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| <div class="title">CPPDetectionOutputLayer.cpp</div> </div> |
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| <a href="_c_p_p_detection_output_layer_8cpp.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) 2018-2019 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "<a class="code" href="_c_p_p_detection_output_layer_8h.xhtml">arm_compute/runtime/CPP/functions/CPPDetectionOutputLayer.h</a>"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include "<a class="code" href="_error_8h.xhtml">arm_compute/core/Error.h</a>"</span></div><div class="line"><a name="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="_validate_8h.xhtml">arm_compute/core/Validate.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="_toolchain_support_8h.xhtml">support/ToolchainSupport.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 <list></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="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></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></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> Status validate_arguments(<span class="keyword">const</span> ITensorInfo *input_loc, <span class="keyword">const</span> ITensorInfo *input_conf, <span class="keyword">const</span> ITensorInfo *input_priorbox, <span class="keyword">const</span> ITensorInfo *output, DetectionOutputLayerInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>)</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> {</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(input_loc, input_conf, input_priorbox, output);</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(input_loc, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input_loc, input_conf, input_priorbox);</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <a class="code" href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(input_loc->num_dimensions() > 2, <span class="stringliteral">"The location input tensor should be [C1, N]."</span>);</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <a class="code" href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(input_conf->num_dimensions() > 2, <span class="stringliteral">"The location input tensor should be [C2, N]."</span>);</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <a class="code" href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(input_priorbox->num_dimensions() > 3, <span class="stringliteral">"The priorbox input tensor should be [C3, 2, N]."</span>);</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <a class="code" href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.eta() <= 0.f && <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.eta() > 1.f, <span class="stringliteral">"Eta should be between 0 and 1"</span>);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> num_priors = input_priorbox->tensor_shape()[0] / 4;</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <a class="code" href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(static_cast<size_t>((num_priors * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.num_loc_classes() * 4)) != input_loc->tensor_shape()[0], <span class="stringliteral">"Number of priors must match number of location predictions."</span>);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <a class="code" href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(static_cast<size_t>((num_priors * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.num_classes())) != input_conf->tensor_shape()[0], <span class="stringliteral">"Number of priors must match number of confidence predictions."</span>);</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="comment">// Validate configured output</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordflow">if</span>(output->total_size() != 0)</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  {</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> max_size = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.keep_top_k() * (input_loc->num_dimensions() > 1 ? input_loc->dimension(1) : 1);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <a class="code" href="_validate_8h.xhtml#a1da797d2762c1cdbb73bfc83136c3a38">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS</a>(output->tensor_shape(), TensorShape(7U, max_size));</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input_loc, output);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  }</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">return</span> Status{};</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> }</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> </div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> <span class="keywordtype">bool</span> SortScorePairDescend(<span class="keyword">const</span> std::pair<float, T> &pair1,</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keyword">const</span> std::pair<float, T> &pair2)</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> {</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keywordflow">return</span> pair1.first > pair2.first;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> }</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> </div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> <span class="keywordtype">void</span> retrieve_all_loc_predictions(<span class="keyword">const</span> ITensor *input_loc, <span class="keyword">const</span> <span class="keywordtype">int</span> num,</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> num_priors, <span class="keyword">const</span> <span class="keywordtype">int</span> num_loc_classes,</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> share_location, std::vector<LabelBBox> &all_location_predictions)</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> {</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < num; ++i)</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  {</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> c = 0; c < num_loc_classes; ++c)</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  {</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keywordtype">int</span> label = share_location ? -1 : c;</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordflow">if</span>(all_location_predictions[i].find(label) == all_location_predictions[i].end())</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  {</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  all_location_predictions[i][label].resize(num_priors);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  }</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  {</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(all_location_predictions[i][label].size() != static_cast<size_t>(num_priors));</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keywordflow">break</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>  }</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>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < num; ++i)</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  {</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> p = 0; p < num_priors; ++p)</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  {</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> c = 0; c < num_loc_classes; ++c)</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  {</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> label = share_location ? -1 : c;</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> base_ptr = i * num_priors * num_loc_classes * 4 + p * num_loc_classes * 4 + c * 4;</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="comment">//xmin, ymin, xmax, ymax</span></div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  all_location_predictions[i][label][p][0] = *reinterpret_cast<float *>(input_loc->ptr_to_element(Coordinates(base_ptr)));</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  all_location_predictions[i][label][p][1] = *reinterpret_cast<float *>(input_loc->ptr_to_element(Coordinates(base_ptr + 1)));</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  all_location_predictions[i][label][p][2] = *reinterpret_cast<float *>(input_loc->ptr_to_element(Coordinates(base_ptr + 2)));</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  all_location_predictions[i][label][p][3] = *reinterpret_cast<float *>(input_loc->ptr_to_element(Coordinates(base_ptr + 3)));</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>  }</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  }</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> }</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> <span class="keywordtype">void</span> retrieve_all_conf_scores(<span class="keyword">const</span> ITensor *input_conf, <span class="keyword">const</span> <span class="keywordtype">int</span> num,</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> num_priors, <span class="keyword">const</span> <span class="keywordtype">int</span> num_classes,</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  std::vector<<a class="code" href="namespacearm__compute_1_1utils.xhtml#a9c3303817ba653b5d1e78efb88d02bcf">std::map</a><<span class="keywordtype">int</span>, std::vector<float>>> &all_confidence_scores)</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> {</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  std::vector<float> tmp_buffer;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  tmp_buffer.resize(num * num_priors * num_classes);</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < num; ++i)</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  {</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> c = 0; c < num_classes; ++c)</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">for</span>(<span class="keywordtype">int</span> p = 0; p < num_priors; ++p)</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>  tmp_buffer[i * num_classes * num_priors + c * num_priors + p] =</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  *reinterpret_cast<float *>(input_conf->ptr_to_element(Coordinates(i * num_classes * num_priors + p * num_classes + c)));</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>  }</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  }</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < num; ++i)</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  {</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> c = 0; c < num_classes; ++c)</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  {</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  all_confidence_scores[i][c].resize(num_priors);</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  all_confidence_scores[i][c].assign(&tmp_buffer[i * num_classes * num_priors + c * num_priors],</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  &tmp_buffer[i * num_classes * num_priors + c * num_priors + num_priors]);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  }</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"> 158</span> </div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> <span class="keywordtype">void</span> retrieve_all_priorbox(<span class="keyword">const</span> ITensor *input_priorbox,</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> num_priors,</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  std::vector<BBox> &all_prior_bboxes,</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  std::vector<std::array<float, 4>> &all_prior_variances)</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> {</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < num_priors; ++i)</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  {</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  all_prior_bboxes[i] =</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>  {</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  *reinterpret_cast<float *>(input_priorbox->ptr_to_element(Coordinates(i * 4))),</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  *reinterpret_cast<float *>(input_priorbox->ptr_to_element(Coordinates(i * 4 + 1))),</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  *reinterpret_cast<float *>(input_priorbox->ptr_to_element(Coordinates(i * 4 + 2))),</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  *reinterpret_cast<float *>(input_priorbox->ptr_to_element(Coordinates(i * 4 + 3)))</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  }</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  };</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  }</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>  std::array<float, 4> var({ { 0, 0, 0, 0 } });</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < num_priors; ++i)</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  {</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> j = 0; j < 4; ++j)</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>  var[j] = *reinterpret_cast<float *>(input_priorbox->ptr_to_element(Coordinates((num_priors + i) * 4 + j)));</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  }</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  all_prior_variances[i] = var;</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  }</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> </div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> <span class="keywordtype">void</span> DecodeBBox(<span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#aa2b075b5da72ec6bb14f90c202443eb0">BBox</a> &prior_bbox, <span class="keyword">const</span> std::array<float, 4> &prior_variance,</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad818ba0ecd4a87d8f1bb0d5b17f07830">DetectionOutputLayerCodeType</a> code_type, <span class="keyword">const</span> <span class="keywordtype">bool</span> variance_encoded_in_target,</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> clip_bbox, <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#aa2b075b5da72ec6bb14f90c202443eb0">BBox</a> &bbox, <a class="code" href="namespacearm__compute.xhtml#aa2b075b5da72ec6bb14f90c202443eb0">BBox</a> &decode_bbox)</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="comment">// if the variance is encoded in target, we simply need to add the offset predictions</span></div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="comment">// otherwise we need to scale the offset accordingly.</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keywordflow">switch</span>(code_type)</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  {</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad818ba0ecd4a87d8f1bb0d5b17f07830ac411afd31d32cec664d372acc12f404a">DetectionOutputLayerCodeType::CORNER</a>:</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  {</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  decode_bbox[0] = prior_bbox[0] + (variance_encoded_in_target ? bbox[0] : prior_variance[0] * bbox[0]);</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  decode_bbox[1] = prior_bbox[1] + (variance_encoded_in_target ? bbox[1] : prior_variance[1] * bbox[1]);</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  decode_bbox[2] = prior_bbox[2] + (variance_encoded_in_target ? bbox[2] : prior_variance[2] * bbox[2]);</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  decode_bbox[3] = prior_bbox[3] + (variance_encoded_in_target ? bbox[3] : prior_variance[3] * bbox[3]);</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> </div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  }</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad818ba0ecd4a87d8f1bb0d5b17f07830a1150a8d7752b01d30d91fe18fe9d8a54">DetectionOutputLayerCodeType::CENTER_SIZE</a>:</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  {</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> prior_width = prior_bbox[2] - prior_bbox[0];</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> prior_height = prior_bbox[3] - prior_bbox[1];</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> </div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="comment">// Check if the prior width and height are right</span></div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(prior_width <= 0.f);</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(prior_height <= 0.f);</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span> </div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> prior_center_x = (prior_bbox[0] + prior_bbox[2]) / 2.;</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> prior_center_y = (prior_bbox[1] + prior_bbox[3]) / 2.;</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> </div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> decode_bbox_center_x = (variance_encoded_in_target ? bbox[0] : prior_variance[0] * bbox[0]) * prior_width + prior_center_x;</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> decode_bbox_center_y = (variance_encoded_in_target ? bbox[1] : prior_variance[1] * bbox[1]) * prior_height + prior_center_y;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> decode_bbox_width = (variance_encoded_in_target ? std::exp(bbox[2]) : std::exp(prior_variance[2] * bbox[2])) * prior_width;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> decode_bbox_height = (variance_encoded_in_target ? std::exp(bbox[3]) : std::exp(prior_variance[3] * bbox[3])) * prior_height;</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span> </div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  decode_bbox[0] = (decode_bbox_center_x - decode_bbox_width / 2.f);</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  decode_bbox[1] = (decode_bbox_center_y - decode_bbox_height / 2.f);</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  decode_bbox[2] = (decode_bbox_center_x + decode_bbox_width / 2.f);</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  decode_bbox[3] = (decode_bbox_center_y + decode_bbox_height / 2.f);</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="keywordflow">break</span>;</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  }</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad818ba0ecd4a87d8f1bb0d5b17f07830afbc6c35854fe02eb9e792f897399c42a">DetectionOutputLayerCodeType::CORNER_SIZE</a>:</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="keyword">const</span> <span class="keywordtype">float</span> prior_width = prior_bbox[2] - prior_bbox[0];</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> prior_height = prior_bbox[3] - prior_bbox[1];</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span> </div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="comment">// Check if the prior width and height are greater than 0</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(prior_width <= 0.f);</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(prior_height <= 0.f);</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> </div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  decode_bbox[0] = prior_bbox[0] + (variance_encoded_in_target ? bbox[0] : prior_variance[0] * bbox[0]) * prior_width;</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  decode_bbox[1] = prior_bbox[1] + (variance_encoded_in_target ? bbox[1] : prior_variance[1] * bbox[1]) * prior_height;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  decode_bbox[2] = prior_bbox[2] + (variance_encoded_in_target ? bbox[2] : prior_variance[2] * bbox[2]) * prior_width;</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  decode_bbox[3] = prior_bbox[3] + (variance_encoded_in_target ? bbox[3] : prior_variance[3] * bbox[3]) * prior_height;</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>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  }</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Unsupported Detection Output Code Type."</span>);</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  }</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="keywordflow">if</span>(clip_bbox)</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  {</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &d_bbox : decode_bbox)</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  {</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  d_bbox = <a class="code" href="namespacearm__compute_1_1utility.xhtml#a96efecf997f13a914609ddf1eb67f624">utility::clamp</a>(d_bbox, 0.f, 1.f);</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>  }</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span> }</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> </div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> <span class="keywordtype">void</span> ApplyNMSFast(<span class="keyword">const</span> std::vector<BBox> &bboxes,</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="keyword">const</span> std::vector<float> &scores, <span class="keyword">const</span> <span class="keywordtype">float</span> score_threshold,</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> nms_threshold, <span class="keyword">const</span> <span class="keywordtype">float</span> eta, <span class="keyword">const</span> <span class="keywordtype">int</span> top_k,</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  std::vector<int> &indices)</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>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(bboxes.size() != scores.size(), <span class="stringliteral">"bboxes and scores have different size."</span>);</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span> </div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="comment">// Get top_k scores (with corresponding indices).</span></div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  std::list<std::pair<float, int>> score_index_vec;</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>  <span class="comment">// Generate index score pairs.</span></div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i = 0; i < scores.size(); ++i)</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  {</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keywordflow">if</span>(scores[i] > score_threshold)</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>  score_index_vec.emplace_back(std::make_pair(scores[i], i));</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>  }</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> </div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="comment">// Sort the score pair according to the scores in descending order</span></div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  score_index_vec.sort(SortScorePairDescend<int>);</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="comment">// Keep top_k scores if needed.</span></div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> score_index_vec_size = score_index_vec.size();</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="keywordflow">if</span>(top_k > -1 && top_k < score_index_vec_size)</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  {</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  score_index_vec.resize(top_k);</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</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>  <span class="comment">// Do nms.</span></div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="keywordtype">float</span> adaptive_threshold = nms_threshold;</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  indices.clear();</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">while</span>(!score_index_vec.empty())</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>  <span class="keyword">const</span> <span class="keywordtype">int</span> idx = score_index_vec.front().second;</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <span class="keywordtype">bool</span> keep = <span class="keyword">true</span>;</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> kept_idx : indices)</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  {</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <span class="keywordflow">if</span>(keep)</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  {</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <span class="comment">// Compute the jaccard (intersection over union IoU) overlap between two bboxes.</span></div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <a class="code" href="namespacearm__compute.xhtml#aa2b075b5da72ec6bb14f90c202443eb0">BBox</a> intersect_bbox = std::array<float, 4>({ 0, 0, 0, 0 });</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <span class="keywordflow">if</span>(bboxes[kept_idx][0] > bboxes[idx][2] || bboxes[kept_idx][2] < bboxes[idx][0] || bboxes[kept_idx][1] > bboxes[idx][3] || bboxes[kept_idx][3] < bboxes[idx][1])</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  {</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  intersect_bbox = std::array<float, 4>({ { 0, 0, 0, 0 } });</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  }</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="keywordflow">else</span></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>  intersect_bbox = std::array<float, 4>({ {</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  std::max(bboxes[idx][0], bboxes[kept_idx][0]),</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  std::max(bboxes[idx][1], bboxes[kept_idx][1]),</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  std::min(bboxes[idx][2], bboxes[kept_idx][2]),</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  std::min(bboxes[idx][3], bboxes[kept_idx][3])</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  }</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  });</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  }</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span> </div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="keywordtype">float</span> intersect_width = intersect_bbox[2] - intersect_bbox[0];</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="keywordtype">float</span> intersect_height = intersect_bbox[3] - intersect_bbox[1];</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span> </div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="keywordtype">float</span> overlap = 0.f;</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="keywordflow">if</span>(intersect_width > 0 && intersect_height > 0)</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>  <span class="keywordtype">float</span> intersect_size = intersect_width * intersect_height;</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keywordtype">float</span> bbox1_size = (bboxes[idx][2] < bboxes[idx][0]</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  || bboxes[idx][3] < bboxes[idx][1]) ?</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  0.f :</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  (bboxes[idx][2] - bboxes[idx][0]) * (bboxes[idx][3] - bboxes[idx][1]); <span class="comment">//BBoxSize(bboxes[idx]);</span></div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="keywordtype">float</span> bbox2_size = (bboxes[kept_idx][2] < bboxes[kept_idx][0]</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  || bboxes[kept_idx][3] < bboxes[kept_idx][1]) ?</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  0.f :</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  (bboxes[kept_idx][2] - bboxes[kept_idx][0]) * (bboxes[kept_idx][3] - bboxes[kept_idx][1]); <span class="comment">// BBoxSize(bboxes[kept_idx]);</span></div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  overlap = intersect_size / (bbox1_size + bbox2_size - intersect_size);</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>  keep = (overlap <= adaptive_threshold);</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  }</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  {</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  }</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="keywordflow">if</span>(keep)</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  {</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  indices.push_back(idx);</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  }</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  score_index_vec.erase(score_index_vec.begin());</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <span class="keywordflow">if</span>(keep && eta < 1.f && adaptive_threshold > 0.5f)</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  {</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  adaptive_threshold *= eta;</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>  }</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span> }</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> } <span class="comment">// namespace</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"><a class="line" href="classarm__compute_1_1_c_p_p_detection_output_layer.xhtml#a0c04f240b8b260665440c161d9a7fae9"> 385</a></span> <a class="code" href="classarm__compute_1_1_c_p_p_detection_output_layer.xhtml#a0c04f240b8b260665440c161d9a7fae9">CPPDetectionOutputLayer::CPPDetectionOutputLayer</a>()</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  : _input_loc(nullptr), _input_conf(nullptr), _input_priorbox(nullptr), _output(nullptr), _info(), _num_priors(), _num(), _all_location_predictions(), _all_confidence_scores(), _all_prior_bboxes(),</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  _all_prior_variances(), _all_decode_bboxes(), _all_indices()</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> {</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> </div><div class="line"><a name="l00391"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_p_p_detection_output_layer.xhtml#a4c87b215abac33e28e279fd7277e2126"> 391</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_p_p_detection_output_layer.xhtml#a4c87b215abac33e28e279fd7277e2126">CPPDetectionOutputLayer::configure</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *input_loc, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *input_conf, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *input_priorbox, <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output, <a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml">DetectionOutputLayerInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>)</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span> {</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <a class="code" href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a>(input_loc, input_conf, input_priorbox, output);</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="comment">// Output auto initialization if not yet initialized</span></div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="comment">// Since the number of bboxes to kept is unknown before nms, the shape is set to the maximum</span></div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <span class="comment">// The maximum is keep_top_k * input_loc_size[1]</span></div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="comment">// Each row is a 7 dimension std::vector, which stores [image_id, label, confidence, xmin, ymin, xmax, ymax]</span></div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> max_size = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.keep_top_k() * (input_loc-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1 ? input_loc-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) : 1);</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), input_loc-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_tensor_shape(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(7<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, max_size)));</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="comment">// Perform validation step</span></div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(validate_arguments(input_loc-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), input_conf-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), input_priorbox-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>));</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span> </div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  _input_loc = input_loc;</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  _input_conf = input_conf;</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  _input_priorbox = input_priorbox;</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  _output = output;</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  _info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>;</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  _num_priors = input_priorbox-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) / 4;</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  _num = (_input_loc-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 1 ? _input_loc-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) : 1);</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span> </div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  _all_location_predictions.resize(_num);</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  _all_confidence_scores.resize(_num);</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  _all_prior_bboxes.resize(_num_priors);</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  _all_prior_variances.resize(_num_priors);</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  _all_decode_bboxes.resize(_num);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span> </div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < _num; ++i)</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  {</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> c = 0; c < _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#ae82a2ccc5739cb255a9a7679d6161399">num_loc_classes</a>(); ++c)</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  {</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> label = _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#a7bc581f245390f063f02c3fcbb422320">share_location</a>() ? -1 : c;</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="keywordflow">if</span>(label == _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#a158d49c7c1df3c6c6589b47d3de56cf0">background_label_id</a>())</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  {</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="comment">// Ignore background class.</span></div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keywordflow">continue</span>;</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  }</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  _all_decode_bboxes[i][label].resize(_num_priors);</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>  }</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  _all_indices.resize(_num);</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span> </div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> coord;</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  coord.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a810a78f0b7cc0270f38d4136e023ea3b">set_num_dimensions</a>(output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>());</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a9586081a29fceb532ab270bd843abee6">set_valid_region</a>(<a class="code" href="structarm__compute_1_1_valid_region.xhtml">ValidRegion</a>(coord, output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>()));</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span> }</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span> </div><div class="line"><a name="l00438"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_p_p_detection_output_layer.xhtml#af1d5e758d546e837b9cabb5991d387e0"> 438</a></span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a> <a class="code" href="classarm__compute_1_1_c_p_p_detection_output_layer.xhtml#af1d5e758d546e837b9cabb5991d387e0">CPPDetectionOutputLayer::validate</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_loc, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_conf, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_priorbox, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output, <a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml">DetectionOutputLayerInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</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>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(validate_arguments(input_loc, input_conf, input_priorbox, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>));</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span> }</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span> </div><div class="line"><a name="l00444"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_p_p_detection_output_layer.xhtml#ad1717410afd0be936c6213a63c8005fb"> 444</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_p_p_detection_output_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">CPPDetectionOutputLayer::run</a>()</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>  <span class="comment">// Retrieve all location predictions.</span></div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  retrieve_all_loc_predictions(_input_loc, _num, _num_priors, _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#ae82a2ccc5739cb255a9a7679d6161399">num_loc_classes</a>(), _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#a7bc581f245390f063f02c3fcbb422320">share_location</a>(), _all_location_predictions);</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span> </div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="comment">// Retrieve all confidences.</span></div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  retrieve_all_conf_scores(_input_conf, _num, _num_priors, _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#a2411dd5edb9ccb581d303f3396e9c14c">num_classes</a>(), _all_confidence_scores);</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span> </div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="comment">// Retrieve all prior bboxes.</span></div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  retrieve_all_priorbox(_input_priorbox, _num_priors, _all_prior_bboxes, _all_prior_variances);</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span> </div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="comment">// Decode all loc predictions to bboxes</span></div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> clip_bbox = <span class="keyword">false</span>;</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < _num; ++i)</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>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> c = 0; c < _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#ae82a2ccc5739cb255a9a7679d6161399">num_loc_classes</a>(); ++c)</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  {</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> label = _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#a7bc581f245390f063f02c3fcbb422320">share_location</a>() ? -1 : c;</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keywordflow">if</span>(label == _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#a158d49c7c1df3c6c6589b47d3de56cf0">background_label_id</a>())</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  {</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="comment">// Ignore background class.</span></div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <span class="keywordflow">continue</span>;</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  }</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(_all_location_predictions[i].find(label) == _all_location_predictions[i].end(), <span class="stringliteral">"Could not find location predictions for label %d."</span>, label);</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> std::vector<BBox> &label_loc_preds = _all_location_predictions[i].find(label)->second;</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span> </div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> num_bboxes = _all_prior_bboxes.size();</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(_all_prior_variances[i].size() != 4);</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span> </div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> j = 0; j < num_bboxes; ++j)</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  {</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  DecodeBBox(_all_prior_bboxes[j], _all_prior_variances[j], _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#a025a49ad16e9d5d59d3919c25a17d1ae">code_type</a>(), _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#aa5081eb6d3f7bf20f32573af8a60f1f9">variance_encoded_in_target</a>(), clip_bbox, label_loc_preds[j], _all_decode_bboxes[i][label][j]);</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>  }</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  }</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> </div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  <span class="keywordtype">int</span> num_kept = 0;</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">for</span>(<span class="keywordtype">int</span> i = 0; i < _num; ++i)</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  {</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ae6550ea34c33d2e943476386d6ba8bed">LabelBBox</a> &decode_bboxes = _all_decode_bboxes[i];</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <span class="keyword">const</span> std::map<int, std::vector<float>> &conf_scores = _all_confidence_scores[i];</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>  std::map<int, std::vector<int>> indices;</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="keywordtype">int</span> num_det = 0;</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> c = 0; c < _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#a2411dd5edb9ccb581d303f3396e9c14c">num_classes</a>(); ++c)</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  {</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  <span class="keywordflow">if</span>(c == _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#a158d49c7c1df3c6c6589b47d3de56cf0">background_label_id</a>())</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  {</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <span class="comment">// Ignore background class</span></div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <span class="keywordflow">continue</span>;</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  }</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> label = _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#a7bc581f245390f063f02c3fcbb422320">share_location</a>() ? -1 : c;</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <span class="keywordflow">if</span>(conf_scores.find(c) == conf_scores.end() || decode_bboxes.find(label) == decode_bboxes.end())</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  {</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Could not find predictions for label %d."</span>, label);</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  }</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  <span class="keyword">const</span> std::vector<float> &scores = conf_scores.find(c)->second;</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <span class="keyword">const</span> std::vector<BBox> &bboxes = decode_bboxes.find(label)->second;</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span> </div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  ApplyNMSFast(bboxes, scores, _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#a47c941c24980e6f61a74986c4a16c16c">confidence_threshold</a>(), _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#af14fc4cf24dfb69a0f225a582ef01d54">nms_threshold</a>(), _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#a206472366fc0981d36701fe46679fd8f">eta</a>(), _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#acf35ae15a9350f47bcba0d0cedeb3e7c">top_k</a>(), indices[c]);</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span> </div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  num_det += indices[c].size();</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> </div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  <span class="keywordtype">int</span> num_to_add = 0;</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <span class="keywordflow">if</span>(_info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#a381583deeb7c92f3b86d959c1e6c8185">keep_top_k</a>() > -1 && num_det > _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#a381583deeb7c92f3b86d959c1e6c8185">keep_top_k</a>())</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  {</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  std::vector<std::pair<float, std::pair<int, int>>> score_index_pairs;</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> <span class="keyword">const</span> &it : indices)</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  {</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> label = it.first;</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <span class="keyword">const</span> std::vector<int> &label_indices = it.second;</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> </div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  <span class="keywordflow">if</span>(conf_scores.find(label) == conf_scores.end())</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  {</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Could not find predictions for label %d."</span>, label);</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> </div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  <span class="keyword">const</span> std::vector<float> &scores = conf_scores.find(label)->second;</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> idx : label_indices)</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  {</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(idx > static_cast<int>(scores.size()));</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  score_index_pairs.emplace_back(std::make_pair(scores[idx], std::make_pair(label, idx)));</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  }</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  }</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span> </div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <span class="comment">// Keep top k results per image.</span></div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  std::sort(score_index_pairs.begin(), score_index_pairs.end(), SortScorePairDescend<std::pair<int, int>>);</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  score_index_pairs.resize(_info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#a381583deeb7c92f3b86d959c1e6c8185">keep_top_k</a>());</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span> </div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  <span class="comment">// Store the new indices.</span></div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span> </div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  std::map<int, std::vector<int>> new_indices;</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> score_index_pair : score_index_pairs)</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  {</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  <span class="keywordtype">int</span> label = score_index_pair.second.first;</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keywordtype">int</span> idx = score_index_pair.second.second;</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  new_indices[label].push_back(idx);</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  }</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  _all_indices[i] = new_indices;</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  num_to_add = _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#a381583deeb7c92f3b86d959c1e6c8185">keep_top_k</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  {</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  _all_indices[i] = indices;</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  num_to_add = num_det;</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  }</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  num_kept += num_to_add;</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  }</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span> </div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <span class="comment">//Update the valid region of the ouput to mark the exact number of detection</span></div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  _output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a9586081a29fceb532ab270bd843abee6">set_valid_region</a>(<a class="code" href="structarm__compute_1_1_valid_region.xhtml">ValidRegion</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0), <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(7, num_kept)));</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span> </div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <span class="keywordtype">int</span> count = 0;</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < _num; ++i)</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  {</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  <span class="keyword">const</span> std::map<int, std::vector<float>> &conf_scores = _all_confidence_scores[i];</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ae6550ea34c33d2e943476386d6ba8bed">LabelBBox</a> &decode_bboxes = _all_decode_bboxes[i];</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &it : _all_indices[i])</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>  <span class="keyword">const</span> <span class="keywordtype">int</span> label = it.first;</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <span class="keyword">const</span> std::vector<float> &scores = conf_scores.find(label)->second;</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> loc_label = _info.<a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml#a7bc581f245390f063f02c3fcbb422320">share_location</a>() ? -1 : label;</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  <span class="keywordflow">if</span>(conf_scores.find(label) == conf_scores.end() || decode_bboxes.find(loc_label) == decode_bboxes.end())</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  {</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <span class="comment">// Either if there are no confidence predictions</span></div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="comment">// or there are no location predictions for current label.</span></div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Could not find predictions for the label %d."</span>, label);</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>  <span class="keyword">const</span> std::vector<BBox> &bboxes = decode_bboxes.find(loc_label)->second;</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <span class="keyword">const</span> std::vector<int> &indices = it.second;</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span> </div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> idx : indices)</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>  *(reinterpret_cast<float *>(_output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(count * 7)))) = i;</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  *(reinterpret_cast<float *>(_output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(count * 7 + 1)))) = label;</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  *(reinterpret_cast<float *>(_output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(count * 7 + 2)))) = scores[idx];</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  *(reinterpret_cast<float *>(_output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(count * 7 + 3)))) = bboxes[idx][0];</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  *(reinterpret_cast<float *>(_output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(count * 7 + 4)))) = bboxes[idx][1];</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  *(reinterpret_cast<float *>(_output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(count * 7 + 5)))) = bboxes[idx][2];</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  *(reinterpret_cast<float *>(_output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(count * 7 + 6)))) = bboxes[idx][3];</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span> </div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  ++count;</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  }</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  }</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  }</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span> }</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span> } <span class="comment">// namespace arm_compute</span></div><div class="ttc" id="_error_8h_xhtml_a05b19c75afe9c24200a62b9724734bbd"><div class="ttname"><a href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR(...)</div><div class="ttdoc">Print the given message then throw an std::runtime_error.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00261">Error.h:261</a></div></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="classarm__compute_1_1_detection_output_layer_info_xhtml_a2411dd5edb9ccb581d303f3396e9c14c"><div class="ttname"><a href="classarm__compute_1_1_detection_output_layer_info.xhtml#a2411dd5edb9ccb581d303f3396e9c14c">arm_compute::DetectionOutputLayerInfo::num_classes</a></div><div class="ttdeci">int num_classes() const</div><div class="ttdoc">Get num classes.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01011">Types.h:1011</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_adbd73147d41e8a640bc299d12613c31e"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">arm_compute::ITensor::ptr_to_element</a></div><div class="ttdeci">uint8_t * ptr_to_element(const Coordinates &id) const</div><div class="ttdoc">Return a pointer to the element at the passed coordinates.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8h_source.xhtml#l00063">ITensor.h:63</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_p_p_detection_output_layer_xhtml_a0c04f240b8b260665440c161d9a7fae9"><div class="ttname"><a href="classarm__compute_1_1_c_p_p_detection_output_layer.xhtml#a0c04f240b8b260665440c161d9a7fae9">arm_compute::CPPDetectionOutputLayer::CPPDetectionOutputLayer</a></div><div class="ttdeci">CPPDetectionOutputLayer()</div><div class="ttdoc">Default constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_detection_output_layer_8cpp_source.xhtml#l00385">CPPDetectionOutputLayer.cpp:385</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="_toolchain_support_8h_xhtml"><div class="ttname"><a href="_toolchain_support_8h.xhtml">ToolchainSupport.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_output_layer_info_xhtml_a7bc581f245390f063f02c3fcbb422320"><div class="ttname"><a href="classarm__compute_1_1_detection_output_layer_info.xhtml#a7bc581f245390f063f02c3fcbb422320">arm_compute::DetectionOutputLayerInfo::share_location</a></div><div class="ttdeci">bool share_location() const</div><div class="ttdoc">Get share location.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01016">Types.h:1016</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad818ba0ecd4a87d8f1bb0d5b17f07830a1150a8d7752b01d30d91fe18fe9d8a54"><div class="ttname"><a href="namespacearm__compute.xhtml#ad818ba0ecd4a87d8f1bb0d5b17f07830a1150a8d7752b01d30d91fe18fe9d8a54">arm_compute::DetectionOutputLayerCodeType::CENTER_SIZE</a></div><div class="ttdoc">Use box centers and size.</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="classarm__compute_1_1_c_p_p_detection_output_layer_xhtml_a4c87b215abac33e28e279fd7277e2126"><div class="ttname"><a href="classarm__compute_1_1_c_p_p_detection_output_layer.xhtml#a4c87b215abac33e28e279fd7277e2126">arm_compute::CPPDetectionOutputLayer::configure</a></div><div class="ttdeci">void configure(const ITensor *input_loc, const ITensor *input_conf, const ITensor *input_priorbox, ITensor *output, DetectionOutputLayerInfo info=DetectionOutputLayerInfo())</div><div class="ttdoc">Configure the detection output layer CPP kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_detection_output_layer_8cpp_source.xhtml#l00391">CPPDetectionOutputLayer.cpp:391</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ae6550ea34c33d2e943476386d6ba8bed"><div class="ttname"><a href="namespacearm__compute.xhtml#ae6550ea34c33d2e943476386d6ba8bed">arm_compute::LabelBBox</a></div><div class="ttdeci">std::map< int, std::vector< BBox > > LabelBBox</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00950">Types.h:950</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a8f3ff7da485ff7e75dab07baadf5b4bd"><div class="ttname"><a href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00545">Validate.h:545</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a8a1e1c105f0bdaf37db408c7cfcb77a4"><div class="ttname"><a href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ON_ERROR(status)</div><div class="ttdoc">Checks if a status contains an error and returns it.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00193">Error.h:193</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_ae7eed178dac535c6e727061b1f5bc6eb"><div class="ttname"><a href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00791">Validate.h:791</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel</div></div> |
| <div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00337">Error.h:337</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml">arm_compute::ITensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_info_8h_source.xhtml#l00040">ITensorInfo.h:40</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a938dcd406ce611ef5345ad2531cdb948"><div class="ttname"><a href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_THROW_ON(status)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00327">Error.h:327</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_output_layer_info_xhtml_a206472366fc0981d36701fe46679fd8f"><div class="ttname"><a href="classarm__compute_1_1_detection_output_layer_info.xhtml#a206472366fc0981d36701fe46679fd8f">arm_compute::DetectionOutputLayerInfo::eta</a></div><div class="ttdeci">float eta() const</div><div class="ttdoc">Get eta.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01041">Types.h:1041</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_status_xhtml"><div class="ttname"><a href="classarm__compute_1_1_status.xhtml">arm_compute::Status</a></div><div class="ttdoc">Status class.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00052">Error.h:52</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad818ba0ecd4a87d8f1bb0d5b17f07830"><div class="ttname"><a href="namespacearm__compute.xhtml#ad818ba0ecd4a87d8f1bb0d5b17f07830">arm_compute::DetectionOutputLayerCodeType</a></div><div class="ttdeci">DetectionOutputLayerCodeType</div><div class="ttdoc">Available Detection Output code types.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00953">Types.h:953</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml">arm_compute::ITensor</a></div><div class="ttdoc">Interface for NEON tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8h_source.xhtml#l00036">ITensor.h:36</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_p_p_detection_output_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_p_p_detection_output_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CPPDetectionOutputLayer::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_detection_output_layer_8cpp_source.xhtml#l00444">CPPDetectionOutputLayer.cpp:444</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_output_layer_info_xhtml_acf35ae15a9350f47bcba0d0cedeb3e7c"><div class="ttname"><a href="classarm__compute_1_1_detection_output_layer_info.xhtml#acf35ae15a9350f47bcba0d0cedeb3e7c">arm_compute::DetectionOutputLayerInfo::top_k</a></div><div class="ttdeci">int top_k() const</div><div class="ttdoc">Get top K.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01056">Types.h:1056</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2018 ARM Limited.</div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a9586081a29fceb532ab270bd843abee6"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a9586081a29fceb532ab270bd843abee6">arm_compute::ITensorInfo::set_valid_region</a></div><div class="ttdeci">virtual void set_valid_region(const ValidRegion &valid_region)=0</div><div class="ttdoc">Set the valid region of the tensor.</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a47be6fa38308d0003c25b60b7dbc45ce"><div class="ttname"><a href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">arm_compute::auto_init_if_empty</a></div><div class="ttdeci">bool auto_init_if_empty(ITensorInfo &info, const TensorShape &shape, int num_channels, DataType data_type, QuantizationInfo quantization_info=QuantizationInfo())</div><div class="ttdoc">Auto initialize the tensor info (shape, number of channels and data type) if the current assignment i...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00201">Helpers.inl:201</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1utility_xhtml_a96efecf997f13a914609ddf1eb67f624"><div class="ttname"><a href="namespacearm__compute_1_1utility.xhtml#a96efecf997f13a914609ddf1eb67f624">arm_compute::utility::clamp</a></div><div class="ttdeci">DataType clamp(const DataType &n, const DataType &lower=std::numeric_limits< RangeType >::lowest(), const DataType &upper=std::numeric_limits< RangeType >::max())</div><div class="ttdoc">Performs clamping among a lower and upper value.</div><div class="ttdef"><b>Definition:</b> <a href="_utility_8h_source.xhtml#l00084">Utility.h:84</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_output_layer_info_xhtml_aa5081eb6d3f7bf20f32573af8a60f1f9"><div class="ttname"><a href="classarm__compute_1_1_detection_output_layer_info.xhtml#aa5081eb6d3f7bf20f32573af8a60f1f9">arm_compute::DetectionOutputLayerInfo::variance_encoded_in_target</a></div><div class="ttdeci">bool variance_encoded_in_target() const</div><div class="ttdoc">Get if variance encoded in target.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01026">Types.h:1026</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_output_layer_info_xhtml_af14fc4cf24dfb69a0f225a582ef01d54"><div class="ttname"><a href="classarm__compute_1_1_detection_output_layer_info.xhtml#af14fc4cf24dfb69a0f225a582ef01d54">arm_compute::DetectionOutputLayerInfo::nms_threshold</a></div><div class="ttdeci">float nms_threshold() const</div><div class="ttdoc">Get nms threshold.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01036">Types.h:1036</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">arm_compute::Channel::U</a></div><div class="ttdoc">Cb/U channel.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_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="_c_p_p_detection_output_layer_8h_xhtml"><div class="ttname"><a href="_c_p_p_detection_output_layer_8h.xhtml">CPPDetectionOutputLayer.h</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a1da797d2762c1cdbb73bfc83136c3a38"><div class="ttname"><a href="_validate_8h.xhtml#a1da797d2762c1cdbb73bfc83136c3a38">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00288">Validate.h:288</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="_error_8h_xhtml_a86084036bd3851575ef871ad5bf079a7"><div class="ttname"><a href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond,...)</div><div class="ttdoc">If the condition is true, an error is returned.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00214">Error.h:214</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_aa2b075b5da72ec6bb14f90c202443eb0"><div class="ttname"><a href="namespacearm__compute.xhtml#aa2b075b5da72ec6bb14f90c202443eb0">arm_compute::BBox</a></div><div class="ttdeci">std::array< float, 4 > BBox</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00948">Types.h:948</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_i_tensor_xhtml_a0e95dc1e53c361348314873b168ae237"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">arm_compute::ITensor::info</a></div><div class="ttdeci">virtual ITensorInfo * info() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_output_layer_info_xhtml_a025a49ad16e9d5d59d3919c25a17d1ae"><div class="ttname"><a href="classarm__compute_1_1_detection_output_layer_info.xhtml#a025a49ad16e9d5d59d3919c25a17d1ae">arm_compute::DetectionOutputLayerInfo::code_type</a></div><div class="ttdeci">DetectionOutputLayerCodeType code_type() const</div><div class="ttdoc">Get detection output code type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01021">Types.h:1021</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml"><div class="ttname"><a href="_error_8h.xhtml">Error.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_output_layer_info_xhtml_a381583deeb7c92f3b86d959c1e6c8185"><div class="ttname"><a href="classarm__compute_1_1_detection_output_layer_info.xhtml#a381583deeb7c92f3b86d959c1e6c8185">arm_compute::DetectionOutputLayerInfo::keep_top_k</a></div><div class="ttdeci">int keep_top_k() const</div><div class="ttdoc">Get the number of total bounding boxes to be kept per image.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01031">Types.h:1031</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_aff911654521523937ff24372a870b89f"><div class="ttname"><a href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00163">Validate.h:163</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_output_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_detection_output_layer_info.xhtml">arm_compute::DetectionOutputLayerInfo</a></div><div class="ttdoc">Detection Output layer info.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00962">Types.h:962</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a921b705e9e3e0fe928928447869e62a5"><div class="ttname"><a href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00161">Validate.h:161</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad818ba0ecd4a87d8f1bb0d5b17f07830ac411afd31d32cec664d372acc12f404a"><div class="ttname"><a href="namespacearm__compute.xhtml#ad818ba0ecd4a87d8f1bb0d5b17f07830ac411afd31d32cec664d372acc12f404a">arm_compute::DetectionOutputLayerCodeType::CORNER</a></div><div class="ttdoc">Use box corners.</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1utils_xhtml_a9c3303817ba653b5d1e78efb88d02bcf"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml#a9c3303817ba653b5d1e78efb88d02bcf">arm_compute::utils::map</a></div><div class="ttdeci">void map(T &tensor, bool blocking)</div><div class="ttdoc">Maps a tensor if needed.</div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00206">Utils.h:206</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_p_p_detection_output_layer_xhtml_af1d5e758d546e837b9cabb5991d387e0"><div class="ttname"><a href="classarm__compute_1_1_c_p_p_detection_output_layer.xhtml#af1d5e758d546e837b9cabb5991d387e0">arm_compute::CPPDetectionOutputLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input_loc, const ITensorInfo *input_conf, const ITensorInfo *input_priorbox, const ITensorInfo *output, DetectionOutputLayerInfo info=DetectionOutputLayerInfo())</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CPPDetectionOutputLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_detection_output_layer_8cpp_source.xhtml#l00438">CPPDetectionOutputLayer.cpp:438</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad818ba0ecd4a87d8f1bb0d5b17f07830afbc6c35854fe02eb9e792f897399c42a"><div class="ttname"><a href="namespacearm__compute.xhtml#ad818ba0ecd4a87d8f1bb0d5b17f07830afbc6c35854fe02eb9e792f897399c42a">arm_compute::DetectionOutputLayerCodeType::CORNER_SIZE</a></div><div class="ttdoc">Use box centers and size.</div></div> |
| <div class="ttc" id="arm__compute_2core_2_helpers_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_helpers_8h.xhtml">Helpers.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a810a78f0b7cc0270f38d4136e023ea3b"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a810a78f0b7cc0270f38d4136e023ea3b">arm_compute::Dimensions::set_num_dimensions</a></div><div class="ttdeci">void set_num_dimensions(size_t num_dimensions)</div><div class="ttdoc">Set number of dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00128">Dimensions.h:128</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_valid_region_xhtml"><div class="ttname"><a href="structarm__compute_1_1_valid_region.xhtml">arm_compute::ValidRegion</a></div><div class="ttdoc">Container for valid region of a window.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00174">Types.h:174</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a4f4125dba5283887b34f889b1c615c0c"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">arm_compute::test::validation::info</a></div><div class="ttdeci">info</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00174">ConvolutionLayer.cpp:174</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_output_layer_info_xhtml_a158d49c7c1df3c6c6589b47d3de56cf0"><div class="ttname"><a href="classarm__compute_1_1_detection_output_layer_info.xhtml#a158d49c7c1df3c6c6589b47d3de56cf0">arm_compute::DetectionOutputLayerInfo::background_label_id</a></div><div class="ttdeci">int background_label_id() const</div><div class="ttdoc">Get background label ID.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01046">Types.h:1046</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_output_layer_info_xhtml_a47c941c24980e6f61a74986c4a16c16c"><div class="ttname"><a href="classarm__compute_1_1_detection_output_layer_info.xhtml#a47c941c24980e6f61a74986c4a16c16c">arm_compute::DetectionOutputLayerInfo::confidence_threshold</a></div><div class="ttdeci">float confidence_threshold() const</div><div class="ttdoc">Get confidence threshold.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01051">Types.h:1051</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml"><div class="ttname"><a href="_validate_8h.xhtml">Validate.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_output_layer_info_xhtml_ae82a2ccc5739cb255a9a7679d6161399"><div class="ttname"><a href="classarm__compute_1_1_detection_output_layer_info.xhtml#ae82a2ccc5739cb255a9a7679d6161399">arm_compute::DetectionOutputLayerInfo::num_loc_classes</a></div><div class="ttdeci">int num_loc_classes() const</div><div class="ttdoc">Get number of location classes.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01061">Types.h:1061</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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