</div><div class="ttdoc">The number of channels for each tensor element.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00257">TensorInfo.h:257</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a5a80b3a6ae624417617d6a8d3209add5"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a5a80b3a6ae624417617d6a8d3209add5">arm_compute::TensorInfo::reset_padding</a></div><div class="ttdeci">ITensorInfo & reset_padding() override</div><div class="ttdoc">Resets the padding settings of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00384">TensorInfo.cpp:384</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a7e890c9c5d4143d64a83b4ac19f4d3e4"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a7e890c9c5d4143d64a83b4ac19f4d3e4">arm_compute::TensorInfo::is_dynamic</a></div><div class="ttdeci">bool is_dynamic() const override</div><div class="ttdoc">Flag indicating whether the shape of the tensor is dynamic, meaning that it can change on kernel/func...</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00289">TensorInfo.h:289</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a5f63b63606dbbbe54474e6e970a6738c"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a5f63b63606dbbbe54474e6e970a6738c">arm_compute::TensorInfo::data_layout</a></div><div class="ttdeci">DataLayout data_layout() const override</div><div class="ttdoc">Get the data layout of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00315">TensorInfo.h:315</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02">arm_compute::DataLayoutDimension</a></div><div class="ttdeci">DataLayoutDimension</div><div class="ttdoc">[DataLayout enum definition]</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00126">Types.h:126</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a8813441b655b97c00139c6a5a6390e97"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">arm_compute::TensorInfo::dimension</a></div><div class="ttdeci">size_t dimension(size_t index) const override</div><div class="ttdoc">Return the size of the requested dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00232">TensorInfo.h:232</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_h_o_g_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_h_o_g_info.xhtml">arm_compute::HOGInfo</a></div><div class="ttdoc">Store the HOG's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_h_o_g_info_8h_source.xhtml#l00035">HOGInfo.h:35</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_ac74736e3863207232a23b7181c1d0f44"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#ac74736e3863207232a23b7181c1d0f44">arm_compute::TensorInfo::quantization_info</a></div><div class="ttdeci">QuantizationInfo quantization_info() const override</div><div class="ttdoc">Get the quantization settings (scale and offset) of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00311">TensorInfo.h:311</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_af53d8203ecc37896ca4579d1ee3fdffc"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#af53d8203ecc37896ca4579d1ee3fdffc">arm_compute::TensorInfo::extend_padding</a></div><div class="ttdeci">bool extend_padding(const PaddingSize &padding) override</div><div class="ttdoc">Update the offset to the first element, the strides and the total size.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00279">TensorInfo.cpp:279</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a0a9053e6c4729ac5201b58068050c319"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a0a9053e6c4729ac5201b58068050c319">arm_compute::TensorInfo::set_data_type</a></div><div class="ttdeci">ITensorInfo & set_data_type(DataType data_type) override</div><div class="ttdoc">Set the data type to the specified value.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00319">TensorInfo.cpp:319</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml">arm_compute::ITensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_info_8h_source.xhtml#l00040">ITensorInfo.h:40</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a650247f9a828d1ef60135b01f8f77765"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a650247f9a828d1ef60135b01f8f77765">arm_compute::TensorInfo::dimension</a></div><div class="ttdeci">size_t dimension(DataLayoutDimension dimension) const override</div><div class="ttdoc">Return the size of the requested data layout dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00236">TensorInfo.h:236</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a79e20eacb1e963e24a21ebd7369effd7"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a79e20eacb1e963e24a21ebd7369effd7">arm_compute::TensorInfo::padding</a></div><div class="ttdeci">PaddingSize padding() const override</div><div class="ttdoc">Padding of tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00277">TensorInfo.h:277</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a5f1ca9d674346287cae57a6c5b5c24ec"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a5f1ca9d674346287cae57a6c5b5c24ec">arm_compute::TensorInfo::strides_in_bytes</a></div><div class="ttdeci">const Strides & strides_in_bytes() const override</div><div class="ttdoc">The strides in bytes for accessing each dimension of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00240">TensorInfo.h:240</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a907f837b924945ad1981c8fe8eca61e4"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a907f837b924945ad1981c8fe8eca61e4">arm_compute::TensorInfo::auto_padding</a></div><div class="ttdeci">bool auto_padding() override</div><div class="ttdoc">Update the offset to the first element and the strides to automatically computed values.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00222">TensorInfo.cpp:222</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) 2017-2020 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="classarm__compute_1_1_tensor_info_xhtml_ac4b36cc1e56b0b7e579bb4b7196490db"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#ac4b36cc1e56b0b7e579bb4b7196490db">arm_compute::TensorInfo::format</a></div><div class="ttdeci">Format format() const override</div><div class="ttdoc">Colour format of the image.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00269">TensorInfo.h:269</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a78839e7861ba8ffed52ca55da2745761"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a78839e7861ba8ffed52ca55da2745761">arm_compute::TensorInfo::set_quantization_info</a></div><div class="ttdeci">ITensorInfo & set_quantization_info(const QuantizationInfo &quantization_info) override</div><div class="ttdoc">Set the quantization settings (scale and offset) of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00372">TensorInfo.cpp:372</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a9a3e72153aeb3ed212e9c3698774e881"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a9a3e72153aeb3ed212e9c3698774e881">arm_compute::TensorInfo::data_type</a></div><div class="ttdeci">DataType data_type() const override</div><div class="ttdoc">Data type used for each element of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00265">TensorInfo.h:265</a></div></div> |
| <div class="ttc" id="arm__compute_2core_2_utils_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_utils_8h.xhtml">Utils.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_quantization_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_quantization_info.xhtml">arm_compute::QuantizationInfo</a></div><div class="ttdoc">Quantization information.</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00069">QuantizationInfo.h:69</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a13622133d9b41900a6a3e8f89e59a78b"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a13622133d9b41900a6a3e8f89e59a78b">arm_compute::TensorInfo::num_dimensions</a></div><div class="ttdeci">size_t num_dimensions() const override</div><div class="ttdoc">The number of dimensions of the tensor (rank)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00253">TensorInfo.h:253</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a70b6e1495b94818cce4981dbac6bdd66"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a70b6e1495b94818cce4981dbac6bdd66">arm_compute::TensorInfo::set_data_layout</a></div><div class="ttdeci">ITensorInfo & set_data_layout(const DataLayout &data_layout) override</div><div class="ttdoc">Set the data layout of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00378">TensorInfo.cpp:378</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#l00053">Types.h:53</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_af398466b602a02b42d8df19fb66a6c60"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#af398466b602a02b42d8df19fb66a6c60">arm_compute::TensorInfo::total_size</a></div><div class="ttdeci">size_t total_size() const override</div><div class="ttdoc">Returns the total size of the tensor in bytes.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00273">TensorInfo.h:273</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="namespacearm__compute_xhtml_abb7e0f23a4f2e63f39433f158dad47ab"><div class="ttname"><a href="namespacearm__compute.xhtml#abb7e0f23a4f2e63f39433f158dad47ab">arm_compute::data_size_from_type</a></div><div class="ttdeci">size_t data_size_from_type(DataType data_type)</div><div class="ttdoc">The size in bytes of the data type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l00109">Utils.h:109</a></div></div> |
| <div class="ttc" id="_i_tensor_info_8h_xhtml"><div class="ttname"><a href="_i_tensor_info_8h.xhtml">ITensorInfo.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a78bf77b2d9b959259f77a32b9a412184"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a78bf77b2d9b959259f77a32b9a412184">arm_compute::TensorInfo::set_is_resizable</a></div><div class="ttdeci">ITensorInfo & set_is_resizable(bool is_resizable) override</div><div class="ttdoc">Set the flag whether the tensor size can be changed.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00293">TensorInfo.h:293</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_ad03af3eeb6f3666d6282ca689c1b2ce8"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#ad03af3eeb6f3666d6282ca689c1b2ce8">arm_compute::TensorInfo::set_num_channels</a></div><div class="ttdeci">ITensorInfo & set_num_channels(int num_channels) override</div><div class="ttdoc">Set the number of channels to the specified value.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00326">TensorInfo.cpp:326</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_ad6b64f33be1e66dcf7612483ffb8fd63"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#ad6b64f33be1e66dcf7612483ffb8fd63">arm_compute::TensorInfo::init</a></div><div class="ttdeci">void init(Format format)</div><div class="ttdoc">Initialize the tensor info with just a format.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00107">TensorInfo.cpp:107</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_strides_xhtml"><div class="ttname"><a href="classarm__compute_1_1_strides.xhtml">arm_compute::Strides</a></div><div class="ttdoc">Strides of an item in bytes.</div><div class="ttdef"><b>Definition:</b> <a href="_strides_8h_source.xhtml#l00037">Strides.h:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a3028bed2da89f8932312b1203723cb66"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a3028bed2da89f8932312b1203723cb66">arm_compute::TensorInfo::set_is_dynamic</a></div><div class="ttdeci">ITensorInfo & set_is_dynamic(bool is_dynamic) override</div><div class="ttdoc">Set the flag whether the tensor size is dynamic.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00298">TensorInfo.h:298</a></div></div> |
| <div class="ttc" id="_strides_8h_xhtml"><div class="ttname"><a href="_strides_8h.xhtml">Strides.h</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_border_size_xhtml_afaafdfc441c2433c70959e3dfe46fd97"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml#afaafdfc441c2433c70959e3dfe46fd97">arm_compute::BorderSize::empty</a></div><div class="ttdeci">constexpr bool empty() const</div><div class="ttdoc">Check if the entire border is zero.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00296">Types.h:296</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="classarm__compute_1_1_dimensions_xhtml_a80a5f2d6e3a697c9aad893a3b4242615"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a80a5f2d6e3a697c9aad893a3b4242615">arm_compute::Dimensions::num_dimensions</a></div><div class="ttdeci">unsigned int num_dimensions() const</div><div class="ttdoc">Returns the effective dimensionality of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00122">Dimensions.h:122</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a7888888b7f18215ae83fd3660d38eccb"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a7888888b7f18215ae83fd3660d38eccb">arm_compute::TensorInfo::offset_element_in_bytes</a></div><div class="ttdeci">int32_t offset_element_in_bytes(const Coordinates &pos) const override</div><div class="ttdoc">The offset in bytes from the beginning of the memory allocation to access the element at position (x,...</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00394">TensorInfo.cpp:394</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_ab54246abe670b06f5624add7e7022904"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#ab54246abe670b06f5624add7e7022904">arm_compute::TensorInfo::offset_first_element_in_bytes</a></div><div class="ttdeci">size_t offset_first_element_in_bytes() const override</div><div class="ttdoc">The offset from the beginning of the memory allocation to the first element of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00244">TensorInfo.h:244</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</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="classarm__compute_1_1_tensor_info_xhtml_a2d7e8b8e05c3318b2d90c40d781745d2"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a2d7e8b8e05c3318b2d90c40d781745d2">arm_compute::TensorInfo::set_tensor_shape</a></div><div class="ttdeci">ITensorInfo & set_tensor_shape(const TensorShape &shape) override</div><div class="ttdoc">Set the shape of an already initialized tensor.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00350">TensorInfo.cpp:350</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a21c2ae9fa438faf42669dadda628080c"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a21c2ae9fa438faf42669dadda628080c">arm_compute::TensorInfo::TensorInfo</a></div><div class="ttdeci">TensorInfo()</div><div class="ttdoc">Default constructor.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_tensor_info_8cpp_source.xhtml#l00035">TensorInfo.cpp:35</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_valid_region_xhtml"><div class="ttname"><a href="structarm__compute_1_1_valid_region.xhtml">arm_compute::ValidRegion</a></div><div class="ttdoc">Container for valid region of a window.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00184">Types.h:184</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00327">Helpers.inl:327</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_adcf156ba30ff118c28690671e83ea06b"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#adcf156ba30ff118c28690671e83ea06b">arm_compute::TensorInfo::operator=</a></div><div class="ttdeci">TensorInfo & operator=(const TensorInfo &)=default</div><div class="ttdoc">Allow instances of this class to be copied.</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a4f4125dba5283887b34f889b1c615c0c"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">arm_compute::test::validation::info</a></div><div class="ttdeci">info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">ConvolutionLayer.cpp:182</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a269b19ce3f357ac65f41f9951906e38e"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a269b19ce3f357ac65f41f9951906e38e">arm_compute::TensorInfo::tensor_shape</a></div><div class="ttdeci">const TensorShape & tensor_shape() const override</div><div class="ttdoc">Size for each dimension of the tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00261">TensorInfo.h:261</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#l00075">Types.h:75</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdoc">[DataLayout enum definition]</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00117">Types.h:117</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a62b67b578f684c4d516843c9dea86a23"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a62b67b578f684c4d516843c9dea86a23">arm_compute::TensorInfo::element_size</a></div><div class="ttdeci">size_t element_size() const override</div><div class="ttdoc">Element size in bytes calculated as data_size() * num_channels()</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00249">TensorInfo.h:249</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_1_tensor_info_xhtml_a4eb5913c3ce5fe2bcbaafd8c9224d384"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a4eb5913c3ce5fe2bcbaafd8c9224d384">arm_compute::TensorInfo::~TensorInfo</a></div><div class="ttdeci">~TensorInfo()=default</div><div class="ttdoc">Default destructor.</div></div> |
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