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| <a href="_shape_calculator_8h.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2017-2019 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#ifndef __ARM_COMPUTE_MISC_SHAPE_CALCULATOR_H__</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#define __ARM_COMPUTE_MISC_SHAPE_CALCULATOR_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="arm__compute_2core_2_helpers_8h.xhtml">arm_compute/core/Helpers.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</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="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="_kernel_descriptors_8h.xhtml">arm_compute/core/KernelDescriptors.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</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="l00031"></a><span class="lineno"> 31</span> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="tensor__transform_8h.xhtml">arm_compute/core/utils/helpers/tensor_transform.h</a>"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include <cmath></span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> </div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> {</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="keyword">namespace </span>misc</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> {</div><div class="line"><a name="l00040"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml"> 40</a></span> <span class="keyword">namespace </span>shape_calculator</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> {</div><div class="line"><a name="l00051"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a951fb0d8dcf2a2a338e26a59ffc9af17"> 51</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a951fb0d8dcf2a2a338e26a59ffc9af17">compute_vector_to_tensor_output_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &input, <span class="keywordtype">size_t</span> conv_w, <span class="keywordtype">size_t</span> conv_h, <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>)</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> {</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_w = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_h = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_c = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div><div class="line"><a name="l00057"></a><span class="lineno"> 57</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>(input);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_w, conv_w);</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_h, conv_h);</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_c, input.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#aa87f8fc26981b0f3228a78c83b95b802">x</a>() / (conv_w * conv_h));</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> }</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> </div><div class="line"><a name="l00072"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a593fb7ecc281425b190cd6f20164b1a3"> 72</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a593fb7ecc281425b190cd6f20164b1a3">compute_permutation_output_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_strides.xhtml">PermutationVector</a> &perm)</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> {</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <a class="code" href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">permute</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>, perm);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> }</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00086"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#afbc83cd4145d161da4c026e1f5743e1d"> 86</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#afbc83cd4145d161da4c026e1f5743e1d">compute_reorg_output_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, int32_t stride)</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> {</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_width = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_channel = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> </div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(stride <= 0);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>((input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_width] % stride != 0), <span class="stringliteral">"The width of the input tensor must be a multiple of stride"</span>);</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>((input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_height] % stride != 0), <span class="stringliteral">"The height of the input tensor must be a multiple of stride"</span>);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> </div><div class="line"><a name="l00096"></a><span class="lineno"> 96</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_width, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[idx_width] / stride);</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_height, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[idx_height] / stride);</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_channel, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[idx_channel] * stride * stride);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> </div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> }</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div><div class="line"><a name="l00113"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6365b505b5c1b98916425bc692b6ea49"> 113</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6365b505b5c1b98916425bc692b6ea49">compute_weights_reshaped_shape</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#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keywordtype">bool</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a> = <span class="keyword">false</span>, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> = 1)</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> {</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="comment">// Number of groups greater than one are only supported for NCHW data layout, and the number of weights must be a multiple of it.</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> == 0);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.data_layout() == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a> && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> > 1);</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.dimension(3) % <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>) != 0);</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> </div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="comment">// Calculate output shape</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> weights_reshaped{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.tensor_shape() };</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  weights_reshaped.set(3, weights_reshaped[3] / <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>);</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> </div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  weights_reshaped.collapse(3);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> tmp_dim = weights_reshaped[0];</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  weights_reshaped.set(0, weights_reshaped[1]);</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  weights_reshaped.set(1, tmp_dim + (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a> ? 1 : 0));</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.num_dimensions() < 5)</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  {</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  weights_reshaped.set(2, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  }</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> </div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keywordflow">return</span> weights_reshaped;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> }</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> </div><div class="line"><a name="l00144"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a389f89ab5121dad0906d0b7324fbf73d"> 144</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a389f89ab5121dad0906d0b7324fbf73d">compute_lhs_reshaped_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &a, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info.xhtml">GEMMLHSMatrixInfo</a> &lhs_info, <span class="keywordtype">bool</span> reinterpret_input_as_3d = <span class="keyword">false</span>)</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> {</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(lhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info.xhtml#a41618231c7d7b990c42fe6846b4dde27">m0</a> == 0);</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(lhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info.xhtml#a9bca3aa421b5fa7c07b0aa751b39b25f">k0</a> == 0);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(lhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info.xhtml#acfef62abd2f827bf05666ba50a705c0d">v0</a> == 0);</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> </div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="comment">// Input width/height</span></div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input_width = a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0);</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input_height = reinterpret_input_as_3d ? a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) * a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(2) : a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1);</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> </div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="comment">// Number of horizontal/vertical blocks in the input tensor</span></div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_horiz_blocks = std::ceil(input_width / static_cast<float>(lhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info.xhtml#a9bca3aa421b5fa7c07b0aa751b39b25f">k0</a>));</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_vert_blocks = std::ceil(input_height / static_cast<float>(lhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info.xhtml#a41618231c7d7b990c42fe6846b4dde27">m0</a>));</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="comment">// Block size</span></div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> block_size = lhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info.xhtml#a41618231c7d7b990c42fe6846b4dde27">m0</a> * lhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info.xhtml#a9bca3aa421b5fa7c07b0aa751b39b25f">k0</a>;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> </div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="comment">// Output width/height</span></div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_width = block_size * num_horiz_blocks * lhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info.xhtml#acfef62abd2f827bf05666ba50a705c0d">v0</a>;</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_height = std::ceil(num_vert_blocks / static_cast<float>(lhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info.xhtml#acfef62abd2f827bf05666ba50a705c0d">v0</a>));</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> </div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> lhs_shape{ a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  lhs_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, output_width);</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  lhs_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(1, output_height);</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> </div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keywordflow">if</span>((reinterpret_input_as_3d) && (lhs_shape.num_dimensions() > 2))</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  {</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="comment">// When the data format is NHWC and the shapes are Nx1x1</span></div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="comment">// the tensor shape num_dimensions is automatically set to 1 instead of 3.</span></div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="comment">// To avoid failures by removing a dimension that doesn't exist</span></div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="comment">// check if the number of dimensions is greater than 2.</span></div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  lhs_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#acb74edf42335de0dca0da5158b704c4b">remove_dimension</a>(2);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  }</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> </div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordflow">return</span> lhs_shape;</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> }</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> </div><div class="line"><a name="l00188"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a09ad10a110d947fd9c444b2ea5e4c127"> 188</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a09ad10a110d947fd9c444b2ea5e4c127">compute_rhs_reshaped_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &a, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info.xhtml">GEMMRHSMatrixInfo</a> &rhs_info)</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> {</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(rhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info.xhtml#ac0c87b58d0d04796c81d8138231dccda">n0</a> == 0);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(rhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info.xhtml#a9bca3aa421b5fa7c07b0aa751b39b25f">k0</a> == 0);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(rhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info.xhtml#a2dc097fefeb3d010fb5570a19cac6cd3">h0</a> == 0);</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="comment">// Input width/height</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input_width = a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0);</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input_height = a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1);</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> </div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="comment">// Number of horizontal/vertical blocks in the input tensor</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_horiz_blocks = std::ceil(input_width / static_cast<float>(rhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info.xhtml#ac0c87b58d0d04796c81d8138231dccda">n0</a>));</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_vert_blocks = std::ceil(input_height / static_cast<float>(rhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info.xhtml#a9bca3aa421b5fa7c07b0aa751b39b25f">k0</a>));</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> </div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="comment">// Block size</span></div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> block_size = rhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info.xhtml#ac0c87b58d0d04796c81d8138231dccda">n0</a> * rhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info.xhtml#a9bca3aa421b5fa7c07b0aa751b39b25f">k0</a>;</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span> </div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="comment">// Output width/height</span></div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_width = block_size * num_vert_blocks * rhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info.xhtml#a2dc097fefeb3d010fb5570a19cac6cd3">h0</a>;</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_height = std::ceil(num_horiz_blocks / static_cast<float>(rhs_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info.xhtml#a2dc097fefeb3d010fb5570a19cac6cd3">h0</a>));</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span> </div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> rhs_shape{ a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  rhs_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, output_width);</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  rhs_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(1, output_height);</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> </div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="keywordflow">return</span> rhs_shape;</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> }</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> </div><div class="line"><a name="l00224"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8d52adbbcd2c53f837c96b5a3d15c4fb"> 224</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8d52adbbcd2c53f837c96b5a3d15c4fb">compute_interleaved_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &a, <span class="keywordtype">int</span> mult_interleave4x4_height = 1, <span class="keywordtype">bool</span> reinterpret_input_as_3d = <span class="keyword">false</span>)</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span> {</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="comment">// The interleaved output matrix will have the following shape: [ a_height * W, ceil(a_width / W) ] where W = 4 * mult_interleave4x4_height</span></div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(mult_interleave4x4_height < 1);</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> interleave_width = 4 * mult_interleave4x4_height;</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_interleaved_a{ a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  shape_interleaved_a.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) * interleave_width);</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordflow">if</span>(reinterpret_input_as_3d)</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  {</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> M = a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) * a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(2);</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> height = std::ceil(M / static_cast<float>(interleave_width));</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  shape_interleaved_a.set(1, height);</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> </div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="comment">// When the data format is NHWC and the shapes are Nx1x1</span></div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="comment">// the tensor shape num_dimensions is automatically set to 1 instead of 3.</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="comment">// To avoid failures by removing a dimension that doesn't exist</span></div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="comment">// check if the number of dimensions is greater than 2.</span></div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keywordflow">if</span>(shape_interleaved_a.num_dimensions() > 2)</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  {</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  shape_interleaved_a.remove_dimension(2);</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  }</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  }</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keywordflow">else</span></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>  shape_interleaved_a.set(1, std::ceil(a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) / static_cast<float>(interleave_width)));</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  }</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> </div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keywordflow">return</span> shape_interleaved_a;</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> }</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span> </div><div class="line"><a name="l00261"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a3f10bc0f3e2a0126ce8c26e3d6a8fb96"> 261</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a3f10bc0f3e2a0126ce8c26e3d6a8fb96">compute_reshaped_depthwise_weights_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_depthwise_convolution_reshape_info.xhtml">DepthwiseConvolutionReshapeInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>)</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> {</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="keyword">const</span> <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>();</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</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#ae8f0126f051c787279a8c9ee3c3a5d55">weights_shape</a>{};</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> </div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> width_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> height_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> channel_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_channels = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(channel_idx);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_rows = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(height_idx);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_cols = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(width_idx);</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> </div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae8f0126f051c787279a8c9ee3c3a5d55">weights_shape</a>.set(0, num_rows * num_cols * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.c0);</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae8f0126f051c787279a8c9ee3c3a5d55">weights_shape</a>.set(1, <a class="code" href="namespacearm__compute.xhtml#acac1575c0edd329ceb4a54d9fe8dcb08">DIV_CEIL</a>(num_channels, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.c0));</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae8f0126f051c787279a8c9ee3c3a5d55">weights_shape</a>;</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span> }</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span> </div><div class="line"><a name="l00284"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a70a2ef9fd754b5798a0a92656f8b5fcf"> 284</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a70a2ef9fd754b5798a0a92656f8b5fcf">compute_transpose1xW_shape</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#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>)</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> {</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="comment">// The transpose1xW output matrix will have the following shape: [ b_height * 16, ceil(b_width / 16.0f) ]</span></div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_transposed1xW_b{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>.tensor_shape() };</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  shape_transposed1xW_b.set(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>.dimension(1) * 16);</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  shape_transposed1xW_b.set(1, std::ceil(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>.dimension(0) / 16.f));</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> </div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="keywordflow">return</span> shape_transposed1xW_b;</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span> }</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span> </div><div class="line"><a name="l00301"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5797726a8fbee3b11b92757c2f0031d6"> 301</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5797726a8fbee3b11b92757c2f0031d6">compute_transpose1xW_with_element_size_shape</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#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>, <span class="keywordtype">int</span> mult_transpose1xW_width = 1)</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span> {</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="comment">// Note: mult_transpose1xW_width expresses the number of chunks with size 1x(W) we want to store on the same row</span></div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="comment">// The transpose1xW output matrix will have the following shape:</span></div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="comment">// [ b_height * W, ceil(b_width / W) ] where W = (16 / element size of the tensor) * mult_transpose1xW_width</span></div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(mult_transpose1xW_width < 1);</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_transposed1xW_b{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>.tensor_shape() };</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> transpose_width = (16 / <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>.element_size()) * mult_transpose1xW_width;</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  shape_transposed1xW_b.set(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>.dimension(1) * transpose_width);</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  shape_transposed1xW_b.set(1, static_cast<size_t>(std::ceil(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>.dimension(0) / static_cast<float>(transpose_width))));</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span> </div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keywordflow">return</span> shape_transposed1xW_b;</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span> }</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span> </div><div class="line"><a name="l00321"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a60ce6c017f70d978b48b101ce314969e"> 321</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a60ce6c017f70d978b48b101ce314969e">compute_reductionA_shape</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#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>)</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span> {</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_vector_sum_col{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>.tensor_shape() };</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keywordflow">if</span>(shape_vector_sum_col.num_dimensions() > 1)</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  {</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  shape_vector_sum_col.remove_dimension(1);</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  }</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span> </div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <span class="keywordflow">return</span> shape_vector_sum_col;</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> }</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span> </div><div class="line"><a name="l00338"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69f9b3191aafc4905f9d029ff9d48fea"> 338</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69f9b3191aafc4905f9d029ff9d48fea">compute_reductionB_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &a)</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span> {</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_vector_sum_row{ a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  shape_vector_sum_row.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>, a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1));</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keywordflow">if</span>(shape_vector_sum_row.num_dimensions() > 1)</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  {</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  shape_vector_sum_row.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#acb74edf42335de0dca0da5158b704c4b">remove_dimension</a>(1);</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  }</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span> </div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <span class="keywordflow">return</span> shape_vector_sum_row;</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span> }</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span> </div><div class="line"><a name="l00359"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a264e2e6d3ff632e90d450435fce66d54"> 359</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a264e2e6d3ff632e90d450435fce66d54">compute_col2im_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &convolved_dims, <span class="keywordtype">bool</span> batch_size_on_z, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> = 1)</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span> {</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> == 0);</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[1] != (convolved_dims.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a75026dc1fa3840404ae4553010efcd52">area</a>()));</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> > 1) && input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[2] != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>);</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> </div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>();</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> width_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> height_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> channel_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span> </div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> col2im_shape{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="comment">// If batches start on 3rd dimension shift dimensions right by 1 to retain upper tensor shape,</span></div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="comment">// as first three will be override by H,W,C data</span></div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="keywordflow">if</span>(batch_size_on_z && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> == 1)</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  {</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  col2im_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#ab6d90bb06b3c19db6aba94975be64d10">shift_right</a>(1);</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  }</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  col2im_shape.set(width_idx, convolved_dims.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a>);</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  col2im_shape.set(height_idx, convolved_dims.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a>);</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  col2im_shape.set(channel_idx, input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[0] * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>);</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span> </div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="keywordflow">return</span> col2im_shape;</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span> }</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> </div><div class="line"><a name="l00390"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf"> 390</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">compute_transposed_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input)</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span> {</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_transposed{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span> </div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  shape_transposed.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1));</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  shape_transposed.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(1, input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0));</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span> </div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="keywordflow">return</span> shape_transposed;</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span> }</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span> </div><div class="line"><a name="l00410"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac7147815227e7ba91814cfdcd38f23ed"> 410</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac7147815227e7ba91814cfdcd38f23ed">compute_depthwise_convolution_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <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#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth_multiplier, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a> = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1U,</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  1U))</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span> {</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <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#a865514e30baa11b11c8fa65f944532fb">input_shape</a>{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae8f0126f051c787279a8c9ee3c3a5d55">weights_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.tensor_shape() };</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span> </div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>();</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> width_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> height_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> channel_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span> </div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_data_layout = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.data_layout();</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> weights_width_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(weights_data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> weights_height_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(weights_data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span> </div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_width = 0;</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_height = 0;</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  std::tie(output_width, output_height) = <a class="code" href="namespacearm__compute.xhtml#abbe3399a66c35c3c353030cd0a84c936">scaled_dimensions</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a>[width_idx], <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a>[height_idx],</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae8f0126f051c787279a8c9ee3c3a5d55">weights_shape</a>[weights_width_idx], <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae8f0126f051c787279a8c9ee3c3a5d55">weights_shape</a>[weights_height_idx],</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span> </div><div class="line"><a name="l00431"></a><span class="lineno"> 431</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a> };</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(width_idx, output_width);</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(height_idx, output_height);</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(channel_idx, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a>[channel_idx] * depth_multiplier);</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span> </div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span> }</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span> </div><div class="line"><a name="l00451"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8234d85cf61e3eb6fa00c012bee6f5bf"> 451</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8234d85cf61e3eb6fa00c012bee6f5bf">compute_deconvolution_upsampled_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <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#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> sx, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> sy,</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  std::pair<unsigned int, unsigned int> &out_dims, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> &padx, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> &pady)</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span> {</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>();</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_w = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_h = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> </div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <span class="comment">// Find the upsampled dimensions</span></div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_x = (input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_w) - 1) * sx + 1;</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_y = (input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_h) - 1) * sy + 1;</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span> </div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="comment">// Find the padding needed for the convolution with stride 1 in order to match output shape</span></div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  padx = out_dims.first - (out_x - <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.dimension(idx_w) + 1);</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  pady = out_dims.second - (out_y - <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.dimension(idx_h) + 1);</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  out_x += padx;</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  out_y += pady;</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span> </div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> scale_out_shape(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>());</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  scale_out_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(idx_w, out_x);</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  scale_out_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(idx_h, out_y);</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span> </div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <span class="keywordflow">return</span> scale_out_shape;</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span> }</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span> </div><div class="line"><a name="l00483"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ae270329cfe3dbab009b700318e8af8b4"> 483</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ae270329cfe3dbab009b700318e8af8b4">compute_deconvolution_output_shape</a>(<span class="keyword">const</span> std::pair<unsigned int, unsigned int> &out_dims, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <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#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>)</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span> {</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <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#a865514e30baa11b11c8fa65f944532fb">input_shape</a>{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <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#ae8f0126f051c787279a8c9ee3c3a5d55">weights_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.tensor_shape() };</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span> </div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>();</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> width_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> height_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> channel_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">DataLayoutDimension::BATCHES</a>);</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span> </div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> out_shape{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a> };</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  out_shape.set(width_idx, out_dims.first);</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  out_shape.set(height_idx, out_dims.second);</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  out_shape.set(channel_idx, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae8f0126f051c787279a8c9ee3c3a5d55">weights_shape</a>[batch_idx]);</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <span class="keywordflow">return</span> out_shape;</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span> }</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span> </div><div class="line"><a name="l00513"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8a9286d053e9f3a958064e4f3cdd02f7"> 513</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8a9286d053e9f3a958064e4f3cdd02f7">compute_im2col_conv_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &kernel_dims, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <span class="keywordtype">bool</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>, <span class="keywordtype">bool</span> batch_size_on_z,</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> = 1)</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span> {</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <span class="comment">// The output shape will be the 3D shape [ out_channels * kernel_area, num_elems_per_out_channel, batches ] if batch_size_on_z == true</span></div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <span class="comment">// or the 4D shape [ out_channels * kernel_area / num_groups, num_elems_per_out_channel, num_groups, batches ] if batch_size_on_z == false</span></div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> </div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> == 0);</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> > 1 && input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>() != <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>);</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> > 1 && batch_size_on_z);</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span> </div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span> </div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>();</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> width_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> height_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> channel_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span> </div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  std::pair<unsigned int, unsigned int> out_dims = <a class="code" href="namespacearm__compute.xhtml#abbe3399a66c35c3c353030cd0a84c936">scaled_dimensions</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[width_idx], <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[height_idx], kernel_dims.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a>, kernel_dims.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">dilation</a>);</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(0, (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[channel_idx] / <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a> * kernel_dims.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a75026dc1fa3840404ae4553010efcd52">area</a>() + (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">has_bias</a> ? 1 : 0))); <span class="comment">// NOLINT</span></div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, (out_dims.first * out_dims.second));</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="keywordflow">if</span>(batch_size_on_z && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.num_dimensions() >= 3)</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  {</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.remove_dimension(2);</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  }</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  {</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(2, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">num_groups</a>);</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  }</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span> </div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span> }</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span> </div><div class="line"><a name="l00551"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a83efb6708574e67d13965bcd2059ad75"> 551</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a83efb6708574e67d13965bcd2059ad75">compute_flatten_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input)</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span> {</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <span class="comment">// The output shape will be the flatten version of the input (i.e. [ width * height * channels, num_batches, ... ] ). Used for FlattenLayer and FullyConnectedLayer.</span></div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span> </div><div class="line"><a name="l00555"></a><span class="lineno"> 555</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span> </div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.collapse(3);</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span> </div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span> }</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span> </div><div class="line"><a name="l00569"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ad16b366db486fec63b6d962937ec4545"> 569</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ad16b366db486fec63b6d962937ec4545">compute_softmax_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keywordtype">size_t</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a> = 1)</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span> {</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <span class="comment">// The output shape will be a 2D version of the input. For instance:</span></div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="comment">// - [x,y,z] and axis 1 will return [x, y*z]</span></div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <span class="comment">// - [x,y,z,w] and axis 2 will return [x*y, w*z]</span></div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <span class="comment">// - [x,y,z,w] and axis 3 will return [x*y*z, w]</span></div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape2D = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span> </div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <span class="keywordflow">if</span>(axis < input->num_dimensions())</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  {</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  <span class="comment">// Collapse from axis onward (this changes the shape)</span></div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  shape2D.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a112cc1d5093b7672bf11569659251a7c">collapse_from</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>);</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span> </div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <span class="comment">// Collapse the rest (collapse is inclusive)</span></div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  shape2D.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a8e15e87871211f98c2b566137e38ef99">collapse</a>(shape2D.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a80a5f2d6e3a697c9aad893a3b4242615">num_dimensions</a>() - 1);</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  }</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  {</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <span class="comment">// Collapse everything</span></div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  shape2D.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a8e15e87871211f98c2b566137e38ef99">collapse</a>(shape2D.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a80a5f2d6e3a697c9aad893a3b4242615">num_dimensions</a>());</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  }</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span> </div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a> == 0)</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  {</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <span class="comment">// If axis is zero the first dim should be one. Since</span></div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <span class="comment">// collapse is an inclusive operation we need to shift</span></div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  shape2D.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#ab6d90bb06b3c19db6aba94975be64d10">shift_right</a>(1);</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  }</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span> </div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  <span class="keywordflow">return</span> shape2D;</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span> }</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span> </div><div class="line"><a name="l00608"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a25e3751f07d4b2771a05d8d01a7f7620"> 608</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a25e3751f07d4b2771a05d8d01a7f7620">compute_winograd_filter_transform_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span> {</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> tensor_shape{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span> </div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> kernel_size = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.kernel_size;</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> output_tile_size = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.output_tile_size;</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> input_tile_size = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(output_tile_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a> + kernel_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a> - 1, output_tile_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a> + kernel_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a> - 1);</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span> </div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  tensor_shape.remove_dimension(<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>));</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  tensor_shape.set(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>, input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(3));</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  tensor_shape.set(<a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>, input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>)));</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  tensor_shape.set(<a class="code" href="classarm__compute_1_1_window.xhtml#a893d17b56b9abc4423ce26e9a24ac5dc">Window::DimZ</a>, input_tile_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a75026dc1fa3840404ae4553010efcd52">area</a>());</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span> </div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  <span class="keywordflow">return</span> tensor_shape;</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span> }</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span> </div><div class="line"><a name="l00631"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a04249f91ec2964d21a91bb7038821000"> 631</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a04249f91ec2964d21a91bb7038821000">compute_winograd_input_transform_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span> {</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.convolution_info;</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> kernel_size = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.kernel_size;</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> output_tile_size = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.output_tile_size;</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> input_tile_size = <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(output_tile_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a> + kernel_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a> - 1, output_tile_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a> + kernel_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a> - 1);</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span> </div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_w = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_h = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_c = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span> </div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <span class="comment">// Compute the number of output tiles along the x and y direction of size "output_tile_size"</span></div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> num_tiles = <a class="code" href="namespacearm__compute.xhtml#a3b0c016b53e97663b39c2f3875f46c24">compute_winograd_convolution_tiles</a>(<a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_w], input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_h]),</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  kernel_size,</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  output_tile_size,</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>);</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span> </div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_c];</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = num_tiles.area();</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth = input_tile_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a75026dc1fa3840404ae4553010efcd52">area</a>();</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span> </div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(0, width);</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, height);</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(2, depth);</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span> </div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span> }</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span> </div><div class="line"><a name="l00667"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5699c316d27b41f0790827791e88ae26"> 667</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5699c316d27b41f0790827791e88ae26">compute_winograd_output_transform_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_winograd_info.xhtml">WinogradInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>)</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span> {</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.convolution_info;</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> kernel_size = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.kernel_size;</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> input_dimensions = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.input_dimensions;</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">winograd_info</a>.output_data_layout;</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span> </div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <span class="comment">// Compute output shape</span></div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_width = 0;</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_height = 0;</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  std::tie(output_width, output_height) = <a class="code" href="namespacearm__compute.xhtml#abbe3399a66c35c3c353030cd0a84c936">scaled_dimensions</a>(input_dimensions.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a>, input_dimensions.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a>,</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  kernel_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a>, kernel_size.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>);</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span> </div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> tensor_shape{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span> </div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  <span class="comment">// Output dimension</span></div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_w = output_width;</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_h = output_height;</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_c = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0);</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span> </div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  tensor_shape.set(<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>), out_w);</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  tensor_shape.set(<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>), out_h);</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  tensor_shape.set(<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>), out_c);</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span> </div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  <span class="keywordflow">return</span> tensor_shape;</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span> }</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span> </div><div class="line"><a name="l00702"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5d320d308c16b8ddda3c9d3f60fad79c"> 702</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5d320d308c16b8ddda3c9d3f60fad79c">compute_deep_convolution_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <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#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>)</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span> {</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <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#a865514e30baa11b11c8fa65f944532fb">input_shape</a>{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  <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#ae8f0126f051c787279a8c9ee3c3a5d55">weights_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.tensor_shape() };</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span> </div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_width = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_channel = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span> </div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input_width = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a>[idx_width];</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input_height = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a>[idx_height];</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weights_width = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae8f0126f051c787279a8c9ee3c3a5d55">weights_shape</a>[idx_width];</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weights_height = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae8f0126f051c787279a8c9ee3c3a5d55">weights_shape</a>[idx_height];</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weights_out_channel = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae8f0126f051c787279a8c9ee3c3a5d55">weights_shape</a>[3];</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_width = 0;</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_height = 0;</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  std::tie(output_width, output_height) = <a class="code" href="namespacearm__compute.xhtml#abbe3399a66c35c3c353030cd0a84c936">scaled_dimensions</a>(input_width, input_height, weights_width, weights_height, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">conv_info</a>);</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span> </div><div class="line"><a name="l00720"></a><span class="lineno"> 720</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a> };</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_width, output_width);</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_height, output_height);</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_channel, weights_out_channel);</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span> </div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span> }</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span> </div><div class="line"><a name="l00734"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a1b843e3850ed7324d11f77882cc597ae"> 734</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a1b843e3850ed7324d11f77882cc597ae">compute_min_max_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input)</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span> {</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>, 2);</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.remove_dimension(1);</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.remove_dimension(1);</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span> </div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span> }</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span> </div><div class="line"><a name="l00751"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ad242bedd6845b8fc13ade41cfc062c83"> 751</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ad242bedd6845b8fc13ade41cfc062c83">compute_pool_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a> pool_info)</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span> {</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pooled_w = 0;</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pooled_h = 0;</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span> </div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span> </div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_global_pooling = pool_info.<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml#a00bcea78a50c94ef3dc2a280b9ed8a7a">is_global_pooling</a>();</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx_width = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pool_size_x = is_global_pooling ? <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[idx_width] : pool_info.<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml#a9e9048b8199d75905ce5102c21f6c424">pool_size</a>().<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">width</a>;</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pool_size_y = is_global_pooling ? <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[idx_height] : pool_info.<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml#a9e9048b8199d75905ce5102c21f6c424">pool_size</a>().<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">height</a>;</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span> </div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  std::tie(pooled_w, pooled_h) = <a class="code" href="namespacearm__compute.xhtml#abbe3399a66c35c3c353030cd0a84c936">scaled_dimensions</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[idx_width],</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[idx_height],</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  pool_size_x,</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  pool_size_y,</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  pool_info.<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml#aec44a113cf93317f52355cbdea990966">pad_stride_info</a>());</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span> </div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_width, pooled_w);</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_height, pooled_h);</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span> </div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span> }</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span> </div><div class="line"><a name="l00784"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a3d3eaddfd85c16e7a9a385ba0e6a45b0"> 784</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a3d3eaddfd85c16e7a9a385ba0e6a45b0">compute_roi_align_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &rois, <a class="code" href="classarm__compute_1_1_r_o_i_pooling_layer_info.xhtml">ROIPoolingLayerInfo</a> pool_info)</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span> {</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span> </div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx_width = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span> </div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_width, pool_info.<a class="code" href="classarm__compute_1_1_r_o_i_pooling_layer_info.xhtml#a5a00bf3cb11be124771cf7e3958e218e">pooled_width</a>());</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_height, pool_info.<a class="code" href="classarm__compute_1_1_r_o_i_pooling_layer_info.xhtml#aee6a0c75a44f00d3306101e14ffc3ebb">pooled_height</a>());</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(3, rois.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1));</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span> </div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span> }</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span> </div><div class="line"><a name="l00805"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#af98bc3ef5c65dbb63bc79700ccdd043b"> 805</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#af98bc3ef5c65dbb63bc79700ccdd043b">compute_rnn_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batch_size)</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span> {</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, batch_size);</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span> </div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span> }</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span> </div><div class="line"><a name="l00822"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#adca241b012a5e00ddfcdc5a8db05a2a3"> 822</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#adca241b012a5e00ddfcdc5a8db05a2a3">compute_mm_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input0, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input1, <span class="keywordtype">bool</span> is_interleaved_transposed, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml">GEMMReshapeInfo</a> &reshape_info)</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span> {</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(input0.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 4, <span class="stringliteral">"The number of dimensions for the matrix A must be <= 4"</span>);</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(is_interleaved_transposed && reshape_info.<a class="code" href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#a00330b8913cac3b07029ac0c3350e806">reinterpret_input_as_3d</a>(), <span class="stringliteral">"The first input tensor cannot be reinterpreted as 3D if is_interleaved_transposed is true"</span>);</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span> </div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> reinterpret_input_as_3d = reshape_info.<a class="code" href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#a00330b8913cac3b07029ac0c3350e806">reinterpret_input_as_3d</a>();</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> reinterpret_output_as_3d = reshape_info.<a class="code" href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#abbd888f118c2209bf7578eb4f8942a07">depth_output_gemm3d</a>() != 0;</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> depth_output_gemm3d = reinterpret_output_as_3d ? reshape_info.<a class="code" href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#abbd888f118c2209bf7578eb4f8942a07">depth_output_gemm3d</a>() : 1;</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> m = reshape_info.<a class="code" href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#a00330b8913cac3b07029ac0c3350e806">reinterpret_input_as_3d</a>() ? input0.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) * input0.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(2) : input0.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1);</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span> </div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  <span class="comment">// If the output of GEMM has to be reinterpreted as 3D, the number of input0 rows (M) is obtained collapsing the second and third</span></div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  <span class="comment">// dimension of the output tensor</span></div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> dim0 = is_interleaved_transposed ? reshape_info.<a class="code" href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#a841b7b7f4c7b50931fabb298cfb7bed3">n</a>() : input1.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0);</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> dim1 = is_interleaved_transposed ? reshape_info.<a class="code" href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#a137948e04c296b448be2c0de97c6adcb">m</a>() / depth_output_gemm3d : m / depth_output_gemm3d;</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> dim2 = reinterpret_input_as_3d ? input0.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[3] : input0.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[2];</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> dim3 = reinterpret_input_as_3d ? 1 : input0.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[3];</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span> </div><div class="line"><a name="l00839"></a><span class="lineno"> 839</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input0.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span> </div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(0, dim0);</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, dim1);</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(2, reinterpret_output_as_3d ? depth_output_gemm3d : dim2);</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(3, reinterpret_output_as_3d ? dim2 : dim3);</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(4, reinterpret_output_as_3d ? dim3 : 1);</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span> </div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span> }</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span> </div><div class="line"><a name="l00860"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5076384fc9981ec1b497daab624a555b"> 860</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#adca241b012a5e00ddfcdc5a8db05a2a3">compute_mm_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input0, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input1, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml">GEMMReshapeInfo</a> &gemm_info)</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span> {</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(input0.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 4, <span class="stringliteral">"The number of dimensions for the matrix A must be <= 4"</span>);</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span> </div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> reinterpret_input_as_3d = gemm_info.<a class="code" href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#a00330b8913cac3b07029ac0c3350e806">reinterpret_input_as_3d</a>();</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> reinterpret_output_as_3d = gemm_info.<a class="code" href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#abbd888f118c2209bf7578eb4f8942a07">depth_output_gemm3d</a>() != 0;</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> depth_output_gemm3d = reinterpret_output_as_3d ? gemm_info.<a class="code" href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#abbd888f118c2209bf7578eb4f8942a07">depth_output_gemm3d</a>() : 1;</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span> </div><div class="line"><a name="l00868"></a><span class="lineno"> 868</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input0.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span> </div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  <span class="keywordflow">if</span>(!reinterpret_input_as_3d && !reinterpret_output_as_3d)</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  {</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(0, gemm_info.<a class="code" href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#a841b7b7f4c7b50931fabb298cfb7bed3">n</a>());</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, gemm_info.<a class="code" href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#a137948e04c296b448be2c0de97c6adcb">m</a>());</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  }</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  {</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  <span class="comment">// If the output of GEMM has to be reinterpreted as 3D, the number of input0 rows (M) is obtained collapsing the second and third</span></div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  <span class="comment">// dimension of the output tensor</span></div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch_size = reinterpret_input_as_3d ? input0.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[3] : input0.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[2];</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(0, gemm_info.<a class="code" href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#a841b7b7f4c7b50931fabb298cfb7bed3">n</a>());</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, gemm_info.<a class="code" href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#a137948e04c296b448be2c0de97c6adcb">m</a>() / depth_output_gemm3d);</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(2, reinterpret_output_as_3d ? depth_output_gemm3d : batch_size);</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(3, reinterpret_output_as_3d ? batch_size : 1);</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  }</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span> </div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span> }</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span> </div><div class="line"><a name="l00897"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5a875efce77eabbdc40028c4fd288b68"> 897</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#adca241b012a5e00ddfcdc5a8db05a2a3">compute_mm_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input0, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input1, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_g_e_m_m_kernel_info.xhtml">GEMMKernelInfo</a> &gemm_info)</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span> {</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(input0.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 4, <span class="stringliteral">"The number of dimensions for the matrix A must be <= 4"</span>);</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span> </div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> reinterpret_input_as_3d = gemm_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_kernel_info.xhtml#aba258627772f6f76f644911b027cbab5">reinterpret_input_as_3d</a>;</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> reinterpret_output_as_3d = gemm_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_kernel_info.xhtml#a370d5ff7251d8d974c634c9ed0dde094">depth_output_gemm3d</a> != 0;</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth_output_gemm3d = reinterpret_output_as_3d ? gemm_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_kernel_info.xhtml#a370d5ff7251d8d974c634c9ed0dde094">depth_output_gemm3d</a> : 1;</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span> </div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input0.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span> </div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  <span class="keywordflow">if</span>(!reinterpret_input_as_3d && !reinterpret_output_as_3d)</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  {</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(0, gemm_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_kernel_info.xhtml#a7d46b74409bd32b539089ef4721ea544">n</a>);</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, gemm_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_kernel_info.xhtml#a4033037df26775dd60840e66d38a08ba">m</a>);</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  }</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  {</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  <span class="comment">// If the output of GEMM has to be reinterpreted as 3D, the number of input0 rows (M) is obtained collapsing the second and third</span></div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span>  <span class="comment">// dimension of the output tensor</span></div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batch_size = reinterpret_input_as_3d ? input0.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[3] : input0.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[2];</div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(0, gemm_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_kernel_info.xhtml#a7d46b74409bd32b539089ef4721ea544">n</a>);</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, gemm_info.<a class="code" href="structarm__compute_1_1_g_e_m_m_kernel_info.xhtml#a4033037df26775dd60840e66d38a08ba">m</a> / depth_output_gemm3d);</div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(2, reinterpret_output_as_3d ? depth_output_gemm3d : batch_size);</div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(3, reinterpret_output_as_3d ? batch_size : 1);</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  }</div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span> </div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span> }</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span> </div><div class="line"><a name="l00934"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a1d3b8af21d25d9e6871673565f9f7532"> 934</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a1d3b8af21d25d9e6871673565f9f7532">compute_output_stage_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> gemm_3d_depth = 1, <span class="keywordtype">bool</span> batch_size_on_z = <span class="keyword">false</span>)</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span> {</div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>() != <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a> && gemm_3d_depth > 1);</div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span> </div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  <span class="keywordflow">if</span>(gemm_3d_depth > 1)</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  {</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  <span class="keywordflow">if</span>(batch_size_on_z)</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  {</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.shift_right(1);</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  }</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(0, input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#aa87f8fc26981b0f3228a78c83b95b802">x</a>());</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#ac4a1050be02b20b3f791b9a483f3abe2">y</a>() / gemm_3d_depth);</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(2, gemm_3d_depth);</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  }</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span> </div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span> }</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span> </div><div class="line"><a name="l00965"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ab288dc7ed664925c6f992b0e6aa3bc1b"> 965</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ab288dc7ed664925c6f992b0e6aa3bc1b">compute_strided_slice_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input,</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> &starts, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> &ends, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> &strides,</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  int32_t begin_mask, int32_t end_mask, int32_t shrink_axis_mask)</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span> {</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1helpers_1_1tensor__transform.xhtml">arm_compute::helpers::tensor_transform</a>;</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1helpers_1_1tensor__transform.xhtml#adb1ed814b11a751017250143fb1a9174">compute_strided_slice_output_shape</a>(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>(), starts, ends, strides, begin_mask, end_mask, shrink_axis_mask);</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span> }</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span> </div><div class="line"><a name="l00981"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a30c67d11c15378ffe67ca3c31e848917"> 981</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a30c67d11c15378ffe67ca3c31e848917">compute_slice_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#a865514e30baa11b11c8fa65f944532fb">input_shape</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> &starts, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> &ends)</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span> {</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1helpers_1_1tensor__transform.xhtml">arm_compute::helpers::tensor_transform</a>;</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span> </div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1helpers_1_1tensor__transform.xhtml#adb1ed814b11a751017250143fb1a9174">compute_strided_slice_output_shape</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a>,</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  starts, ends, <a class="code" href="namespacearm__compute.xhtml#a11916d4148a39a67794050373f54825a">BiStrides</a>(),</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  0, <a class="code" href="namespacearm__compute_1_1helpers_1_1tensor__transform.xhtml#a396d80389277ad8cd13a5e0567776191">construct_slice_end_mask</a>(ends), 0);</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span> }</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span> </div><div class="line"><a name="l00998"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac4d688e137d670d209b647ec37592a92"> 998</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac4d688e137d670d209b647ec37592a92">compute_batch_to_space_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keyword">const</span> <span class="keywordtype">int</span> block_x, <span class="keyword">const</span> <span class="keywordtype">int</span> block_y)</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span> {</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(block_x <= 0 || block_y <= 0);</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span> </div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>();</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_width = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_batch = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">DataLayoutDimension::BATCHES</a>);</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span> </div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_width, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_width] * block_x);</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_height, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_height] * block_y);</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_batch, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_batch] / (block_x * block_y));</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span> </div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span> }</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span> </div><div class="line"><a name="l01022"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a1d878f15b921e3f845ee9b683db359d7"> 1022</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a1d878f15b921e3f845ee9b683db359d7">compute_depth_to_space_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keywordtype">int</span> block)</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span> {</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(block < 2);</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span> </div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>();</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_width = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_channel = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span> </div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_width, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_width) * block);</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_height, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_height) * block);</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_channel, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_channel) / (block * block));</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span> </div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span> }</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span> </div><div class="line"><a name="l01047"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#abe10cfa0b480704109fd1a925301f58b"> 1047</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#abe10cfa0b480704109fd1a925301f58b">compute_split_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_splits)</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span> {</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> empty_shape;</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>  empty_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, 0);</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span> </div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> out_shape{ input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span> </div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  <span class="comment">// Return empty shape if axis is invalid</span></div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a> > input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a80a5f2d6e3a697c9aad893a3b4242615">num_dimensions</a>())</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>  {</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  <span class="keywordflow">return</span> empty_shape;</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  }</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span> </div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  <span class="keywordtype">size_t</span> axis_size = out_shape[<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>];</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span> </div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>  <span class="comment">// Return empty shape if num_split is not valid</span></div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>  <span class="keywordflow">if</span>(axis_size % num_splits)</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  {</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  <span class="keywordflow">return</span> empty_shape;</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>  }</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span> </div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>  out_shape[<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>] = axis_size / num_splits;</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>  <span class="keywordflow">return</span> out_shape;</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span> }</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span> </div><div class="line"><a name="l01082"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a585529133e437dc5f935d33de17c4abb"> 1082</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a585529133e437dc5f935d33de17c4abb">compute_space_to_batch_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keyword">const</span> <span class="keywordtype">int</span> block_x, <span class="keyword">const</span> <span class="keywordtype">int</span> block_y, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &padding_left, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &padding_right)</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span> {</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span> </div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>();</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_width = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_batch = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">DataLayoutDimension::BATCHES</a>);</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span> </div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_width, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_width] * block_x + padding_left.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a94b8468af876f5ab54020d5e9787a4f0">x</a>() + padding_right.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a94b8468af876f5ab54020d5e9787a4f0">x</a>());</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_height, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_height] * block_y + padding_left.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#aaaeb4853150b7d0e8b685fd08052924f">y</a>() + padding_right.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#aaaeb4853150b7d0e8b685fd08052924f">y</a>());</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_batch, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_batch] / (block_x * block_y));</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span> </div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span> }</div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span> </div><div class="line"><a name="l01105"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#abd7ceb09e076b5e3374aeb1fab08fc84"> 1105</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#abd7ceb09e076b5e3374aeb1fab08fc84">compute_space_to_depth_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, int32_t block_shape)</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span> {</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span> </div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>();</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_width = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_depth = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span> </div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_width, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_width] * block_shape);</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_height, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_height] * block_shape);</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(idx_depth, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[idx_depth] / (block_shape * block_shape));</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span> </div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span> }</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span> </div><div class="line"><a name="l01128"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ae3f672f124e4228db364bb811e770226"> 1128</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ae3f672f124e4228db364bb811e770226">compute_prior_box_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_prior_box_layer_info.xhtml">PriorBoxLayerInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>)</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span> {</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>  <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>();</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_w = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> idx_h = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> num_priors = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.aspect_ratios().size() * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.min_sizes().size() + <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.max_sizes().size();</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span> </div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{};</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(0, input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_w) * input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_h) * num_priors * 4);</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, 2);</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span> </div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span> }</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span> </div><div class="line"><a name="l01149"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a4e7f3187350db69156c1026860ace4e5"> 1149</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a4e7f3187350db69156c1026860ace4e5">compute_padded_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#a865514e30baa11b11c8fa65f944532fb">input_shape</a>, <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ac1a1b012674e0f1de071a611391828ad">PaddingList</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">padding</a>)</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span> {</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> padded_shape = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a>;</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> dim = 0; dim < <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">padding</a>.size(); ++dim)</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  {</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  <span class="keyword">const</span> <span class="keyword">auto</span> &padding_pair = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">padding</a>[dim];</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  <span class="keyword">const</span> uint32_t shape_on_index = (padded_shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a80a5f2d6e3a697c9aad893a3b4242615">num_dimensions</a>() <= dim) ? 1 : <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a>[dim];</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  padded_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(dim, padding_pair.first + shape_on_index + padding_pair.second);</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  }</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  <span class="keywordflow">return</span> padded_shape;</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span> }</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span> </div><div class="line"><a name="l01168"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a626cdfbacb377ee26462155d421717d3"> 1168</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a626cdfbacb377ee26462155d421717d3">compute_tiled_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#a865514e30baa11b11c8fa65f944532fb">input_shape</a>, <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#afe9e10e5fdfd1e2665ac17c75c0cacd8">Multiples</a> &multiples)</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span> {</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> tiled_shape = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a>;</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> dim = 0; dim < multiples.size(); ++dim)</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  {</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  tiled_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(dim, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a>[dim] * multiples[dim]);</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  }</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  <span class="keywordflow">return</span> tiled_shape;</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span> }</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span> </div><div class="line"><a name="l01185"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a0b0f2e38b65473f68561e0598c3107ff"> 1185</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a0b0f2e38b65473f68561e0598c3107ff">compute_reduced_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &input, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>)</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span> {</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>{ input };</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>, 1);</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span> </div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span> }</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span> </div><div class="line"><a name="l01200"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a3173d90757ec6ff31441b55883eafbca"> 1200</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a3173d90757ec6ff31441b55883eafbca">compute_upsample_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>)</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span> {</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a> = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>();</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_width = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx_height = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span> </div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> scale_out_shape(input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>());</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_x = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_width) * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.x();</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> out_y = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_height) * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.y();</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>  scale_out_shape.set(idx_width, out_x);</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>  scale_out_shape.set(idx_height, out_y);</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span> </div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  <span class="keywordflow">return</span> scale_out_shape;</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span> }</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span> </div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l01222"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#acb3f0c947411cfe1d8c5f67af2cad851"> 1222</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#acb3f0c947411cfe1d8c5f67af2cad851">extract_shape</a>(T *data)</div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span> {</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  <span class="keywordflow">return</span> data->info()->tensor_shape();</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span> }</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span> </div><div class="line"><a name="l01227"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#af79493c6c07a3eb2b3a27712221b66b8"> 1227</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#acb3f0c947411cfe1d8c5f67af2cad851">extract_shape</a>(<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *data)</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span> {</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>  <span class="keywordflow">return</span> data-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span> }</div><div class="line"><a name="l01231"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a4205a4be15a80494979ec1f19a66b21a"> 1231</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#acb3f0c947411cfe1d8c5f67af2cad851">extract_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *data)</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span> {</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  <span class="keywordflow">return</span> data-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span> }</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span> </div><div class="line"><a name="l01236"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ae7f0e5491e0f43e371f7db047a03dd4c"> 1236</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#acb3f0c947411cfe1d8c5f67af2cad851">extract_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> *data)</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span> {</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>  <span class="keywordflow">return</span> *data;</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span> }</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span> </div><div class="line"><a name="l01241"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a29fee5f196a5154a39526b7b88594059"> 1241</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#acb3f0c947411cfe1d8c5f67af2cad851">extract_shape</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> *data)</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span> {</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  <span class="keywordflow">return</span> *data;</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span> }</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span> </div><div class="line"><a name="l01253"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#acedb0877d41f2ae0591a2d4e84318140"> 1253</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#acedb0877d41f2ae0591a2d4e84318140">calculate_unstack_shape</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a>, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>)</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span> {</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a> > <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a>.num_dimensions());</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a>.remove_dimension(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>);</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a>;</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span> }</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span> </div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l01268"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6100aeb494088632647c3e0d639c99ab"> 1268</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6100aeb494088632647c3e0d639c99ab">calculate_concatenate_shape</a>(<span class="keyword">const</span> std::vector<T *> &input, <span class="keywordtype">size_t</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>)</div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span> {</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> out_shape = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#acb3f0c947411cfe1d8c5f67af2cad851">extract_shape</a>(input[0]);</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span> </div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span> <span class="preprocessor">#if defined(ARM_COMPUTE_ASSERTS_ENABLED)</span></div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  <span class="comment">// All dimensions must match except the axis one</span></div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < <a class="code" href="namespacearm__compute.xhtml#a769d636d7a3c7c84579a5f477a18bc9d">MAX_DIMS</a>; ++i)</div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>  {</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>  <span class="keywordflow">if</span>(i == <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>)</div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>  {</div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  <span class="keywordflow">continue</span>;</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>  }</div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span> </div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>  <span class="keywordflow">for</span>(<span class="keyword">const</span> <span class="keyword">auto</span> &tensor : input)</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>  {</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(tensor == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>  <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> = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#acb3f0c947411cfe1d8c5f67af2cad851">extract_shape</a>(tensor);</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(out_shape[i] != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>[i]);</div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>  }</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>  }</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span> <span class="preprocessor">#endif // defined(ARM_COMPUTE_ASSERTS_ENABLED)</span></div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span> </div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>  <span class="comment">// Calculate output shape</span></div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>  <span class="keywordtype">size_t</span> new_size = 0;</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>  <span class="keywordflow">for</span>(<span class="keyword">const</span> <span class="keyword">auto</span> &tensor : input)</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>  {</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>  <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> = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#acb3f0c947411cfe1d8c5f67af2cad851">extract_shape</a>(tensor);</div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  new_size += <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>[<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>];</div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>  }</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span> </div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>  out_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>, new_size);</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span> </div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>  <span class="keywordflow">return</span> out_shape;</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span> }</div><div class="line"><a name="l01310"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a32c692ab35f40f9ce6e27e1f4016e921"> 1310</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a32c692ab35f40f9ce6e27e1f4016e921">compute_stack_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> &a, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f2a0a7758d36198492af046c46ddbf5">num_tensors</a>)</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span> {</div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a> > a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>());</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 4);</div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span> </div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_out{ a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>  shape_out.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f2a0a7758d36198492af046c46ddbf5">num_tensors</a>);</div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span> </div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i_shift = 0;</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span> </div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>(); ++i)</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>  {</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  <span class="keywordflow">if</span>(i == <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>)</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  {</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>  i_shift++;</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>  }</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span> </div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  shape_out.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(i + i_shift, a.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[i]);</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>  }</div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>  <span class="keywordflow">return</span> shape_out;</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span> }</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span> </div><div class="line"><a name="l01332"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ae6578c8df1088c90dfa0d1be6bca605d"> 1332</a></span> <span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ae6578c8df1088c90dfa0d1be6bca605d">compute_gather_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#a865514e30baa11b11c8fa65f944532fb">input_shape</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &indices_shape, uint32_t actual_axis)</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span> {</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(indices_shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a80a5f2d6e3a697c9aad893a3b4242615">num_dimensions</a>() > 1);</div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a>.num_dimensions() > 4);</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(actual_axis >= <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a>.num_dimensions());</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span> </div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</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#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">input_shape</a>;</div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[actual_axis] = indices_shape[0];</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span> </div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span> }</div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span> } <span class="comment">// namespace shape_calculator</span></div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span> } <span class="comment">// namespace misc</span></div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span> } <span class="comment">// namespace arm_compute</span></div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span> <span class="preprocessor">#endif </span><span class="comment">/* __ARM_COMPUTE_MISC_SHAPE_CALCULATOR_H__ */</span><span class="preprocessor"></span></div><div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a1f4e725b8e1ea36b30e09dc08ae6961d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">arm_compute::ITensorInfo::num_dimensions</a></div><div class="ttdeci">virtual size_t num_dimensions() const =0</div><div class="ttdoc">The number of dimensions of the tensor (rank)</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_ab6d90bb06b3c19db6aba94975be64d10"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#ab6d90bb06b3c19db6aba94975be64d10">arm_compute::TensorShape::shift_right</a></div><div class="ttdeci">void shift_right(size_t step)</div><div class="ttdoc">Shifts right the tensor shape increasing its dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00143">TensorShape.h:143</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a30c67d11c15378ffe67ca3c31e848917"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a30c67d11c15378ffe67ca3c31e848917">arm_compute::misc::shape_calculator::compute_slice_shape</a></div><div class="ttdeci">TensorShape compute_slice_shape(const TensorShape &input_shape, const Coordinates &starts, const Coordinates &ends)</div><div class="ttdoc">Calculate the slice output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00981">ShapeCalculator.h:981</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="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a593fb7ecc281425b190cd6f20164b1a3"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a593fb7ecc281425b190cd6f20164b1a3">arm_compute::misc::shape_calculator::compute_permutation_output_shape</a></div><div class="ttdeci">TensorShape compute_permutation_output_shape(const ITensorInfo &input, const PermutationVector &perm)</div><div class="ttdoc">Calculate the permuted shape of an input given a permutation vector.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00072">ShapeCalculator.h:72</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a6100aeb494088632647c3e0d639c99ab"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6100aeb494088632647c3e0d639c99ab">arm_compute::misc::shape_calculator::calculate_concatenate_shape</a></div><div class="ttdeci">TensorShape calculate_concatenate_shape(const std::vector< T * > &input, size_t axis)</div><div class="ttdoc">Calculate the concatenate output shape of the concatenate operation along a single axis.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l01268">ShapeCalculator.h:1268</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acf5f12bbab64dd614bd8220c97fe484f"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acf5f12bbab64dd614bd8220c97fe484f">arm_compute::test::validation::data_layout</a></div><div class="ttdeci">const DataLayout data_layout</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00146">Im2Col.cpp:146</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_acb74edf42335de0dca0da5158b704c4b"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#acb74edf42335de0dca0da5158b704c4b">arm_compute::TensorShape::remove_dimension</a></div><div class="ttdeci">void remove_dimension(size_t n)</div><div class="ttdoc">Accessor to remove the dimension n from the tensor shape.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00110">TensorShape.h:110</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a04249f91ec2964d21a91bb7038821000"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a04249f91ec2964d21a91bb7038821000">arm_compute::misc::shape_calculator::compute_winograd_input_transform_shape</a></div><div class="ttdeci">TensorShape compute_winograd_input_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info)</div><div class="ttdoc">Calculate the winograd input transform shape.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00631">ShapeCalculator.h:631</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_acedb0877d41f2ae0591a2d4e84318140"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#acedb0877d41f2ae0591a2d4e84318140">arm_compute::misc::shape_calculator::calculate_unstack_shape</a></div><div class="ttdeci">TensorShape calculate_unstack_shape(TensorShape input_shape, unsigned int axis)</div><div class="ttdoc">Calculate the unstack shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l01253">ShapeCalculator.h:1253</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a5797726a8fbee3b11b92757c2f0031d6"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5797726a8fbee3b11b92757c2f0031d6">arm_compute::misc::shape_calculator::compute_transpose1xW_with_element_size_shape</a></div><div class="ttdeci">TensorShape compute_transpose1xW_with_element_size_shape(const ITensorInfo &b, int mult_transpose1xW_width=1)</div><div class="ttdoc">Calculate the transposed 1xW width element shape.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00301">ShapeCalculator.h:301</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_g_e_m_m_kernel_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_kernel_info.xhtml">arm_compute::GEMMKernelInfo</a></div><div class="ttdoc">Descriptor used by the GEMM kernels.</div><div class="ttdef"><b>Definition:</b> <a href="_kernel_descriptors_8h_source.xhtml#l00055">KernelDescriptors.h:55</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a32c692ab35f40f9ce6e27e1f4016e921"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a32c692ab35f40f9ce6e27e1f4016e921">arm_compute::misc::shape_calculator::compute_stack_shape</a></div><div class="ttdeci">TensorShape compute_stack_shape(const ITensorInfo &a, unsigned int axis, unsigned int num_tensors)</div><div class="ttdoc">Calculate the stack output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l01310">ShapeCalculator.h:1310</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_ac7147815227e7ba91814cfdcd38f23ed"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac7147815227e7ba91814cfdcd38f23ed">arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape</a></div><div class="ttdeci">TensorShape compute_depthwise_convolution_shape(const ITensorInfo &input, const ITensorInfo &weights, PadStrideInfo conv_info, unsigned int depth_multiplier, const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Calculate the depthwise convolution output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00410">ShapeCalculator.h:410</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ad3fd4136244e42ad89b01c02b904336d"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ad3fd4136244e42ad89b01c02b904336d">arm_compute::test::validation::dilation</a></div><div class="ttdeci">dilation</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00174">ConvolutionLayer.cpp:174</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a178f0d3d87f959e00a743328d95359d2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">arm_compute::ITensorInfo::dimension</a></div><div class="ttdeci">virtual size_t dimension(size_t index) const =0</div><div class="ttdoc">Return the size of the requested dimension.</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ac1a1b012674e0f1de071a611391828ad"><div class="ttname"><a href="namespacearm__compute.xhtml#ac1a1b012674e0f1de071a611391828ad">arm_compute::PaddingList</a></div><div class="ttdeci">std::vector< PaddingInfo > PaddingList</div><div class="ttdoc">List of padding information.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00445">Types.h:445</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a3d3eaddfd85c16e7a9a385ba0e6a45b0"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a3d3eaddfd85c16e7a9a385ba0e6a45b0">arm_compute::misc::shape_calculator::compute_roi_align_shape</a></div><div class="ttdeci">TensorShape compute_roi_align_shape(const ITensorInfo &input, const ITensorInfo &rois, ROIPoolingLayerInfo pool_info)</div><div class="ttdoc">Calculate the output roi align shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00784">ShapeCalculator.h:784</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_abbe3399a66c35c3c353030cd0a84c936"><div class="ttname"><a href="namespacearm__compute.xhtml#abbe3399a66c35c3c353030cd0a84c936">arm_compute::scaled_dimensions</a></div><div class="ttdeci">std::pair< unsigned int, unsigned int > scaled_dimensions(unsigned int width, unsigned int height, unsigned int kernel_width, unsigned int kernel_height, const PadStrideInfo &pad_stride_info, const Size2D &dilation=Size2D(1U, 1U))</div><div class="ttdoc">Returns expected width and height of output scaled tensor depending on dimensions rounding mode.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_utils_8cpp_source.xhtml#l00387">Utils.cpp:387</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_aa76b4a6e74940dabc5b7fc6b2dab3545"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">arm_compute::test::validation::b</a></div><div class="ttdeci">SimpleTensor< float > b</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_2_d_f_t_8cpp_source.xhtml#l00157">DFT.cpp:157</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a5d320d308c16b8ddda3c9d3f60fad79c"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5d320d308c16b8ddda3c9d3f60fad79c">arm_compute::misc::shape_calculator::compute_deep_convolution_shape</a></div><div class="ttdeci">TensorShape compute_deep_convolution_shape(const ITensorInfo &input, const ITensorInfo &weights, PadStrideInfo conv_info)</div><div class="ttdoc">Calculate the deep convolution shape output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00702">ShapeCalculator.h:702</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info_xhtml_acfef62abd2f827bf05666ba50a705c0d"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info.xhtml#acfef62abd2f827bf05666ba50a705c0d">arm_compute::GEMMLHSMatrixInfo::v0</a></div><div class="ttdeci">unsigned int v0</div><div class="ttdoc">Number of vertical blocks of size (m0xk0) stored on the same output row.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01860">Types.h:1860</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_g_e_m_m_kernel_info_xhtml_a370d5ff7251d8d974c634c9ed0dde094"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_kernel_info.xhtml#a370d5ff7251d8d974c634c9ed0dde094">arm_compute::GEMMKernelInfo::depth_output_gemm3d</a></div><div class="ttdeci">unsigned int depth_output_gemm3d</div><div class="ttdoc">Depth of the output tensor in case is reinterpreted as 3D.</div><div class="ttdef"><b>Definition:</b> <a href="_kernel_descriptors_8h_source.xhtml#l00060">KernelDescriptors.h:60</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_winograd_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_winograd_info.xhtml">arm_compute::WinogradInfo</a></div><div class="ttdoc">Winograd information.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l02043">Types.h:2043</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_e_m_m_reshape_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml">arm_compute::GEMMReshapeInfo</a></div><div class="ttdoc">GEMM reshape information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01724">Types.h:1724</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a00525ff582f16038a1d3819aa44a23a3"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a00525ff582f16038a1d3819aa44a23a3">arm_compute::test::validation::conv_info</a></div><div class="ttdeci">conv_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00599">Winograd.cpp:599</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_adca241b012a5e00ddfcdc5a8db05a2a3"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#adca241b012a5e00ddfcdc5a8db05a2a3">arm_compute::misc::shape_calculator::compute_mm_shape</a></div><div class="ttdeci">TensorShape compute_mm_shape(const ITensorInfo &input0, const ITensorInfo &input1, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info)</div><div class="ttdoc">Calculate the matrix multiplication output shape of two tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00822">ShapeCalculator.h:822</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a60ce6c017f70d978b48b101ce314969e"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a60ce6c017f70d978b48b101ce314969e">arm_compute::misc::shape_calculator::compute_reductionA_shape</a></div><div class="ttdeci">TensorShape compute_reductionA_shape(const ITensorInfo &b)</div><div class="ttdoc">Calculate the reductionA shape used in GEMMLowp.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00321">ShapeCalculator.h:321</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_ad16b366db486fec63b6d962937ec4545"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ad16b366db486fec63b6d962937ec4545">arm_compute::misc::shape_calculator::compute_softmax_shape</a></div><div class="ttdeci">TensorShape compute_softmax_shape(const ITensorInfo *input, size_t axis=1)</div><div class="ttdoc">Calculate the softmax output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00569">ShapeCalculator.h:569</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a865514e30baa11b11c8fa65f944532fb"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a865514e30baa11b11c8fa65f944532fb">arm_compute::test::validation::input_shape</a></div><div class="ttdeci">input_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00174">ConvolutionLayer.cpp:174</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::DataLayoutDimension::HEIGHT</a></div><div class="ttdoc">height</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a11916d4148a39a67794050373f54825a"><div class="ttname"><a href="namespacearm__compute.xhtml#a11916d4148a39a67794050373f54825a">arm_compute::BiStrides</a></div><div class="ttdeci">Coordinates BiStrides</div><div class="ttdoc">Bidirectional strides.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00049">Types.h:49</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a585529133e437dc5f935d33de17c4abb"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a585529133e437dc5f935d33de17c4abb">arm_compute::misc::shape_calculator::compute_space_to_batch_shape</a></div><div class="ttdeci">TensorShape compute_space_to_batch_shape(const ITensorInfo *input, const int block_x, const int block_y, const Size2D &padding_left, const Size2D &padding_right)</div><div class="ttdoc">Calculate the space to batch output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l01082">ShapeCalculator.h:1082</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info_xhtml_a2dc097fefeb3d010fb5570a19cac6cd3"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info.xhtml#a2dc097fefeb3d010fb5570a19cac6cd3">arm_compute::GEMMRHSMatrixInfo::h0</a></div><div class="ttdeci">unsigned int h0</div><div class="ttdoc">Number of horizontal blocks of size (k0xn0) stored on the same output row.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01870">Types.h:1870</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a809d18ccde99d938a68cb90ef53aa749"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a809d18ccde99d938a68cb90ef53aa749">arm_compute::test::validation::winograd_info</a></div><div class="ttdeci">winograd_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00330">Winograd.cpp:330</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00337">Error.h:337</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a1d878f15b921e3f845ee9b683db359d7"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a1d878f15b921e3f845ee9b683db359d7">arm_compute::misc::shape_calculator::compute_depth_to_space_shape</a></div><div class="ttdeci">TensorShape compute_depth_to_space_shape(const ITensorInfo *input, int block)</div><div class="ttdoc">Calculate the depth to space output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l01022">ShapeCalculator.h:1022</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_af98bc3ef5c65dbb63bc79700ccdd043b"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#af98bc3ef5c65dbb63bc79700ccdd043b">arm_compute::misc::shape_calculator::compute_rnn_shape</a></div><div class="ttdeci">TensorShape compute_rnn_shape(const ITensorInfo *input, const unsigned int batch_size)</div><div class="ttdoc">Calculate the RNN shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00805">ShapeCalculator.h:805</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info.xhtml">arm_compute::GEMMLHSMatrixInfo</a></div><div class="ttdoc">GEMM LHS (Left Hand Side) matrix information.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01856">Types.h:1856</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="namespacearm__compute_1_1test_1_1validation_xhtml_a4f2a0a7758d36198492af046c46ddbf5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f2a0a7758d36198492af046c46ddbf5">arm_compute::test::validation::num_tensors</a></div><div class="ttdeci">num_tensors</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_stack_layer_8cpp_source.xhtml#l00227">StackLayer.cpp:227</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1helpers_1_1tensor__transform_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1helpers_1_1tensor__transform.xhtml">arm_compute::helpers::tensor_transform</a></div><div class="ttdef"><b>Definition:</b> <a href="tensor__transform_8h_source.xhtml#l00033">tensor_transform.h:33</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a3b0c016b53e97663b39c2f3875f46c24"><div class="ttname"><a href="namespacearm__compute.xhtml#a3b0c016b53e97663b39c2f3875f46c24">arm_compute::compute_winograd_convolution_tiles</a></div><div class="ttdeci">Size2D compute_winograd_convolution_tiles(const Size2D &in_dims, const Size2D &kernel_size, const Size2D &output_tile_size, const PadStrideInfo &conv_info)</div><div class="ttdoc">Calculate the number of output tiles required by Winograd Convolution layer.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_helpers_8h_source.xhtml#l00744">Helpers.h:744</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_afbc83cd4145d161da4c026e1f5743e1d"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#afbc83cd4145d161da4c026e1f5743e1d">arm_compute::misc::shape_calculator::compute_reorg_output_shape</a></div><div class="ttdeci">TensorShape compute_reorg_output_shape(const ITensorInfo &input, int32_t stride)</div><div class="ttdoc">Calculate the output shape of the reorg layer given a stride.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00086">ShapeCalculator.h:86</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_size2_d_xhtml_a94b8468af876f5ab54020d5e9787a4f0"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml#a94b8468af876f5ab54020d5e9787a4f0">arm_compute::Size2D::x</a></div><div class="ttdeci">size_t x() const</div><div class="ttdoc">Semantic accessor for width as x.</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00077">Size2D.h:77</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_r_o_i_pooling_layer_info_xhtml_a5a00bf3cb11be124771cf7e3958e218e"><div class="ttname"><a href="classarm__compute_1_1_r_o_i_pooling_layer_info.xhtml#a5a00bf3cb11be124771cf7e3958e218e">arm_compute::ROIPoolingLayerInfo::pooled_width</a></div><div class="ttdeci">unsigned int pooled_width() const</div><div class="ttdoc">Get the pooled width of the layer.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01291">Types.h:1291</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_depthwise_convolution_reshape_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_depthwise_convolution_reshape_info.xhtml">arm_compute::DepthwiseConvolutionReshapeInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01829">Types.h:1829</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a3f10bc0f3e2a0126ce8c26e3d6a8fb96"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a3f10bc0f3e2a0126ce8c26e3d6a8fb96">arm_compute::misc::shape_calculator::compute_reshaped_depthwise_weights_shape</a></div><div class="ttdeci">TensorShape compute_reshaped_depthwise_weights_shape(const ITensorInfo &input, const DepthwiseConvolutionReshapeInfo &info)</div><div class="ttdoc">Calculate the reshaped shape of the weights to use in depthwise convolution.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00261">ShapeCalculator.h:261</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a8d52adbbcd2c53f837c96b5a3d15c4fb"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8d52adbbcd2c53f837c96b5a3d15c4fb">arm_compute::misc::shape_calculator::compute_interleaved_shape</a></div><div class="ttdeci">TensorShape compute_interleaved_shape(const ITensorInfo &a, int mult_interleave4x4_height=1, bool reinterpret_input_as_3d=false)</div><div class="ttdoc">Calculate the interleaved shape of an input tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00224">ShapeCalculator.h:224</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a1d3b8af21d25d9e6871673565f9f7532"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a1d3b8af21d25d9e6871673565f9f7532">arm_compute::misc::shape_calculator::compute_output_stage_shape</a></div><div class="ttdeci">TensorShape compute_output_stage_shape(const ITensorInfo &input, unsigned int gemm_3d_depth=1, bool batch_size_on_z=false)</div><div class="ttdoc">Calculate the matrix multiplication output shape of two tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00934">ShapeCalculator.h:934</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2018 ARM Limited.</div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_size2_d_xhtml_a02afeaaf8574e7a78d6b466ff2695052"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml#a02afeaaf8574e7a78d6b466ff2695052">arm_compute::Size2D::height</a></div><div class="ttdeci">size_t height</div><div class="ttdoc">Height of the image region or rectangle.</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00093">Size2D.h:93</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a1b843e3850ed7324d11f77882cc597ae"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a1b843e3850ed7324d11f77882cc597ae">arm_compute::misc::shape_calculator::compute_min_max_shape</a></div><div class="ttdeci">TensorShape compute_min_max_shape(const ITensorInfo *input)</div><div class="ttdoc">Calculate the min/max shape output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00734">ShapeCalculator.h:734</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1helpers_1_1tensor__transform_xhtml_a396d80389277ad8cd13a5e0567776191"><div class="ttname"><a href="namespacearm__compute_1_1helpers_1_1tensor__transform.xhtml#a396d80389277ad8cd13a5e0567776191">arm_compute::helpers::tensor_transform::construct_slice_end_mask</a></div><div class="ttdeci">int32_t construct_slice_end_mask(Coordinates ends)</div><div class="ttdoc">Constructs end mask in case we want to perform a slice operation using the strided slice interface.</div><div class="ttdef"><b>Definition:</b> <a href="tensor__transform_8cpp_source.xhtml#l00165">tensor_transform.cpp:165</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_ab288dc7ed664925c6f992b0e6aa3bc1b"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ab288dc7ed664925c6f992b0e6aa3bc1b">arm_compute::misc::shape_calculator::compute_strided_slice_shape</a></div><div class="ttdeci">TensorShape compute_strided_slice_shape(const ITensorInfo &input, const Coordinates &starts, const Coordinates &ends, const Coordinates &strides, int32_t begin_mask, int32_t end_mask, int32_t shrink_axis_mask)</div><div class="ttdoc">Calculate the strided slice output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00965">ShapeCalculator.h:965</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_acac1575c0edd329ceb4a54d9fe8dcb08"><div class="ttname"><a href="namespacearm__compute.xhtml#acac1575c0edd329ceb4a54d9fe8dcb08">arm_compute::DIV_CEIL</a></div><div class="ttdeci">constexpr auto DIV_CEIL(S val, T m) -> decltype((val+m - 1)/m)</div><div class="ttdoc">Calculate the rounded up quotient of val / m.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l00053">Utils.h:53</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a769d636d7a3c7c84579a5f477a18bc9d"><div class="ttname"><a href="namespacearm__compute.xhtml#a769d636d7a3c7c84579a5f477a18bc9d">arm_compute::MAX_DIMS</a></div><div class="ttdeci">constexpr size_t MAX_DIMS</div><div class="ttdoc">Constant value used to indicate maximum dimensions of a Window, TensorShape and Coordinates.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00037">Dimensions.h:37</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a69cb11b5b37f94a6bea9eaad9d13cccf"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">arm_compute::misc::shape_calculator::compute_transposed_shape</a></div><div class="ttdeci">TensorShape compute_transposed_shape(const ITensorInfo &input)</div><div class="ttdoc">Calculate the transposed shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00390">ShapeCalculator.h:390</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a21c3e11887f3acf9284ca763372c7da0"><div class="ttname"><a href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">arm_compute::permute</a></div><div class="ttdeci">void permute(Dimensions< T > &dimensions, const PermutationVector &perm)</div><div class="ttdoc">Permutes given Dimensions according to a permutation vector.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_helpers_8h_source.xhtml#l00570">Helpers.h:570</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_ae270329cfe3dbab009b700318e8af8b4"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ae270329cfe3dbab009b700318e8af8b4">arm_compute::misc::shape_calculator::compute_deconvolution_output_shape</a></div><div class="ttdeci">TensorShape compute_deconvolution_output_shape(const std::pair< unsigned int, unsigned int > &out_dims, const ITensorInfo &input, const ITensorInfo &weights)</div><div class="ttdoc">Calculate the output shape of the deconvolution layer.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00483">ShapeCalculator.h:483</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_ad242bedd6845b8fc13ade41cfc062c83"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ad242bedd6845b8fc13ade41cfc062c83">arm_compute::misc::shape_calculator::compute_pool_shape</a></div><div class="ttdeci">TensorShape compute_pool_shape(const ITensorInfo &input, PoolingLayerInfo pool_info)</div><div class="ttdoc">Calculate the output pool shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00751">ShapeCalculator.h:751</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_aa87f8fc26981b0f3228a78c83b95b802"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#aa87f8fc26981b0f3228a78c83b95b802">arm_compute::Dimensions::x</a></div><div class="ttdeci">T x() const</div><div class="ttdoc">Alias to access the size of the first dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00081">Dimensions.h:81</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_e_m_m_reshape_info_xhtml_a841b7b7f4c7b50931fabb298cfb7bed3"><div class="ttname"><a href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#a841b7b7f4c7b50931fabb298cfb7bed3">arm_compute::GEMMReshapeInfo::n</a></div><div class="ttdeci">int n() const</div><div class="ttdoc">Number of matrix B columns.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01762">Types.h:1762</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="namespacearm__compute_1_1test_1_1validation_xhtml_ae8f0126f051c787279a8c9ee3c3a5d55"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ae8f0126f051c787279a8c9ee3c3a5d55">arm_compute::test::validation::weights_shape</a></div><div class="ttdeci">weights_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00174">ConvolutionLayer.cpp:174</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a83efb6708574e67d13965bcd2059ad75"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a83efb6708574e67d13965bcd2059ad75">arm_compute::misc::shape_calculator::compute_flatten_shape</a></div><div class="ttdeci">TensorShape compute_flatten_shape(const ITensorInfo *input)</div><div class="ttdoc">Calculate the flattened output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00551">ShapeCalculator.h:551</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info_xhtml_a9bca3aa421b5fa7c07b0aa751b39b25f"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info.xhtml#a9bca3aa421b5fa7c07b0aa751b39b25f">arm_compute::GEMMRHSMatrixInfo::k0</a></div><div class="ttdeci">unsigned int k0</div><div class="ttdoc">Number of partial accumulations performed by the matrix multiplication.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01869">Types.h:1869</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_ae3f672f124e4228db364bb811e770226"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ae3f672f124e4228db364bb811e770226">arm_compute::misc::shape_calculator::compute_prior_box_shape</a></div><div class="ttdeci">TensorShape compute_prior_box_shape(const ITensorInfo &input, const PriorBoxLayerInfo &info)</div><div class="ttdoc">Calculate the prior box output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l01128">ShapeCalculator.h:1128</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a951fb0d8dcf2a2a338e26a59ffc9af17"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a951fb0d8dcf2a2a338e26a59ffc9af17">arm_compute::misc::shape_calculator::compute_vector_to_tensor_output_shape</a></div><div class="ttdeci">TensorShape compute_vector_to_tensor_output_shape(const TensorShape &input, size_t conv_w, size_t conv_h, const DataLayout &data_layout)</div><div class="ttdoc">Calculate the output tensor shape of a vector input given the convolution dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00051">ShapeCalculator.h:51</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_g_e_m_m_kernel_info_xhtml_a4033037df26775dd60840e66d38a08ba"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_kernel_info.xhtml#a4033037df26775dd60840e66d38a08ba">arm_compute::GEMMKernelInfo::m</a></div><div class="ttdeci">unsigned int m</div><div class="ttdoc">Number of LHS rows.</div><div class="ttdef"><b>Definition:</b> <a href="_kernel_descriptors_8h_source.xhtml#l00057">KernelDescriptors.h:57</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_g_e_m_m_kernel_info_xhtml_a7d46b74409bd32b539089ef4721ea544"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_kernel_info.xhtml#a7d46b74409bd32b539089ef4721ea544">arm_compute::GEMMKernelInfo::n</a></div><div class="ttdeci">unsigned int n</div><div class="ttdoc">Number of RHS columns.</div><div class="ttdef"><b>Definition:</b> <a href="_kernel_descriptors_8h_source.xhtml#l00058">KernelDescriptors.h:58</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_aa96e81276ee4f87ab386cd05a5539a7d"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">arm_compute::Window::DimX</a></div><div class="ttdeci">static constexpr size_t DimX</div><div class="ttdoc">Alias for dimension 0 also known as X dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00043">Window.h:43</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a112cc1d5093b7672bf11569659251a7c"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a112cc1d5093b7672bf11569659251a7c">arm_compute::Dimensions::collapse_from</a></div><div class="ttdeci">void collapse_from(size_t start)</div><div class="ttdoc">Collapse dimensions starting from a given point.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00162">Dimensions.h:162</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a389f89ab5121dad0906d0b7324fbf73d"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a389f89ab5121dad0906d0b7324fbf73d">arm_compute::misc::shape_calculator::compute_lhs_reshaped_shape</a></div><div class="ttdeci">TensorShape compute_lhs_reshaped_shape(const ITensorInfo &a, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d=false)</div><div class="ttdoc">Calculate the Left Hand Side matrix reshaped shape.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00144">ShapeCalculator.h:144</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info.xhtml">arm_compute::GEMMRHSMatrixInfo</a></div><div class="ttdoc">GEMM RHS (Right Hand Side) matrix information.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01866">Types.h:1866</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7c66505457d00ece3aa4b34cab80757d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">arm_compute::ITensorInfo::tensor_shape</a></div><div class="ttdeci">virtual const TensorShape & tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor.</div></div> |
| <div class="ttc" id="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info_xhtml_ac0c87b58d0d04796c81d8138231dccda"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_r_h_s_matrix_info.xhtml#ac0c87b58d0d04796c81d8138231dccda">arm_compute::GEMMRHSMatrixInfo::n0</a></div><div class="ttdeci">unsigned int n0</div><div class="ttdoc">Number of columns processed by the matrix multiplication.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01868">Types.h:1868</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a2270b3e1d20651d2d8341c858c890830"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a2270b3e1d20651d2d8341c858c890830">arm_compute::test::validation::num_groups</a></div><div class="ttdeci">const unsigned int num_groups</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00148">Im2Col.cpp:148</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a626cdfbacb377ee26462155d421717d3"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a626cdfbacb377ee26462155d421717d3">arm_compute::misc::shape_calculator::compute_tiled_shape</a></div><div class="ttdeci">TensorShape compute_tiled_shape(const TensorShape &input_shape, const Multiples &multiples)</div><div class="ttdoc">Calculate the tiled shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l01168">ShapeCalculator.h:1168</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a7fc93f37dac131a1a40b7921f9df3a9a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">arm_compute::test::validation::output_shape</a></div><div class="ttdeci">output_shape</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00174">ConvolutionLayer.cpp:174</a></div></div> |
| <div class="ttc" id="classarm__compute_1_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_1_1misc_1_1shape__calculator_xhtml_a09ad10a110d947fd9c444b2ea5e4c127"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a09ad10a110d947fd9c444b2ea5e4c127">arm_compute::misc::shape_calculator::compute_rhs_reshaped_shape</a></div><div class="ttdeci">TensorShape compute_rhs_reshaped_shape(const ITensorInfo &a, const GEMMRHSMatrixInfo &rhs_info)</div><div class="ttdoc">Calculate the Right Hand Side matrix reshaped shape.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00188">ShapeCalculator.h:188</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_g_e_m_m_kernel_info_xhtml_aba258627772f6f76f644911b027cbab5"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_kernel_info.xhtml#aba258627772f6f76f644911b027cbab5">arm_compute::GEMMKernelInfo::reinterpret_input_as_3d</a></div><div class="ttdeci">bool reinterpret_input_as_3d</div><div class="ttdoc">Flag used to reinterpret the input as 3D.</div><div class="ttdef"><b>Definition:</b> <a href="_kernel_descriptors_8h_source.xhtml#l00061">KernelDescriptors.h:61</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_prior_box_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_prior_box_layer_info.xhtml">arm_compute::PriorBoxLayerInfo</a></div><div class="ttdoc">PriorBox layer info.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00825">Types.h:825</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_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00676">Types.h:676</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a9aeced5a5128f60a31ea3e327a45ee21"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a9aeced5a5128f60a31ea3e327a45ee21">arm_compute::test::validation::has_bias</a></div><div class="ttdeci">const bool has_bias</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_im2_col_8cpp_source.xhtml#l00147">Im2Col.cpp:147</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a25e3751f07d4b2771a05d8d01a7f7620"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a25e3751f07d4b2771a05d8d01a7f7620">arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape</a></div><div class="ttdeci">TensorShape compute_winograd_filter_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info)</div><div class="ttdoc">Calculate the winograd filter transform shape.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00608">ShapeCalculator.h:608</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::DataLayoutDimension::CHANNEL</a></div><div class="ttdoc">channel</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a4e7f3187350db69156c1026860ace4e5"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a4e7f3187350db69156c1026860ace4e5">arm_compute::misc::shape_calculator::compute_padded_shape</a></div><div class="ttdeci">TensorShape compute_padded_shape(const TensorShape &input_shape, const PaddingList &padding)</div><div class="ttdoc">Calculate the padded shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l01149">ShapeCalculator.h:1149</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a8234d85cf61e3eb6fa00c012bee6f5bf"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8234d85cf61e3eb6fa00c012bee6f5bf">arm_compute::misc::shape_calculator::compute_deconvolution_upsampled_shape</a></div><div class="ttdeci">TensorShape compute_deconvolution_upsampled_shape(const ITensorInfo &input, const ITensorInfo &weights, unsigned int sx, unsigned int sy, std::pair< unsigned int, unsigned int > &out_dims, unsigned int &padx, unsigned int &pady)</div><div class="ttdoc">Calculate the upsampled output shape used for deconvolution.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00451">ShapeCalculator.h:451</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">arm_compute::DataLayoutDimension::BATCHES</a></div><div class="ttdoc">batches</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a></div><div class="ttdoc">Num samples, channels, height, width.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_size2_d_xhtml_aaaeb4853150b7d0e8b685fd08052924f"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml#aaaeb4853150b7d0e8b685fd08052924f">arm_compute::Size2D::y</a></div><div class="ttdeci">size_t y() const</div><div class="ttdoc">Semantic accessor for height as y.</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00086">Size2D.h:86</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_pooling_layer_info_xhtml_aec44a113cf93317f52355cbdea990966"><div class="ttname"><a href="classarm__compute_1_1_pooling_layer_info.xhtml#aec44a113cf93317f52355cbdea990966">arm_compute::PoolingLayerInfo::pad_stride_info</a></div><div class="ttdeci">PadStrideInfo pad_stride_info() const</div><div class="ttdoc">Get the padding and stride.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01252">Types.h:1252</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="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a69f9b3191aafc4905f9d029ff9d48fea"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69f9b3191aafc4905f9d029ff9d48fea">arm_compute::misc::shape_calculator::compute_reductionB_shape</a></div><div class="ttdeci">TensorShape compute_reductionB_shape(const ITensorInfo &a)</div><div class="ttdoc">Calculate the reductionB shape used in GEMMLowp.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00338">ShapeCalculator.h:338</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_pooling_layer_info_xhtml_a9e9048b8199d75905ce5102c21f6c424"><div class="ttname"><a href="classarm__compute_1_1_pooling_layer_info.xhtml#a9e9048b8199d75905ce5102c21f6c424">arm_compute::PoolingLayerInfo::pool_size</a></div><div class="ttdeci">const Size2D & pool_size() const</div><div class="ttdoc">Get the pooling size.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01247">Types.h:1247</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_ad2d402364fa822b0b7775081291eeca9"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">arm_compute::Window::DimY</a></div><div class="ttdeci">static constexpr size_t DimY</div><div class="ttdoc">Alias for dimension 1 also known as Y dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00045">Window.h:45</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_acb3f0c947411cfe1d8c5f67af2cad851"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#acb3f0c947411cfe1d8c5f67af2cad851">arm_compute::misc::shape_calculator::extract_shape</a></div><div class="ttdeci">TensorShape extract_shape(T *data)</div><div class="ttdoc">Get the tensor shape.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l01222">ShapeCalculator.h:1222</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_accc088009d44c521706aa98d6387ee21"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">arm_compute::test::validation::axis</a></div><div class="ttdeci">axis</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_stack_layer_8cpp_source.xhtml#l00226">StackLayer.cpp:226</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a3173d90757ec6ff31441b55883eafbca"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a3173d90757ec6ff31441b55883eafbca">arm_compute::misc::shape_calculator::compute_upsample_shape</a></div><div class="ttdeci">TensorShape compute_upsample_shape(const ITensorInfo &input, const Size2D &info)</div><div class="ttdoc">Calculate the upsampled shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l01200">ShapeCalculator.h:1200</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a5699c316d27b41f0790827791e88ae26"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5699c316d27b41f0790827791e88ae26">arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape</a></div><div class="ttdeci">TensorShape compute_winograd_output_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info)</div><div class="ttdoc">Calculate the winograd output transform shape.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00667">ShapeCalculator.h:667</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_size2_d_xhtml_a02bed8590a9ddf520e58a060059518ec"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml#a02bed8590a9ddf520e58a060059518ec">arm_compute::Size2D::width</a></div><div class="ttdeci">size_t width</div><div class="ttdoc">Width of the image region or rectangle.</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00092">Size2D.h:92</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_a893d17b56b9abc4423ce26e9a24ac5dc"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a893d17b56b9abc4423ce26e9a24ac5dc">arm_compute::Window::DimZ</a></div><div class="ttdeci">static constexpr size_t DimZ</div><div class="ttdoc">Alias for dimension 2 also known as Z dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00047">Window.h:47</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_r_o_i_pooling_layer_info_xhtml_aee6a0c75a44f00d3306101e14ffc3ebb"><div class="ttname"><a href="classarm__compute_1_1_r_o_i_pooling_layer_info.xhtml#aee6a0c75a44f00d3306101e14ffc3ebb">arm_compute::ROIPoolingLayerInfo::pooled_height</a></div><div class="ttdeci">unsigned int pooled_height() const</div><div class="ttdoc">Get the pooled height of the layer.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01296">Types.h:1296</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a6365b505b5c1b98916425bc692b6ea49"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6365b505b5c1b98916425bc692b6ea49">arm_compute::misc::shape_calculator::compute_weights_reshaped_shape</a></div><div class="ttdeci">TensorShape compute_weights_reshaped_shape(const ITensorInfo &weights, bool has_bias=false, unsigned int num_groups=1)</div><div class="ttdoc">Calculate the reshaped shape of the weights.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00113">ShapeCalculator.h:113</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_e_m_m_reshape_info_xhtml_a137948e04c296b448be2c0de97c6adcb"><div class="ttname"><a href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#a137948e04c296b448be2c0de97c6adcb">arm_compute::GEMMReshapeInfo::m</a></div><div class="ttdeci">int m() const</div><div class="ttdoc">Number of matrix A rows.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01754">Types.h:1754</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_r_o_i_pooling_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_r_o_i_pooling_layer_info.xhtml">arm_compute::ROIPoolingLayerInfo</a></div><div class="ttdoc">ROI Pooling Layer Information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01276">Types.h:1276</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_size2_d_xhtml"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml">arm_compute::Size2D</a></div><div class="ttdoc">Class for specifying the size of an image or rectangle.</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00034">Size2D.h:34</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="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">arm_compute::DataLayout::NHWC</a></div><div class="ttdoc">Num samples, height, width, channels.</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_ac4d688e137d670d209b647ec37592a92"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac4d688e137d670d209b647ec37592a92">arm_compute::misc::shape_calculator::compute_batch_to_space_shape</a></div><div class="ttdeci">TensorShape compute_batch_to_space_shape(const ITensorInfo *input, const int block_x, const int block_y)</div><div class="ttdoc">Calculate the batch to space output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00998">ShapeCalculator.h:998</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a70a2ef9fd754b5798a0a92656f8b5fcf"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a70a2ef9fd754b5798a0a92656f8b5fcf">arm_compute::misc::shape_calculator::compute_transpose1xW_shape</a></div><div class="ttdeci">TensorShape compute_transpose1xW_shape(const ITensorInfo &b)</div><div class="ttdoc">Calculate the transposed 1xW shape.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00284">ShapeCalculator.h:284</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_e_m_m_reshape_info_xhtml_abbd888f118c2209bf7578eb4f8942a07"><div class="ttname"><a href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#abbd888f118c2209bf7578eb4f8942a07">arm_compute::GEMMReshapeInfo::depth_output_gemm3d</a></div><div class="ttdeci">int depth_output_gemm3d() const</div><div class="ttdoc">Depth (third dimension) of the output tensor to be used with the GEMM3D kernel.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01797">Types.h:1797</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">ConvolutionLayer.cpp:180</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a9c54fb6cea3557692fe7c00c40bb40ad"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">arm_compute::TensorShape::set</a></div><div class="ttdeci">TensorShape & set(size_t dimension, size_t value, bool apply_dim_correction=true)</div><div class="ttdoc">Accessor to set the value of one of the dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00078">TensorShape.h:78</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::DataLayoutDimension::WIDTH</a></div><div class="ttdoc">width</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="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info_xhtml_a9bca3aa421b5fa7c07b0aa751b39b25f"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info.xhtml#a9bca3aa421b5fa7c07b0aa751b39b25f">arm_compute::GEMMLHSMatrixInfo::k0</a></div><div class="ttdeci">unsigned int k0</div><div class="ttdoc">Number of partial accumulations performed by the matrix multiplication.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01859">Types.h:1859</a></div></div> |
| <div class="ttc" id="tensor__transform_8h_xhtml"><div class="ttname"><a href="tensor__transform_8h.xhtml">tensor_transform.h</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info_xhtml_a41618231c7d7b990c42fe6846b4dde27"><div class="ttname"><a href="structarm__compute_1_1_g_e_m_m_l_h_s_matrix_info.xhtml#a41618231c7d7b990c42fe6846b4dde27">arm_compute::GEMMLHSMatrixInfo::m0</a></div><div class="ttdeci">unsigned int m0</div><div class="ttdoc">Number of rows processed by the matrix multiplication.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01858">Types.h:1858</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_ac4a1050be02b20b3f791b9a483f3abe2"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#ac4a1050be02b20b3f791b9a483f3abe2">arm_compute::Dimensions::y</a></div><div class="ttdeci">T y() const</div><div class="ttdoc">Alias to access the size of the second dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00086">Dimensions.h:86</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a0b0f2e38b65473f68561e0598c3107ff"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a0b0f2e38b65473f68561e0598c3107ff">arm_compute::misc::shape_calculator::compute_reduced_shape</a></div><div class="ttdeci">TensorShape compute_reduced_shape(const TensorShape &input, unsigned int axis)</div><div class="ttdoc">Calculate the reduced shape of a tensor given an axis.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l01185">ShapeCalculator.h:1185</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a264e2e6d3ff632e90d450435fce66d54"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a264e2e6d3ff632e90d450435fce66d54">arm_compute::misc::shape_calculator::compute_col2im_shape</a></div><div class="ttdeci">TensorShape compute_col2im_shape(const ITensorInfo &input, const Size2D &convolved_dims, bool batch_size_on_z, unsigned int num_groups=1)</div><div class="ttdoc">Calculate the Col2Im shape.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00359">ShapeCalculator.h:359</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#l00326">Helpers.inl:326</a></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="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00174">ConvolutionLayer.cpp:174</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_g_e_m_m_reshape_info_xhtml_a00330b8913cac3b07029ac0c3350e806"><div class="ttname"><a href="classarm__compute_1_1_g_e_m_m_reshape_info.xhtml#a00330b8913cac3b07029ac0c3350e806">arm_compute::GEMMReshapeInfo::reinterpret_input_as_3d</a></div><div class="ttdeci">bool reinterpret_input_as_3d() const</div><div class="ttdoc">Flag which specifies if the input tensor has to be reinterpreted as 3D.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01805">Types.h:1805</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a735a025fce26c1ef147b54426df18181"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a735a025fce26c1ef147b54426df18181">arm_compute::test::validation::padding</a></div><div class="ttdeci">const PaddingSize padding</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_absolute_difference_8cpp_source.xhtml#l00113">AbsoluteDifference.cpp:113</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_abe10cfa0b480704109fd1a925301f58b"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#abe10cfa0b480704109fd1a925301f58b">arm_compute::misc::shape_calculator::compute_split_shape</a></div><div class="ttdeci">TensorShape compute_split_shape(const ITensorInfo *input, unsigned int axis, unsigned int num_splits)</div><div class="ttdoc">Calculate the split output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l01047">ShapeCalculator.h:1047</a></div></div> |
| <div class="ttc" id="_kernel_descriptors_8h_xhtml"><div class="ttname"><a href="_kernel_descriptors_8h.xhtml">KernelDescriptors.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_pooling_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pooling_layer_info.xhtml">arm_compute::PoolingLayerInfo</a></div><div class="ttdoc">Pooling Layer Information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01191">Types.h:1191</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#l00114">Types.h:114</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_afe9e10e5fdfd1e2665ac17c75c0cacd8"><div class="ttname"><a href="namespacearm__compute.xhtml#afe9e10e5fdfd1e2665ac17c75c0cacd8">arm_compute::Multiples</a></div><div class="ttdeci">std::vector< uint32_t > Multiples</div><div class="ttdoc">Information to produce a tiled version of a Tensor.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00448">Types.h:448</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a8a9286d053e9f3a958064e4f3cdd02f7"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8a9286d053e9f3a958064e4f3cdd02f7">arm_compute::misc::shape_calculator::compute_im2col_conv_shape</a></div><div class="ttdeci">TensorShape compute_im2col_conv_shape(const ITensorInfo *input, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation, bool batch_size_on_z, unsigned int num_groups=1)</div><div class="ttdoc">Calculate the im2col output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00513">ShapeCalculator.h:513</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a8e15e87871211f98c2b566137e38ef99"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a8e15e87871211f98c2b566137e38ef99">arm_compute::TensorShape::collapse</a></div><div class="ttdeci">void collapse(size_t n, size_t first=0)</div><div class="ttdoc">Collapse the first n dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00132">TensorShape.h:132</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_pooling_layer_info_xhtml_a00bcea78a50c94ef3dc2a280b9ed8a7a"><div class="ttname"><a href="classarm__compute_1_1_pooling_layer_info.xhtml#a00bcea78a50c94ef3dc2a280b9ed8a7a">arm_compute::PoolingLayerInfo::is_global_pooling</a></div><div class="ttdeci">bool is_global_pooling() const</div><div class="ttdoc">Check if is global pooling.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01262">Types.h:1262</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_size2_d_xhtml_a75026dc1fa3840404ae4553010efcd52"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml#a75026dc1fa3840404ae4553010efcd52">arm_compute::Size2D::area</a></div><div class="ttdeci">size_t area() const</div><div class="ttdoc">The area of the image or rectangle calculated as (width * height)</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00053">Size2D.h:53</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a367b5090ab432bc7de2c32369e087ab1"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">arm_compute::ITensorInfo::data_layout</a></div><div class="ttdeci">virtual DataLayout data_layout() const =0</div><div class="ttdoc">Get the data layout of the tensor.</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_abd7ceb09e076b5e3374aeb1fab08fc84"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#abd7ceb09e076b5e3374aeb1fab08fc84">arm_compute::misc::shape_calculator::compute_space_to_depth_shape</a></div><div class="ttdeci">TensorShape compute_space_to_depth_shape(const ITensorInfo *input, int32_t block_shape)</div><div class="ttdoc">Calculate the space to batch output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l01105">ShapeCalculator.h:1105</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1helpers_1_1tensor__transform_xhtml_adb1ed814b11a751017250143fb1a9174"><div class="ttname"><a href="namespacearm__compute_1_1helpers_1_1tensor__transform.xhtml#adb1ed814b11a751017250143fb1a9174">arm_compute::helpers::tensor_transform::compute_strided_slice_output_shape</a></div><div class="ttdeci">TensorShape compute_strided_slice_output_shape(TensorShape input_shape, Coordinates starts, Coordinates ends, Coordinates strides, int32_t begin_mask=0, int32_t end_mask=0, int32_t shrink_axis_mask=0, bool return_unshrinked=false)</div><div class="ttdoc">Computes output shape of strided slice.</div><div class="ttdef"><b>Definition:</b> <a href="tensor__transform_8cpp_source.xhtml#l00132">tensor_transform.cpp:132</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_ae6578c8df1088c90dfa0d1be6bca605d"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ae6578c8df1088c90dfa0d1be6bca605d">arm_compute::misc::shape_calculator::compute_gather_shape</a></div><div class="ttdeci">TensorShape compute_gather_shape(const TensorShape &input_shape, const TensorShape &indices_shape, uint32_t actual_axis)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l01332">ShapeCalculator.h:1332</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond,...)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00328">Error.h:328</a></div></div> |
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