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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) 2017-2018 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="arm__compute_2core_2_utils_8h.xhtml">arm_compute/core/Utils.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "<a class="code" href="tensor__transform_8h.xhtml">arm_compute/core/utils/helpers/tensor_transform.h</a>"</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include <cmath></span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> {</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="keyword">namespace </span>misc</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> {</div><div class="line"><a name="l00039"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml"> 39</a></span> <span class="keyword">namespace </span>shape_calculator</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> {</div><div class="line"><a name="l00041"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a951fb0d8dcf2a2a338e26a59ffc9af17"> 41</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#a786677cbfb3f5677b4d84f3056eb08db">data_layout</a>)</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> {</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <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="l00048"></a><span class="lineno"> 48</span>  output_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(idx_w, conv_w);</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  output_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(idx_h, conv_h);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  output_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(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="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</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="l00053"></a><span class="lineno"> 53</span> }</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> </div><div class="line"><a name="l00055"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a593fb7ecc281425b190cd6f20164b1a3"> 55</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="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.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <a class="code" href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">permute</a>(output_shape, perm);</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</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="l00060"></a><span class="lineno"> 60</span> }</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div><div class="line"><a name="l00062"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#afbc83cd4145d161da4c026e1f5743e1d"> 62</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="l00063"></a><span class="lineno"> 63</span> {</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</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="l00065"></a><span class="lineno"> 65</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="l00066"></a><span class="lineno"> 66</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="l00067"></a><span class="lineno"> 67</span> </div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(stride <= 0);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</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="l00070"></a><span class="lineno"> 70</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="l00071"></a><span class="lineno"> 71</span> </div><div class="line"><a name="l00072"></a><span class="lineno"> 72</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="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</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="l00075"></a><span class="lineno"> 75</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="l00076"></a><span class="lineno"> 76</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="l00077"></a><span class="lineno"> 77</span> </div><div class="line"><a name="l00078"></a><span class="lineno"> 78</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="l00079"></a><span class="lineno"> 79</span> }</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00081"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a6365b505b5c1b98916425bc692b6ea49"> 81</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> &weights, <span class="keywordtype">bool</span> has_bias = <span class="keyword">false</span>, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_groups = 1)</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> {</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</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="l00084"></a><span class="lineno"> 84</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(num_groups == 0);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(weights.<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> && num_groups > 1);</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>((weights.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(3) % num_groups) != 0);</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="comment">// Calculate output shape</span></div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> weights_reshaped{ weights.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  weights_reshaped.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(3, weights_reshaped[3] / num_groups);</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>  weights_reshaped.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a8e15e87871211f98c2b566137e38ef99">collapse</a>(3);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> tmp_dim = weights_reshaped[0];</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  weights_reshaped.set(0, weights_reshaped[1]);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  weights_reshaped.set(1, tmp_dim + (has_bias ? 1 : 0));</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordflow">if</span>(weights.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() < 5)</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>  weights_reshaped.set(2, num_groups);</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  }</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> </div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keywordflow">return</span> weights_reshaped;</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> }</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> </div><div class="line"><a name="l00104"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8d52adbbcd2c53f837c96b5a3d15c4fb"> 104</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 class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">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="l00105"></a><span class="lineno"> 105</span> {</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</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="l00107"></a><span class="lineno"> 107</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(mult_interleave4x4_height < 1);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> interleave_width = 4 * mult_interleave4x4_height;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</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="l00110"></a><span class="lineno"> 110</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="l00111"></a><span class="lineno"> 111</span>  <span class="keywordflow">if</span>(reinterpret_input_as_3d)</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  {</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</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="l00114"></a><span class="lineno"> 114</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="l00115"></a><span class="lineno"> 115</span>  shape_interleaved_a.set(1, height);</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> </div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="comment">// When the data format is NHWC and the shapes are Nx1x1</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="comment">// the tensor shape num_dimensions is automatically set to 1 instead of 3.</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="comment">// To avoid failures by removing a dimension that doesn't exist</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="comment">// check if the number of dimensions is greater than 2.</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keywordflow">if</span>(shape_interleaved_a.num_dimensions() > 2)</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  {</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  shape_interleaved_a.remove_dimension(2);</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  }</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  }</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  {</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</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) / <span class="keyword">static_cast<</span><span class="keywordtype">float</span><span class="keyword">></span>(interleave_width)));</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> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordflow">return</span> shape_interleaved_a;</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> </div><div class="line"><a name="l00134"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a70a2ef9fd754b5798a0a92656f8b5fcf"> 134</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#a7b8004eef325a40dd43eb80755610fff">b</a>)</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> {</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</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="l00137"></a><span class="lineno"> 137</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_transposed1xW_b{ b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  shape_transposed1xW_b.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) * 16);</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  shape_transposed1xW_b.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(1, std::ceil(b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) / 16.f));</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordflow">return</span> shape_transposed1xW_b;</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> }</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> </div><div class="line"><a name="l00144"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5797726a8fbee3b11b92757c2f0031d6"> 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#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#a7b8004eef325a40dd43eb80755610fff">b</a>, <span class="keywordtype">int</span> mult_transpose1xW_width = 1)</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>  <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="l00147"></a><span class="lineno"> 147</span>  <span class="comment">// The transpose1xW output matrix will have the following shape:</span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</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="l00149"></a><span class="lineno"> 149</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(mult_transpose1xW_width < 1);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_transposed1xW_b{ b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> transpose_width = (16 / b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#aa459796b5489eca8a9160cb5dcf1a103">element_size</a>()) * mult_transpose1xW_width;</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  shape_transposed1xW_b.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) * transpose_width);</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  shape_transposed1xW_b.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(1, static_cast<size_t>(std::ceil(b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) / <span class="keyword">static_cast<</span><span class="keywordtype">float</span><span class="keyword">></span>(transpose_width))));</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keywordflow">return</span> shape_transposed1xW_b;</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> }</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div><div class="line"><a name="l00158"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a60ce6c017f70d978b48b101ce314969e"> 158</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#a7b8004eef325a40dd43eb80755610fff">b</a>)</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> {</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_vector_sum_col{ b.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keywordflow">if</span>(shape_vector_sum_col.num_dimensions() > 1)</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  {</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  shape_vector_sum_col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#acb74edf42335de0dca0da5158b704c4b">remove_dimension</a>(1);</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> </div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keywordflow">return</span> shape_vector_sum_col;</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> }</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> </div><div class="line"><a name="l00169"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69f9b3191aafc4905f9d029ff9d48fea"> 169</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 class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">a</a>)</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>  <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="l00172"></a><span class="lineno"> 172</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="l00173"></a><span class="lineno"> 173</span>  <span class="keywordflow">if</span>(shape_vector_sum_row.num_dimensions() > 1)</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  {</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</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="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> shape_vector_sum_row;</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="l00181"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a264e2e6d3ff632e90d450435fce66d54"> 181</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> num_groups = 1)</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> {</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(num_groups == 0);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</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="l00185"></a><span class="lineno"> 185</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>((num_groups > 1) && input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[2] != num_groups);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> </div><div class="line"><a name="l00187"></a><span class="lineno"> 187</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#a786677cbfb3f5677b4d84f3056eb08db">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="l00188"></a><span class="lineno"> 188</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> </div><div class="line"><a name="l00192"></a><span class="lineno"> 192</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="l00193"></a><span class="lineno"> 193</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="l00194"></a><span class="lineno"> 194</span>  <span class="comment">// as first three will be override by H,W,C data</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="keywordflow">if</span>(batch_size_on_z && num_groups == 1)</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  {</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</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="l00198"></a><span class="lineno"> 198</span>  }</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</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="l00200"></a><span class="lineno"> 200</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="l00201"></a><span class="lineno"> 201</span>  col2im_shape.set(channel_idx, input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()[0] * num_groups);</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> </div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keywordflow">return</span> col2im_shape;</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> </div><div class="line"><a name="l00206"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf"> 206</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="l00207"></a><span class="lineno"> 207</span> {</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</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="l00209"></a><span class="lineno"> 209</span> </div><div class="line"><a name="l00210"></a><span class="lineno"> 210</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="l00211"></a><span class="lineno"> 211</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="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> shape_transposed;</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="l00216"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ab51afcfdb9caea9e8185ae6a42ba4779"> 216</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#ab51afcfdb9caea9e8185ae6a42ba4779">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> &weights, <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#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth_multiplier)</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> {</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> input_shape{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> weights_shape{ weights.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> </div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a786677cbfb3f5677b4d84f3056eb08db">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="l00222"></a><span class="lineno"> 222</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_width = 0;</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_height = 0;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  std::tie(output_width, output_height) = <a class="code" href="namespacearm__compute.xhtml#a546c6bed3c307414e8d0934bc13259e5">scaled_dimensions</a>(input_shape[width_idx], input_shape[height_idx],</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  weights_shape[width_idx], weights_shape[height_idx],</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  conv_info);</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> </div><div class="line"><a name="l00232"></a><span class="lineno"> 232</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_shape };</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</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="l00234"></a><span class="lineno"> 234</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="l00235"></a><span class="lineno"> 235</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(channel_idx, input_shape[channel_idx] * depth_multiplier);</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="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span> }</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> </div><div class="line"><a name="l00240"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5c2e95d65407a26489ea38431ad851f5"> 240</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#a5c2e95d65407a26489ea38431ad851f5">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> &weights, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> sx, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> sy, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inner_border_right,</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inner_border_top,</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</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="l00243"></a><span class="lineno"> 243</span> {</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</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#a786677cbfb3f5677b4d84f3056eb08db">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="l00245"></a><span class="lineno"> 245</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</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>  <span class="comment">// Find the upsampled dimensions</span></div><div class="line"><a name="l00249"></a><span class="lineno"> 249</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 + inner_border_right + 1;</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</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 + inner_border_top + 1;</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> </div><div class="line"><a name="l00252"></a><span class="lineno"> 252</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="l00253"></a><span class="lineno"> 253</span>  padx = out_dims.first - (out_x - weights.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_w) + 1);</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  pady = out_dims.second - (out_y - weights.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(idx_h) + 1);</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  out_x += padx;</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  out_y += pady;</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> </div><div class="line"><a name="l00258"></a><span class="lineno"> 258</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="l00259"></a><span class="lineno"> 259</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="l00260"></a><span class="lineno"> 260</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="l00261"></a><span class="lineno"> 261</span> </div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keywordflow">return</span> scale_out_shape;</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> }</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span> </div><div class="line"><a name="l00265"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ae270329cfe3dbab009b700318e8af8b4"> 265</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> &weights)</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> {</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> input_shape{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> weights_shape{ weights.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> </div><div class="line"><a name="l00270"></a><span class="lineno"> 270</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#a786677cbfb3f5677b4d84f3056eb08db">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="l00271"></a><span class="lineno"> 271</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">DataLayoutDimension::BATCHES</a>);</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span> </div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> out_shape{ input_shape };</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  out_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(width_idx, out_dims.first);</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  out_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(height_idx, out_dims.second);</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  out_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(channel_idx, weights_shape[batch_idx]);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="keywordflow">return</span> out_shape;</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> }</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> </div><div class="line"><a name="l00283"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a8a9286d053e9f3a958064e4f3cdd02f7"> 283</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#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>, <span class="keywordtype">bool</span> has_bias, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> &dilation, <span class="keywordtype">bool</span> batch_size_on_z,</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_groups = 1)</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 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="l00287"></a><span class="lineno"> 287</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="l00288"></a><span class="lineno"> 288</span> </div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(num_groups == 0);</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(num_groups > 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="l00291"></a><span class="lineno"> 291</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(num_groups > 1 && batch_size_on_z);</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>  <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="l00294"></a><span class="lineno"> 294</span> </div><div class="line"><a name="l00295"></a><span class="lineno"> 295</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#a786677cbfb3f5677b4d84f3056eb08db">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="l00296"></a><span class="lineno"> 296</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span> </div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  std::pair<unsigned int, unsigned int> out_dims = <a class="code" href="namespacearm__compute.xhtml#a546c6bed3c307414e8d0934bc13259e5">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>, conv_info, dilation);</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</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] / num_groups * kernel_dims.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a75026dc1fa3840404ae4553010efcd52">area</a>() + (has_bias ? 1 : 0))); <span class="comment">// NOLINT</span></div><div class="line"><a name="l00302"></a><span class="lineno"> 302</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="l00303"></a><span class="lineno"> 303</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="l00304"></a><span class="lineno"> 304</span>  {</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</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="l00306"></a><span class="lineno"> 306</span>  }</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  {</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(2, num_groups);</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  }</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> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</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="l00315"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a83efb6708574e67d13965bcd2059ad75"> 315</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="l00316"></a><span class="lineno"> 316</span> {</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</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="l00318"></a><span class="lineno"> 318</span> </div><div class="line"><a name="l00319"></a><span class="lineno"> 319</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="l00320"></a><span class="lineno"> 320</span> </div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.collapse(3);</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>  <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="l00324"></a><span class="lineno"> 324</span> }</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> </div><div class="line"><a name="l00326"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ad16b366db486fec63b6d962937ec4545"> 326</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> axis = 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>  <span class="comment">// The output shape will be a 2D version of the input. For instance:</span></div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <span class="comment">// - [x,y,z] and axis 1 will return [x, y*z]</span></div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  <span class="comment">// - [x,y,z,w] and axis 2 will return [x*y, w*z]</span></div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <span class="comment">// - [x,y,z,w] and axis 3 will return [x*y*z, w]</span></div><div class="line"><a name="l00332"></a><span class="lineno"> 332</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="l00333"></a><span class="lineno"> 333</span> </div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="keywordflow">if</span>(axis < input->num_dimensions())</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  {</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="comment">// Collapse from axis onward (this changes the shape)</span></div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  shape2D.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a112cc1d5093b7672bf11569659251a7c">collapse_from</a>(axis);</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> </div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="comment">// Collapse the rest (collapse is inclusive)</span></div><div class="line"><a name="l00340"></a><span class="lineno"> 340</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="l00341"></a><span class="lineno"> 341</span>  }</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keywordflow">else</span></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>  <span class="comment">// Collapse everything</span></div><div class="line"><a name="l00345"></a><span class="lineno"> 345</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="l00346"></a><span class="lineno"> 346</span>  }</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span> </div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="keywordflow">if</span>(axis == 0)</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  {</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="comment">// If axis is zero the first dim should be one. Since</span></div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="comment">// collapse is an inclusive operation we need to shift</span></div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  shape2D.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#ab6d90bb06b3c19db6aba94975be64d10">shift_right</a>(1);</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  }</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> </div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keywordflow">return</span> shape2D;</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span> }</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> </div><div class="line"><a name="l00358"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac18940223de1db7f6ed6c49119be7cd8"> 358</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#ac18940223de1db7f6ed6c49119be7cd8">compute_interleave_custom_shape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &input, <span class="keyword">const</span> <span class="keywordtype">int</span> x_interleave, <span class="keyword">const</span> <span class="keywordtype">int</span> y_interleave)</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span> {</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <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="l00361"></a><span class="lineno"> 361</span> </div><div class="line"><a name="l00362"></a><span class="lineno"> 362</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>.x() * x_interleave);</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.set(1, std::ceil(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>.y() / <span class="keyword">static_cast<</span><span class="keywordtype">float</span><span class="keyword">></span>(y_interleave)));</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="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>;</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span> }</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span> </div><div class="line"><a name="l00368"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a734391819bbd2b0fa8400c06b7956d9e"> 368</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#a734391819bbd2b0fa8400c06b7956d9e">compute_fully_connected_reshaped_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="keywordtype">bool</span> transpose_weights, <span class="keywordtype">bool</span> is_batched_fc_layer, <span class="keyword">const</span> <span class="keywordtype">int</span> interleave)</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> <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="l00371"></a><span class="lineno"> 371</span> </div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="comment">// Transpose weights if the user hasn't done it</span></div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="keywordflow">if</span>(transpose_weights)</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>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">compute_transposed_shape</a>(*input);</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> </div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="comment">// If we run multiple batches we need 1xW transpose, too.</span></div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <span class="keywordflow">if</span>(is_batched_fc_layer)</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>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a69cb11b5b37f94a6bea9eaad9d13cccf">compute_transposed_shape</a>(input-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_tensor_shape(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>));</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a> = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac18940223de1db7f6ed6c49119be7cd8">compute_interleave_custom_shape</a>(output_shape, interleave, interleave);</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  }</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> </div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <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="l00386"></a><span class="lineno"> 386</span> }</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> </div><div class="line"><a name="l00388"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a25e3751f07d4b2771a05d8d01a7f7620"> 388</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="l00389"></a><span class="lineno"> 389</span> {</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</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="l00391"></a><span class="lineno"> 391</span> </div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> kernel_size = winograd_info.<a class="code" href="structarm__compute_1_1_winograd_info.xhtml#aca57076ead1d06c47d3d32f4302b14ac">kernel_size</a>;</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> output_tile_size = winograd_info.<a class="code" href="structarm__compute_1_1_winograd_info.xhtml#a5cd6561e9acc0cf9ba11bc2f51ec7a33">output_tile_size</a>;</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</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="l00395"></a><span class="lineno"> 395</span> </div><div class="line"><a name="l00396"></a><span class="lineno"> 396</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="l00397"></a><span class="lineno"> 397</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="l00398"></a><span class="lineno"> 398</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="l00399"></a><span class="lineno"> 399</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="l00400"></a><span class="lineno"> 400</span> </div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="keywordflow">return</span> tensor_shape;</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span> }</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span> </div><div class="line"><a name="l00404"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a04249f91ec2964d21a91bb7038821000"> 404</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="l00405"></a><span class="lineno"> 405</span> {</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</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#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a> = winograd_info.<a class="code" href="structarm__compute_1_1_winograd_info.xhtml#ad99e40b120f0a9e96821c08bf60a84f2">convolution_info</a>;</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> kernel_size = winograd_info.<a class="code" href="structarm__compute_1_1_winograd_info.xhtml#aca57076ead1d06c47d3d32f4302b14ac">kernel_size</a>;</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> output_tile_size = winograd_info.<a class="code" href="structarm__compute_1_1_winograd_info.xhtml#a5cd6561e9acc0cf9ba11bc2f51ec7a33">output_tile_size</a>;</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_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="l00410"></a><span class="lineno"> 410</span> </div><div class="line"><a name="l00411"></a><span class="lineno"> 411</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="l00412"></a><span class="lineno"> 412</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="l00413"></a><span class="lineno"> 413</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="l00414"></a><span class="lineno"> 414</span> </div><div class="line"><a name="l00415"></a><span class="lineno"> 415</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="l00416"></a><span class="lineno"> 416</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="l00417"></a><span class="lineno"> 417</span>  kernel_size,</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  output_tile_size,</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  conv_info);</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> <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="l00422"></a><span class="lineno"> 422</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="l00423"></a><span class="lineno"> 423</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="l00424"></a><span class="lineno"> 424</span> </div><div class="line"><a name="l00425"></a><span class="lineno"> 425</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="l00426"></a><span class="lineno"> 426</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="l00427"></a><span class="lineno"> 427</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="l00428"></a><span class="lineno"> 428</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="l00429"></a><span class="lineno"> 429</span> </div><div class="line"><a name="l00430"></a><span class="lineno"> 430</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="l00431"></a><span class="lineno"> 431</span> }</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span> </div><div class="line"><a name="l00433"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5699c316d27b41f0790827791e88ae26"> 433</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="l00434"></a><span class="lineno"> 434</span> {</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</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#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a> = winograd_info.<a class="code" href="structarm__compute_1_1_winograd_info.xhtml#ad99e40b120f0a9e96821c08bf60a84f2">convolution_info</a>;</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> kernel_size = winograd_info.<a class="code" href="structarm__compute_1_1_winograd_info.xhtml#aca57076ead1d06c47d3d32f4302b14ac">kernel_size</a>;</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a> input_dimensions = winograd_info.<a class="code" href="structarm__compute_1_1_winograd_info.xhtml#af9ef316b2c98c946b47cd18f1319b93f">input_dimensions</a>;</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</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#a786677cbfb3f5677b4d84f3056eb08db">data_layout</a> = winograd_info.<a class="code" href="structarm__compute_1_1_winograd_info.xhtml#a0bc60aaf23b2d3ed3b344576a708e84e">output_data_layout</a>;</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span> </div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <span class="comment">// Compute output shape</span></div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_width = 0;</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_height = 0;</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  std::tie(output_width, output_height) = <a class="code" href="namespacearm__compute.xhtml#a546c6bed3c307414e8d0934bc13259e5">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="l00444"></a><span class="lineno"> 444</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>, conv_info);</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span> </div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <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="l00447"></a><span class="lineno"> 447</span> </div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <span class="comment">// Output dimension</span></div><div class="line"><a name="l00449"></a><span class="lineno"> 449</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="l00450"></a><span class="lineno"> 450</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="l00451"></a><span class="lineno"> 451</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="l00452"></a><span class="lineno"> 452</span> </div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  tensor_shape.set(<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>), out_w);</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  tensor_shape.set(<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>), out_h);</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  tensor_shape.set(<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>), out_c);</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span> </div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <span class="keywordflow">return</span> tensor_shape;</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span> }</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span> </div><div class="line"><a name="l00460"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5d320d308c16b8ddda3c9d3f60fad79c"> 460</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> &weights, <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#acbf8f8a6dd185de04c1981c57a8963cf">conv_info</a>)</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span> {</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> input_shape{ input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> weights_shape{ weights.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>() };</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span> </div><div class="line"><a name="l00465"></a><span class="lineno"> 465</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="l00466"></a><span class="lineno"> 466</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="l00467"></a><span class="lineno"> 467</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="l00468"></a><span class="lineno"> 468</span> </div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input_width = input_shape[idx_width];</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input_height = input_shape[idx_height];</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weights_width = weights_shape[idx_width];</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weights_height = weights_shape[idx_height];</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weights_out_channel = weights_shape[3];</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_width = 0;</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> output_height = 0;</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  std::tie(output_width, output_height) = <a class="code" href="namespacearm__compute.xhtml#a546c6bed3c307414e8d0934bc13259e5">scaled_dimensions</a>(input_width, input_height, weights_width, weights_height, conv_info);</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span> </div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <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_shape };</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</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="l00480"></a><span class="lineno"> 480</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="l00481"></a><span class="lineno"> 481</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="l00482"></a><span class="lineno"> 482</span> </div><div class="line"><a name="l00483"></a><span class="lineno"> 483</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="l00484"></a><span class="lineno"> 484</span> }</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span> </div><div class="line"><a name="l00486"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a1b843e3850ed7324d11f77882cc597ae"> 486</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="l00487"></a><span class="lineno"> 487</span> {</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</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="l00489"></a><span class="lineno"> 489</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="l00490"></a><span class="lineno"> 490</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="l00491"></a><span class="lineno"> 491</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="l00492"></a><span class="lineno"> 492</span> </div><div class="line"><a name="l00493"></a><span class="lineno"> 493</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="l00494"></a><span class="lineno"> 494</span> }</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span> </div><div class="line"><a name="l00496"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ad242bedd6845b8fc13ade41cfc062c83"> 496</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="l00497"></a><span class="lineno"> 497</span> {</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pooled_w = 0;</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pooled_h = 0;</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span> </div><div class="line"><a name="l00501"></a><span class="lineno"> 501</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="l00502"></a><span class="lineno"> 502</span> </div><div class="line"><a name="l00503"></a><span class="lineno"> 503</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="l00504"></a><span class="lineno"> 504</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="l00505"></a><span class="lineno"> 505</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="l00506"></a><span class="lineno"> 506</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="l00507"></a><span class="lineno"> 507</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="l00508"></a><span class="lineno"> 508</span> </div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  std::tie(pooled_w, pooled_h) = <a class="code" href="namespacearm__compute.xhtml#a546c6bed3c307414e8d0934bc13259e5">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="l00510"></a><span class="lineno"> 510</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a7fc93f37dac131a1a40b7921f9df3a9a">output_shape</a>[idx_height],</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  pool_size_x,</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  pool_size_y,</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</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="l00514"></a><span class="lineno"> 514</span> </div><div class="line"><a name="l00515"></a><span class="lineno"> 515</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="l00516"></a><span class="lineno"> 516</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="l00517"></a><span class="lineno"> 517</span> </div><div class="line"><a name="l00518"></a><span class="lineno"> 518</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="l00519"></a><span class="lineno"> 519</span> }</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span> </div><div class="line"><a name="l00521"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#af98bc3ef5c65dbb63bc79700ccdd043b"> 521</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="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>  <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="l00525"></a><span class="lineno"> 525</span> </div><div class="line"><a name="l00526"></a><span class="lineno"> 526</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="l00527"></a><span class="lineno"> 527</span> }</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span> </div><div class="line"><a name="l00529"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#adca241b012a5e00ddfcdc5a8db05a2a3"> 529</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="l00530"></a><span class="lineno"> 530</span> {</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</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="l00532"></a><span class="lineno"> 532</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="l00533"></a><span class="lineno"> 533</span> </div><div class="line"><a name="l00534"></a><span class="lineno"> 534</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="l00535"></a><span class="lineno"> 535</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="l00536"></a><span class="lineno"> 536</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="l00537"></a><span class="lineno"> 537</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="l00538"></a><span class="lineno"> 538</span> </div><div class="line"><a name="l00539"></a><span class="lineno"> 539</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="l00540"></a><span class="lineno"> 540</span>  <span class="comment">// dimension of the output tensor</span></div><div class="line"><a name="l00541"></a><span class="lineno"> 541</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="l00542"></a><span class="lineno"> 542</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="l00543"></a><span class="lineno"> 543</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="l00544"></a><span class="lineno"> 544</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="l00545"></a><span class="lineno"> 545</span> </div><div class="line"><a name="l00546"></a><span class="lineno"> 546</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="l00547"></a><span class="lineno"> 547</span> </div><div class="line"><a name="l00548"></a><span class="lineno"> 548</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="l00549"></a><span class="lineno"> 549</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="l00550"></a><span class="lineno"> 550</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="l00551"></a><span class="lineno"> 551</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="l00552"></a><span class="lineno"> 552</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="l00553"></a><span class="lineno"> 553</span> </div><div class="line"><a name="l00554"></a><span class="lineno"> 554</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="l00555"></a><span class="lineno"> 555</span> }</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span> </div><div class="line"><a name="l00557"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a1d3b8af21d25d9e6871673565f9f7532"> 557</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="l00558"></a><span class="lineno"> 558</span> {</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(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="l00560"></a><span class="lineno"> 560</span> </div><div class="line"><a name="l00561"></a><span class="lineno"> 561</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="l00562"></a><span class="lineno"> 562</span>  <span class="keywordflow">if</span>(gemm_3d_depth > 1)</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  {</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <span class="keywordflow">if</span>(batch_size_on_z)</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  {</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  output_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#ab6d90bb06b3c19db6aba94975be64d10">shift_right</a>(1);</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  }</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  output_shape.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="l00569"></a><span class="lineno"> 569</span>  output_shape.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="l00570"></a><span class="lineno"> 570</span>  output_shape.set(2, gemm_3d_depth);</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  }</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span> </div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <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="l00574"></a><span class="lineno"> 574</span> }</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span> </div><div class="line"><a name="l00576"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ab288dc7ed664925c6f992b0e6aa3bc1b"> 576</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="l00577"></a><span class="lineno"> 577</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="l00578"></a><span class="lineno"> 578</span>  int32_t begin_mask, int32_t end_mask, int32_t shrink_axis_mask)</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span> {</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  <span class="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="l00581"></a><span class="lineno"> 581</span> </div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &input_shape = input.<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span> </div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  <span class="comment">// Get actual start, end coordinates and strides</span></div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> final_strides = <a class="code" href="namespacearm__compute_1_1helpers_1_1tensor__transform.xhtml#a125684caafdab8cdee40e424166cf61f">strided_slice_strides</a>(input_shape, strides);</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> starts_abs = <a class="code" href="namespacearm__compute_1_1helpers_1_1tensor__transform.xhtml#aa5cd68178f4a5d7e9f3be67c19178fbe">strided_slice_absolute_start_coords</a>(input_shape, starts, final_strides, begin_mask);</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> ends_abs = <a class="code" href="namespacearm__compute_1_1helpers_1_1tensor__transform.xhtml#a88bcbb798479cb74a34e0c4b1de181c8">strided_slice_absolute_end_coords</a>(input_shape, starts_abs, ends, final_strides, end_mask, shrink_axis_mask);</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span> </div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1helpers_1_1tensor__transform.xhtml#af13ccc2d4ed13a513cf7d031233cbfdd">compute_strided_slice_output_shape</a>(input_shape, starts_abs, ends_abs, final_strides);</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> </div><div class="line"><a name="l00592"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac4d688e137d670d209b647ec37592a92"> 592</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="l00593"></a><span class="lineno"> 593</span> {</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</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="l00595"></a><span class="lineno"> 595</span> </div><div class="line"><a name="l00596"></a><span class="lineno"> 596</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#a786677cbfb3f5677b4d84f3056eb08db">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="l00597"></a><span class="lineno"> 597</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">DataLayoutDimension::BATCHES</a>);</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span> </div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <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="l00602"></a><span class="lineno"> 602</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="l00603"></a><span class="lineno"> 603</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="l00604"></a><span class="lineno"> 604</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="l00605"></a><span class="lineno"> 605</span> </div><div class="line"><a name="l00606"></a><span class="lineno"> 606</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="l00607"></a><span class="lineno"> 607</span> }</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span> </div><div class="line"><a name="l00609"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#abe10cfa0b480704109fd1a925301f58b"> 609</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> axis, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_splits)</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span> {</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> empty_shape;</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</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="l00613"></a><span class="lineno"> 613</span> </div><div class="line"><a name="l00614"></a><span class="lineno"> 614</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="l00615"></a><span class="lineno"> 615</span> </div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <span class="comment">// Return empty shape if axis is invalid</span></div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <span class="keywordflow">if</span>(axis > 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="l00618"></a><span class="lineno"> 618</span>  {</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <span class="keywordflow">return</span> empty_shape;</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> </div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  <span class="keywordtype">size_t</span> axis_size = out_shape[axis];</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span> </div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  <span class="comment">// Return empty shape if num_split is not valid</span></div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <span class="keywordflow">if</span>(axis_size % num_splits)</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  {</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  <span class="keywordflow">return</span> empty_shape;</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  }</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span> </div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  out_shape[axis] = axis_size / num_splits;</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <span class="keywordflow">return</span> out_shape;</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> </div><div class="line"><a name="l00634"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a585529133e437dc5f935d33de17c4abb"> 634</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="l00635"></a><span class="lineno"> 635</span> {</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</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="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> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a786677cbfb3f5677b4d84f3056eb08db">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="l00639"></a><span class="lineno"> 639</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a628bcf7e10fc1c2a984f379a1ec3393a">DataLayoutDimension::BATCHES</a>);</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span> </div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  <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="l00644"></a><span class="lineno"> 644</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="l00645"></a><span class="lineno"> 645</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="l00646"></a><span class="lineno"> 646</span> </div><div class="line"><a name="l00647"></a><span class="lineno"> 647</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="l00648"></a><span class="lineno"> 648</span> }</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span> </div><div class="line"><a name="l00650"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ae3f672f124e4228db364bb811e770226"> 650</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#a096668313a9a819d54a2e65ec21ff0cc">info</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="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a786677cbfb3f5677b4d84f3056eb08db">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="l00653"></a><span class="lineno"> 653</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>);</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> num_priors = info.<a class="code" href="classarm__compute_1_1_prior_box_layer_info.xhtml#a6a8ad2364ce45d0cb7c6938544711f7c">aspect_ratios</a>().size() * info.<a class="code" href="classarm__compute_1_1_prior_box_layer_info.xhtml#a147b1e13f347e7b995de58f8fafd6723">min_sizes</a>().size() + info.<a class="code" href="classarm__compute_1_1_prior_box_layer_info.xhtml#a985940fefc02bdbc0ea1c916bb0ac82b">max_sizes</a>().size();</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>  <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="l00658"></a><span class="lineno"> 658</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="l00659"></a><span class="lineno"> 659</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="l00660"></a><span class="lineno"> 660</span> </div><div class="line"><a name="l00661"></a><span class="lineno"> 661</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="l00662"></a><span class="lineno"> 662</span> }</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span> </div><div class="line"><a name="l00664"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a4e7f3187350db69156c1026860ace4e5"> 664</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> &input_shape, <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="l00665"></a><span class="lineno"> 665</span> {</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> padded_shape = input_shape;</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> dim = 0; dim < padding.size(); ++dim)</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>  padded_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(dim, padding[dim].first + input_shape[dim] + padding[dim].second);</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  }</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  <span class="keywordflow">return</span> padded_shape;</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span> }</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span> </div><div class="line"><a name="l00674"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a3173d90757ec6ff31441b55883eafbca"> 674</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#a096668313a9a819d54a2e65ec21ff0cc">info</a>)</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span> {</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</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#a786677cbfb3f5677b4d84f3056eb08db">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="l00677"></a><span class="lineno"> 677</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>);</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</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>(data_layout, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</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> 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="l00681"></a><span class="lineno"> 681</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) * info.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#a94b8468af876f5ab54020d5e9787a4f0">x</a>();</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</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) * info.<a class="code" href="classarm__compute_1_1_size2_d.xhtml#aaaeb4853150b7d0e8b685fd08052924f">y</a>();</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  scale_out_shape.set(idx_width, out_x);</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  scale_out_shape.set(idx_height, out_y);</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span> </div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  <span class="keywordflow">return</span> scale_out_shape;</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span> }</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span> </div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00690"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#acb3f0c947411cfe1d8c5f67af2cad851"> 690</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="l00691"></a><span class="lineno"> 691</span> {</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  <span class="keywordflow">return</span> data->info()->tensor_shape();</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span> }</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span> </div><div class="line"><a name="l00695"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#af79493c6c07a3eb2b3a27712221b66b8"> 695</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="l00696"></a><span class="lineno"> 696</span> {</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</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="l00698"></a><span class="lineno"> 698</span> }</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span> </div><div class="line"><a name="l00700"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ae7f0e5491e0f43e371f7db047a03dd4c"> 700</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="l00701"></a><span class="lineno"> 701</span> {</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  <span class="keywordflow">return</span> *data;</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> </div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00706"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a90d6bd879f5e05b591dc58892348c65c"> 706</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#a90d6bd879f5e05b591dc58892348c65c">calculate_depth_concatenate_shape</a>(<span class="keyword">const</span> std::vector<T *> &inputs_vector)</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span> {</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  <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>(inputs_vector[0]);</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span> </div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  <span class="keywordtype">size_t</span> max_x = 0;</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  <span class="keywordtype">size_t</span> max_y = 0;</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  <span class="keywordtype">size_t</span> depth = 0;</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span> </div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  <span class="keywordflow">for</span>(<span class="keyword">const</span> <span class="keyword">auto</span> &tensor : inputs_vector)</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  {</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</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="l00717"></a><span class="lineno"> 717</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="l00718"></a><span class="lineno"> 718</span>  max_x = std::max(shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#aa87f8fc26981b0f3228a78c83b95b802">x</a>(), max_x);</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  max_y = std::max(shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#ac4a1050be02b20b3f791b9a483f3abe2">y</a>(), max_y);</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  depth += shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#abb29a685080e999c2a0cb874d2f7bb5a">z</a>();</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  }</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span> </div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  out_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, max_x);</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  out_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(1, max_y);</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  out_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(2, depth);</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>  <span class="keywordflow">return</span> out_shape;</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span> }</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span> </div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00731"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ad98c5b23d8630ce7b15c961992fd3dac"> 731</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#ad98c5b23d8630ce7b15c961992fd3dac">calculate_width_concatenate_shape</a>(<span class="keyword">const</span> std::vector<T *> &inputs_vector)</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span> {</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</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>(inputs_vector[0]);</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span> </div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  <span class="keywordtype">size_t</span> width = 0;</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  <span class="keywordflow">for</span>(<span class="keyword">const</span> <span class="keyword">auto</span> &tensor : inputs_vector)</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  {</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  <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="l00739"></a><span class="lineno"> 739</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="l00740"></a><span class="lineno"> 740</span>  width += shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#aa87f8fc26981b0f3228a78c83b95b802">x</a>();</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  }</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>  out_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, width);</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span> </div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  <span class="keywordflow">return</span> out_shape;</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span> }</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span> } <span class="comment">// namespace shape_calculator</span></div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span> } <span class="comment">// namespace misc</span></div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span> } <span class="comment">// namespace arm_compute</span></div><div class="line"><a name="l00750"></a><span class="lineno"> 750</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_arithmetic_division_8cpp_source.xhtml#l00096">ArithmeticDivision.cpp:96</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="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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00055">ShapeCalculator.h:55</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00404">ShapeCalculator.h:404</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_winograd_info_xhtml_a0bc60aaf23b2d3ed3b344576a708e84e"><div class="ttname"><a href="structarm__compute_1_1_winograd_info.xhtml#a0bc60aaf23b2d3ed3b344576a708e84e">arm_compute::WinogradInfo::output_data_layout</a></div><div class="ttdeci">DataLayout output_data_layout</div><div class="ttdoc">Data layout to use for the output tensor once the convolution has been applied (NCHW or NHWC) ...</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01731">Types.h:1731</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00144">ShapeCalculator.h:144</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_aac40b7097f2bda9274ae07fa33d15a79"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#aac40b7097f2bda9274ae07fa33d15a79">arm_compute::test::validation::a</a></div><div class="ttdeci">gemm configure & a</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00102">GEMM.cpp:102</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#l00480">Types.h:480</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_ad98c5b23d8630ce7b15c961992fd3dac"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ad98c5b23d8630ce7b15c961992fd3dac">arm_compute::misc::shape_calculator::calculate_width_concatenate_shape</a></div><div class="ttdeci">TensorShape calculate_width_concatenate_shape(const std::vector< T *> &inputs_vector)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00731">ShapeCalculator.h:731</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00460">ShapeCalculator.h:460</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#l01712">Types.h:1712</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#l01484">Types.h:1484</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_winograd_info_xhtml_ad99e40b120f0a9e96821c08bf60a84f2"><div class="ttname"><a href="structarm__compute_1_1_winograd_info.xhtml#ad99e40b120f0a9e96821c08bf60a84f2">arm_compute::WinogradInfo::convolution_info</a></div><div class="ttdeci">PadStrideInfo convolution_info</div><div class="ttdoc">Convolution info (Pads, strides,...) </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01730">Types.h:1730</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_prior_box_layer_info_xhtml_a6a8ad2364ce45d0cb7c6938544711f7c"><div class="ttname"><a href="classarm__compute_1_1_prior_box_layer_info.xhtml#a6a8ad2364ce45d0cb7c6938544711f7c">arm_compute::PriorBoxLayerInfo::aspect_ratios</a></div><div class="ttdeci">std::vector< float > aspect_ratios() const</div><div class="ttdoc">Get aspect ratios. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00939">Types.h:939</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00529">ShapeCalculator.h:529</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00158">ShapeCalculator.h:158</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00326">ShapeCalculator.h:326</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_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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00634">ShapeCalculator.h:634</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#l00251">Winograd.cpp:251</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_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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00521">ShapeCalculator.h:521</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a786677cbfb3f5677b4d84f3056eb08db"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a786677cbfb3f5677b4d84f3056eb08db">arm_compute::test::validation::data_layout</a></div><div class="ttdeci">data_layout</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00251">Winograd.cpp:251</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&#39;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_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_1_1misc_1_1shape__calculator_xhtml_ac18940223de1db7f6ed6c49119be7cd8"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ac18940223de1db7f6ed6c49119be7cd8">arm_compute::misc::shape_calculator::compute_interleave_custom_shape</a></div><div class="ttdeci">TensorShape compute_interleave_custom_shape(const TensorShape &input, const int x_interleave, const int y_interleave)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00358">ShapeCalculator.h:358</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#l00701">Helpers.h:701</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00062">ShapeCalculator.h:62</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="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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00104">ShapeCalculator.h:104</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00557">ShapeCalculator.h:557</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a096668313a9a819d54a2e65ec21ff0cc"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">arm_compute::test::validation::info</a></div><div class="ttdeci">src info() -> set_format(Format::S16)</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00486">ShapeCalculator.h:486</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00576">ShapeCalculator.h:576</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a5c2e95d65407a26489ea38431ad851f5"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a5c2e95d65407a26489ea38431ad851f5">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, unsigned int inner_border_right, unsigned int inner_border_top, std::pair< unsigned int, unsigned int > &out_dims, unsigned int &padx, unsigned int &pady)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00240">ShapeCalculator.h:240</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00206">ShapeCalculator.h:206</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#l00536">Helpers.h:536</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00265">ShapeCalculator.h:265</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00496">ShapeCalculator.h:496</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#l01521">Types.h:1521</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_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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00315">ShapeCalculator.h:315</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00650">ShapeCalculator.h:650</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00041">ShapeCalculator.h:41</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="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_winograd_info_xhtml_a5cd6561e9acc0cf9ba11bc2f51ec7a33"><div class="ttname"><a href="structarm__compute_1_1_winograd_info.xhtml#a5cd6561e9acc0cf9ba11bc2f51ec7a33">arm_compute::WinogradInfo::output_tile_size</a></div><div class="ttdeci">Size2D output_tile_size</div><div class="ttdoc">Width and height of the output tile. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01727">Types.h:1727</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_ab51afcfdb9caea9e8185ae6a42ba4779"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ab51afcfdb9caea9e8185ae6a42ba4779">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)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00216">ShapeCalculator.h:216</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_abb29a685080e999c2a0cb874d2f7bb5a"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#abb29a685080e999c2a0cb874d2f7bb5a">arm_compute::Dimensions::z</a></div><div class="ttdeci">T z() const</div><div class="ttdoc">Alias to access the size of the third dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00091">Dimensions.h:91</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1helpers_1_1tensor__transform_xhtml_aa5cd68178f4a5d7e9f3be67c19178fbe"><div class="ttname"><a href="namespacearm__compute_1_1helpers_1_1tensor__transform.xhtml#aa5cd68178f4a5d7e9f3be67c19178fbe">arm_compute::helpers::tensor_transform::strided_slice_absolute_start_coords</a></div><div class="ttdeci">Coordinates strided_slice_absolute_start_coords(TensorShape input_shape, Coordinates starts, Coordinates strides, int32_t begin_mask=0)</div><div class="ttdoc">Returns the absolute start coordinates of strided slice. </div><div class="ttdef"><b>Definition:</b> <a href="tensor__transform_8cpp_source.xhtml#l00056">tensor_transform.cpp:56</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_g_e_m_m_8cpp_source.xhtml#l00087">GEMM.cpp:87</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_coordinates_xhtml"><div class="ttname"><a href="classarm__compute_1_1_coordinates.xhtml">arm_compute::Coordinates</a></div><div class="ttdoc">Coordinates of an item. </div><div class="ttdef"><b>Definition:</b> <a href="_coordinates_8h_source.xhtml#l00037">Coordinates.h:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1misc_1_1_i_cloneable_xhtml_a4d10e5012a872e7f78f2b539b673049d"><div class="ttname"><a href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">arm_compute::misc::ICloneable::clone</a></div><div class="ttdeci">virtual std::unique_ptr< T > clone() const =0</div><div class="ttdoc">Provide a clone of the current object of class T. </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#l00834">Types.h:834</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#l00685">Types.h:685</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_aa459796b5489eca8a9160cb5dcf1a103"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#aa459796b5489eca8a9160cb5dcf1a103">arm_compute::ITensorInfo::element_size</a></div><div class="ttdeci">virtual size_t element_size() const =0</div><div class="ttdoc">Element size in bytes calculated as data_size() * num_channels() </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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00388">ShapeCalculator.h:388</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00664">ShapeCalculator.h:664</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1helpers_1_1tensor__transform_xhtml_a88bcbb798479cb74a34e0c4b1de181c8"><div class="ttname"><a href="namespacearm__compute_1_1helpers_1_1tensor__transform.xhtml#a88bcbb798479cb74a34e0c4b1de181c8">arm_compute::helpers::tensor_transform::strided_slice_absolute_end_coords</a></div><div class="ttdeci">Coordinates strided_slice_absolute_end_coords(TensorShape input_shape, Coordinates starts_abs, Coordinates ends, Coordinates strides, int32_t end_mask=0, int32_t shrink_axis_mask=0)</div><div class="ttdoc">Returns the absolute ends coordinates of strided slice. </div><div class="ttdef"><b>Definition:</b> <a href="tensor__transform_8cpp_source.xhtml#l00091">tensor_transform.cpp:91</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acbf8f8a6dd185de04c1981c57a8963cf"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acbf8f8a6dd185de04c1981c57a8963cf">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#l00694">Winograd.cpp:694</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#l01018">Types.h:1018</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_a734391819bbd2b0fa8400c06b7956d9e"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a734391819bbd2b0fa8400c06b7956d9e">arm_compute::misc::shape_calculator::compute_fully_connected_reshaped_weights_shape</a></div><div class="ttdeci">TensorShape compute_fully_connected_reshaped_weights_shape(const ITensorInfo *input, bool transpose_weights, bool is_batched_fc_layer, const int interleave)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00368">ShapeCalculator.h:368</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00169">ShapeCalculator.h:169</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#l01013">Types.h:1013</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00690">ShapeCalculator.h:690</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00674">ShapeCalculator.h:674</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00433">ShapeCalculator.h:433</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="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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00081">ShapeCalculator.h:81</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#l01513">Types.h:1513</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="classarm__compute_1_1_prior_box_layer_info_xhtml_a985940fefc02bdbc0ea1c916bb0ac82b"><div class="ttname"><a href="classarm__compute_1_1_prior_box_layer_info.xhtml#a985940fefc02bdbc0ea1c916bb0ac82b">arm_compute::PriorBoxLayerInfo::max_sizes</a></div><div class="ttdeci">std::vector< float > max_sizes() const</div><div class="ttdoc">Get max sizes. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00934">Types.h:934</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00592">ShapeCalculator.h:592</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_a90d6bd879f5e05b591dc58892348c65c"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#a90d6bd879f5e05b591dc58892348c65c">arm_compute::misc::shape_calculator::calculate_depth_concatenate_shape</a></div><div class="ttdeci">TensorShape calculate_depth_concatenate_shape(const std::vector< T *> &inputs_vector)</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00706">ShapeCalculator.h:706</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00134">ShapeCalculator.h:134</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#l01556">Types.h:1556</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_winograd_info_xhtml_aca57076ead1d06c47d3d32f4302b14ac"><div class="ttname"><a href="structarm__compute_1_1_winograd_info.xhtml#aca57076ead1d06c47d3d32f4302b14ac">arm_compute::WinogradInfo::kernel_size</a></div><div class="ttdeci">Size2D kernel_size</div><div class="ttdoc">Width and height of the kernel. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01728">Types.h:1728</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="tensor__transform_8h_xhtml"><div class="ttname"><a href="tensor__transform_8h.xhtml">tensor_transform.h</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_1helpers_1_1tensor__transform_xhtml_a125684caafdab8cdee40e424166cf61f"><div class="ttname"><a href="namespacearm__compute_1_1helpers_1_1tensor__transform.xhtml#a125684caafdab8cdee40e424166cf61f">arm_compute::helpers::tensor_transform::strided_slice_strides</a></div><div class="ttdeci">Coordinates strided_slice_strides(TensorShape input_shape, Coordinates strides)</div><div class="ttdoc">Returns the final strides of strided slice. </div><div class="ttdef"><b>Definition:</b> <a href="tensor__transform_8cpp_source.xhtml#l00133">tensor_transform.cpp:133</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1helpers_1_1tensor__transform_xhtml_af13ccc2d4ed13a513cf7d031233cbfdd"><div class="ttname"><a href="namespacearm__compute_1_1helpers_1_1tensor__transform.xhtml#af13ccc2d4ed13a513cf7d031233cbfdd">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_abs, Coordinates ends_abs, Coordinates final_strides)</div><div class="ttdoc">Computes output shape of strided slice. </div><div class="ttdef"><b>Definition:</b> <a href="tensor__transform_8cpp_source.xhtml#l00142">tensor_transform.cpp:142</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00181">ShapeCalculator.h:181</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_a7b8004eef325a40dd43eb80755610fff"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a7b8004eef325a40dd43eb80755610fff">arm_compute::test::validation::b</a></div><div class="ttdeci">CLTensor b</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00091">GEMM.cpp:91</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_winograd_info_xhtml_af9ef316b2c98c946b47cd18f1319b93f"><div class="ttname"><a href="structarm__compute_1_1_winograd_info.xhtml#af9ef316b2c98c946b47cd18f1319b93f">arm_compute::WinogradInfo::input_dimensions</a></div><div class="ttdeci">Size2D input_dimensions</div><div class="ttdoc">Width and height of the input tensor before the convolution is applied. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01729">Types.h:1729</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#l01564">Types.h:1564</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_arithmetic_division_8cpp_source.xhtml#l00111">ArithmeticDivision.cpp:111</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00609">ShapeCalculator.h:609</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#l00957">Types.h:957</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#l00111">Types.h:111</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="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00283">ShapeCalculator.h:283</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="namespacearm__compute_xhtml_a546c6bed3c307414e8d0934bc13259e5"><div class="ttname"><a href="namespacearm__compute.xhtml#a546c6bed3c307414e8d0934bc13259e5">arm_compute::scaled_dimensions</a></div><div class="ttdeci">const 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#l00352">Utils.cpp:352</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#l01028">Types.h:1028</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="classarm__compute_1_1_prior_box_layer_info_xhtml_a147b1e13f347e7b995de58f8fafd6723"><div class="ttname"><a href="classarm__compute_1_1_prior_box_layer_info.xhtml#a147b1e13f347e7b995de58f8fafd6723">arm_compute::PriorBoxLayerInfo::min_sizes</a></div><div class="ttdeci">std::vector< float > min_sizes() const</div><div class="ttdoc">Get min sizes. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00899">Types.h:899</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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