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| <div class="title">CPPDetectionPostProcessLayer.cpp</div> </div> |
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| <a href="_c_p_p_detection_post_process_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) 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_post_process_layer_8h.xhtml">arm_compute/runtime/CPP/functions/CPPDetectionPostProcessLayer.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 <cstddef></span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include <ios></span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include <list></span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> {</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="keyword">namespace</span></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> Status validate_arguments(<span class="keyword">const</span> ITensorInfo *input_box_encoding, <span class="keyword">const</span> ITensorInfo *input_class_score, <span class="keyword">const</span> ITensorInfo *input_anchors,</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  ITensorInfo *output_boxes, ITensorInfo *output_classes, ITensorInfo *output_scores, ITensorInfo *num_detection,</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  DetectionPostProcessLayerInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kBatchSize, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kNumCoordBox)</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> {</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(input_box_encoding, input_class_score, input_anchors);</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(input_box_encoding, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>);</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input_box_encoding, input_anchors);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(input_box_encoding->num_dimensions() > 3, <span class="stringliteral">"The location input tensor shape should be [4, N, kBatchSize]."</span>);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keywordflow">if</span>(input_box_encoding->num_dimensions() > 2)</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  {</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <a class="code" href="_error_8h.xhtml#ab7f9a52dc65a6a76f1576d99828fa1ea">ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR</a>(input_box_encoding->dimension(2) != kBatchSize, <span class="stringliteral">"The third dimension of the input box_encoding tensor should be equal to %d."</span>, kBatchSize);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  }</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <a class="code" href="_error_8h.xhtml#ab7f9a52dc65a6a76f1576d99828fa1ea">ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR</a>(input_box_encoding->dimension(0) != kNumCoordBox, <span class="stringliteral">"The first dimension of the input box_encoding tensor should be equal to %d."</span>, kNumCoordBox);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(input_class_score->dimension(0) != (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.num_classes() + 1),</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="stringliteral">"The first dimension of the input class_prediction should be equal to the number of classes plus one."</span>);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> </div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(input_anchors->num_dimensions() > 3, <span class="stringliteral">"The anchors input tensor shape should be [4, N, kBatchSize]."</span>);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">if</span>(input_anchors->num_dimensions() > 2)</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  {</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <a class="code" href="_error_8h.xhtml#ab7f9a52dc65a6a76f1576d99828fa1ea">ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR</a>(input_anchors->dimension(0) != kNumCoordBox, <span class="stringliteral">"The first dimension of the input anchors tensor should be equal to %d."</span>, kNumCoordBox);</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>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>((input_box_encoding->dimension(1) != input_class_score->dimension(1))</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  || (input_box_encoding->dimension(1) != input_anchors->dimension(1)),</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="stringliteral">"The second dimension of the inputs should be the same."</span>);</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(num_detection->num_dimensions() > 1, <span class="stringliteral">"The num_detection output tensor shape should be [M]."</span>);</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.iou_threshold() <= 0.0f) || (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.iou_threshold() > 1.0f), <span class="stringliteral">"The intersection over union should be positive and less than 1."</span>);</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <a class="code" href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.max_classes_per_detection() <= 0, <span class="stringliteral">"The number of max classes per detection should be positive."</span>);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_detected_boxes = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.max_detections() * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.max_classes_per_detection();</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="comment">// Validate configured outputs</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keywordflow">if</span>(output_boxes->total_size() != 0)</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  {</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <a class="code" href="_validate_8h.xhtml#a1da797d2762c1cdbb73bfc83136c3a38">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS</a>(output_boxes->tensor_shape(), TensorShape(4U, num_detected_boxes, 1U));</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(output_boxes, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  }</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordflow">if</span>(output_classes->total_size() != 0)</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  {</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="_validate_8h.xhtml#a1da797d2762c1cdbb73bfc83136c3a38">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS</a>(output_classes->tensor_shape(), TensorShape(num_detected_boxes, 1U));</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(output_classes, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  }</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keywordflow">if</span>(output_scores->total_size() != 0)</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  {</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <a class="code" href="_validate_8h.xhtml#a1da797d2762c1cdbb73bfc83136c3a38">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS</a>(output_scores->tensor_shape(), TensorShape(num_detected_boxes, 1U));</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(output_scores, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  }</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordflow">if</span>(num_detection->total_size() != 0)</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>  <a class="code" href="_validate_8h.xhtml#a1da797d2762c1cdbb73bfc83136c3a38">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS</a>(num_detection->tensor_shape(), TensorShape(1U));</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(num_detection, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  }</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="keywordflow">return</span> Status{};</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> }</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> <span class="comment"></span></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> <span class="comment">/** Decode a bbox according to a anchors and scale info.</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> <span class="comment"> *</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> <span class="comment"> * @param[in] input_box_encoding The input prior bounding boxes.</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> <span class="comment"> * @param[in] input_anchors The corresponding input variance.</span></div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> <span class="comment"> * @param[in] info The detection informations</span></div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> <span class="comment"> * @param[out] decoded_boxes The decoded bboxes.</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> <span class="comment"> */</span></div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> <span class="keywordtype">void</span> DecodeCenterSizeBoxes(<span class="keyword">const</span> ITensor *input_box_encoding, <span class="keyword">const</span> ITensor *input_anchors, DetectionPostProcessLayerInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>, Tensor *decoded_boxes)</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="keyword">const</span> QuantizationInfo &qi_box = input_box_encoding->info()->quantization_info();</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keyword">const</span> QuantizationInfo &qi_anchors = input_anchors->info()->quantization_info();</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <a class="code" href="namespacearm__compute.xhtml#aa2b075b5da72ec6bb14f90c202443eb0">BBox</a> box_centersize;</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <a class="code" href="namespacearm__compute.xhtml#aa2b075b5da72ec6bb14f90c202443eb0">BBox</a> anchor;</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  Window win;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  win.use_tensor_dimensions(input_box_encoding->info()->tensor_shape());</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  win.set_dimension_step(0U, 4U);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  win.set_dimension_step(1U, 1U);</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  Iterator box_it(input_box_encoding, win);</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  Iterator anchor_it(input_anchors, win);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  Iterator decoded_it(decoded_boxes, win);</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> half_factor = 0.5f;</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>  <a class="code" href="namespacearm__compute.xhtml#a5002bf7ec46d52971f9526e94172cfee">execute_window_loop</a>(win, [&](<span class="keyword">const</span> Coordinates &)</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  {</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute.xhtml#a0bee325b210f81bb89fe1f9e15badf9c">is_data_type_quantized</a>(input_box_encoding->info()->data_type()))</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  {</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keyword">const</span> <span class="keyword">auto</span> box_ptr = reinterpret_cast<const qasymm8_t *>(box_it.ptr());</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keyword">const</span> <span class="keyword">auto</span> anchor_ptr = reinterpret_cast<const qasymm8_t *>(anchor_it.ptr());</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  box_centersize = <a class="code" href="namespacearm__compute.xhtml#aa2b075b5da72ec6bb14f90c202443eb0">BBox</a>({ <a class="code" href="namespacearm__compute.xhtml#ac1d8253f8b422e143ab989ad2a4d29dd">dequantize_qasymm8</a>(*box_ptr, qi_box), <a class="code" href="namespacearm__compute.xhtml#ac1d8253f8b422e143ab989ad2a4d29dd">dequantize_qasymm8</a>(*(box_ptr + 1), qi_box),</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <a class="code" href="namespacearm__compute.xhtml#ac1d8253f8b422e143ab989ad2a4d29dd">dequantize_qasymm8</a>(*(2 + box_ptr), qi_box), <a class="code" href="namespacearm__compute.xhtml#ac1d8253f8b422e143ab989ad2a4d29dd">dequantize_qasymm8</a>(*(3 + box_ptr), qi_box)</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  });</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  anchor = <a class="code" href="namespacearm__compute.xhtml#aa2b075b5da72ec6bb14f90c202443eb0">BBox</a>({ <a class="code" href="namespacearm__compute.xhtml#ac1d8253f8b422e143ab989ad2a4d29dd">dequantize_qasymm8</a>(*anchor_ptr, qi_anchors), <a class="code" href="namespacearm__compute.xhtml#ac1d8253f8b422e143ab989ad2a4d29dd">dequantize_qasymm8</a>(*(anchor_ptr + 1), qi_anchors),</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <a class="code" href="namespacearm__compute.xhtml#ac1d8253f8b422e143ab989ad2a4d29dd">dequantize_qasymm8</a>(*(2 + anchor_ptr), qi_anchors), <a class="code" href="namespacearm__compute.xhtml#ac1d8253f8b422e143ab989ad2a4d29dd">dequantize_qasymm8</a>(*(3 + anchor_ptr), qi_anchors)</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  });</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  }</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  {</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keyword">const</span> <span class="keyword">auto</span> box_ptr = reinterpret_cast<const float *>(box_it.ptr());</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keyword">const</span> <span class="keyword">auto</span> anchor_ptr = reinterpret_cast<const float *>(anchor_it.ptr());</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  box_centersize = <a class="code" href="namespacearm__compute.xhtml#aa2b075b5da72ec6bb14f90c202443eb0">BBox</a>({ *box_ptr, *(box_ptr + 1), *(2 + box_ptr), *(3 + box_ptr) });</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  anchor = <a class="code" href="namespacearm__compute.xhtml#aa2b075b5da72ec6bb14f90c202443eb0">BBox</a>({ *anchor_ptr, *(anchor_ptr + 1), *(2 + anchor_ptr), *(3 + anchor_ptr) });</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  }</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="comment">// BBox is equavalent to CenterSizeEncoding [y,x,h,w]</span></div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> y_center = box_centersize[0] / <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.scale_value_y() * anchor[2] + anchor[0];</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> x_center = box_centersize[1] / <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.scale_value_x() * anchor[3] + anchor[1];</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> half_h = half_factor * static_cast<float>(std::exp(box_centersize[2] / <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.scale_value_h())) * anchor[2];</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> half_w = half_factor * static_cast<float>(std::exp(box_centersize[3] / <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.scale_value_w())) * anchor[3];</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="comment">// Box Corner encoding boxes are saved as [xmin, ymin, xmax, ymax]</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keyword">auto</span> decoded_ptr = reinterpret_cast<float *>(decoded_it.ptr());</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  *(decoded_ptr) = x_center - half_w; <span class="comment">// xmin</span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  *(1 + decoded_ptr) = y_center - half_h; <span class="comment">// ymin</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  *(2 + decoded_ptr) = x_center + half_w; <span class="comment">// xmax</span></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  *(3 + decoded_ptr) = y_center + half_h; <span class="comment">// ymax</span></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>  box_it, anchor_it, decoded_it);</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> }</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> <span class="keywordtype">void</span> SaveOutputs(<span class="keyword">const</span> Tensor *decoded_boxes, <span class="keyword">const</span> std::vector<int> &result_idx_boxes_after_nms, <span class="keyword">const</span> std::vector<float> &result_scores_after_nms, <span class="keyword">const</span> std::vector<int> &result_classes_after_nms,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  std::vector<unsigned int> &sorted_indices, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_output, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> max_detections, ITensor *output_boxes, ITensor *output_classes, ITensor *output_scores,</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  ITensor *num_detection)</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> {</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="comment">// xmin,ymin,xmax,ymax -> ymin,xmin,ymax,xmax</span></div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0;</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keywordflow">for</span>(; i < num_output; ++i)</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  {</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> box_in_idx = result_idx_boxes_after_nms[sorted_indices[i]];</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  *(reinterpret_cast<float *>(output_boxes->ptr_to_element(Coordinates(0, i)))) = *(reinterpret_cast<float *>(decoded_boxes->ptr_to_element(Coordinates(1, box_in_idx))));</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  *(reinterpret_cast<float *>(output_boxes->ptr_to_element(Coordinates(1, i)))) = *(reinterpret_cast<float *>(decoded_boxes->ptr_to_element(Coordinates(0, box_in_idx))));</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  *(reinterpret_cast<float *>(output_boxes->ptr_to_element(Coordinates(2, i)))) = *(reinterpret_cast<float *>(decoded_boxes->ptr_to_element(Coordinates(3, box_in_idx))));</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  *(reinterpret_cast<float *>(output_boxes->ptr_to_element(Coordinates(3, i)))) = *(reinterpret_cast<float *>(decoded_boxes->ptr_to_element(Coordinates(2, box_in_idx))));</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  *(reinterpret_cast<float *>(output_classes->ptr_to_element(Coordinates(i)))) = static_cast<float>(result_classes_after_nms[sorted_indices[i]]);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  *(reinterpret_cast<float *>(output_scores->ptr_to_element(Coordinates(i)))) = result_scores_after_nms[sorted_indices[i]];</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  }</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keywordflow">for</span>(; i < max_detections; ++i)</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  {</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  *(reinterpret_cast<float *>(output_boxes->ptr_to_element(Coordinates(1, i)))) = 0.0f;</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  *(reinterpret_cast<float *>(output_boxes->ptr_to_element(Coordinates(0, i)))) = 0.0f;</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  *(reinterpret_cast<float *>(output_boxes->ptr_to_element(Coordinates(3, i)))) = 0.0f;</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  *(reinterpret_cast<float *>(output_boxes->ptr_to_element(Coordinates(2, i)))) = 0.0f;</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  *(reinterpret_cast<float *>(output_classes->ptr_to_element(Coordinates(i)))) = 0.0f;</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  *(reinterpret_cast<float *>(output_scores->ptr_to_element(Coordinates(i)))) = 0.0f;</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  }</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  *(reinterpret_cast<float *>(num_detection->ptr_to_element(Coordinates(0)))) = num_output;</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> }</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> } <span class="comment">// namespace</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> </div><div class="line"><a name="l00184"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_p_p_detection_post_process_layer.xhtml#a39f8e99aae48c88ae3b3d98692c4107f"> 184</a></span> <a class="code" href="classarm__compute_1_1_c_p_p_detection_post_process_layer.xhtml#a39f8e99aae48c88ae3b3d98692c4107f">CPPDetectionPostProcessLayer::CPPDetectionPostProcessLayer</a>(std::shared_ptr<IMemoryManager> memory_manager)</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  : _memory_group(std::move(memory_manager)), _nms(), _input_box_encoding(nullptr), _input_scores(nullptr), _input_anchors(nullptr), _output_boxes(nullptr), _output_classes(nullptr),</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  _output_scores(nullptr), _num_detection(nullptr), _info(), _num_boxes(), _num_classes_with_background(), _num_max_detected_boxes(), _dequantize_scores(false), _decoded_boxes(), _decoded_scores(),</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  _selected_indices(), _class_scores(), _input_scores_to_use(nullptr)</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> {</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> </div><div class="line"><a name="l00191"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_p_p_detection_post_process_layer.xhtml#ab8136561c7a7d555b3a2c3bf0614986c"> 191</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_p_p_detection_post_process_layer.xhtml#ab8136561c7a7d555b3a2c3bf0614986c">CPPDetectionPostProcessLayer::configure</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *input_box_encoding, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *input_scores, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *input_anchors,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output_boxes, <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output_classes, <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output_scores, <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *num_detection, <a class="code" href="classarm__compute_1_1_detection_post_process_layer_info.xhtml">DetectionPostProcessLayerInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>)</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>  <a class="code" href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a>(input_box_encoding, input_scores, input_anchors, output_boxes, output_classes, output_scores);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  _num_max_detected_boxes = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.max_detections() * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.max_classes_per_detection();</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>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*output_boxes-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(_kNumCoordBox, _num_max_detected_boxes, _kBatchSize), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*output_classes-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(_num_max_detected_boxes, _kBatchSize), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*output_scores-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(_num_max_detected_boxes, _kBatchSize), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*num_detection-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> </div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="comment">// Perform validation step</span></div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(validate_arguments(input_box_encoding-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), input_scores-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), input_anchors-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), output_boxes-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), output_classes-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), output_scores-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(),</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  num_detection-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(),</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>, _kBatchSize, _kNumCoordBox));</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> </div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  _input_box_encoding = input_box_encoding;</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  _input_scores = input_scores;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  _input_anchors = input_anchors;</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  _output_boxes = output_boxes;</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  _output_classes = output_classes;</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  _output_scores = output_scores;</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  _num_detection = num_detection;</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  _info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>;</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  _num_boxes = input_box_encoding-><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);</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  _num_classes_with_background = _input_scores-><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);</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  _dequantize_scores = (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.dequantize_scores() && <a class="code" href="namespacearm__compute.xhtml#a0bee325b210f81bb89fe1f9e15badf9c">is_data_type_quantized</a>(input_box_encoding-><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#a7cfb31af63202568efef5214acfbf3ba">data_type</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>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*_decoded_boxes.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">info</a>(), <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(_kNumCoordBox, _input_box_encoding-><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), _kBatchSize), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*_decoded_scores.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">info</a>(), <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(_input_scores-><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), _input_scores-><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), _kBatchSize), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*_selected_indices.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">info</a>(), <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.use_regular_nms() ? <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.detection_per_class() : <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.max_detections()), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>));</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_classes_per_box = std::min(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.max_classes_per_detection(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.num_classes());</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*_class_scores.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">info</a>(), <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.use_regular_nms() ? <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(_num_boxes) : <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(_num_boxes * num_classes_per_box), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> </div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  _input_scores_to_use = _dequantize_scores ? &_decoded_scores : _input_scores;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> </div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="comment">// Manage intermediate buffers</span></div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_decoded_boxes);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_decoded_scores);</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_selected_indices);</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">manage</a>(&_class_scores);</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  _nms.<a class="code" href="classarm__compute_1_1_c_p_p_non_maximum_suppression.xhtml#ad8387a0ef4a1e0f7382c06146857e4be">configure</a>(&_decoded_boxes, &_class_scores, &_selected_indices, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.use_regular_nms() ? <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.detection_per_class() : <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.max_detections(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.nms_score_threshold(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.iou_threshold());</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> </div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="comment">// Allocate and reserve intermediate tensors and vectors</span></div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  _decoded_boxes.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  _decoded_scores.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  _selected_indices.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  _class_scores.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> }</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> </div><div class="line"><a name="l00241"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_p_p_detection_post_process_layer.xhtml#a08744faa3f68379fa82bcbd96bbec691"> 241</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_post_process_layer.xhtml#a08744faa3f68379fa82bcbd96bbec691">CPPDetectionPostProcessLayer::validate</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_box_encoding, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_class_score, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input_anchors,</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output_boxes, <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output_classes, <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output_scores, <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *num_detection, <a class="code" href="classarm__compute_1_1_detection_post_process_layer_info.xhtml">DetectionPostProcessLayerInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>)</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> {</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kBatchSize = 1;</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kNumCoordBox = 4;</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> _decoded_boxes_info = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(kNumCoordBox, input_box_encoding-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1)), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> _decoded_scores_info = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(input_box_encoding-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1)), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> _selected_indices_info = <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.max_detections()), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>);</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>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_c_p_p_non_maximum_suppression.xhtml#ac36104d178e32cb6e30af07e69d978d4">CPPNonMaximumSuppression::validate</a>(&_decoded_boxes_info, &_decoded_scores_info, &_selected_indices_info, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.max_detections(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.nms_score_threshold(),</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>.iou_threshold()));</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(validate_arguments(input_box_encoding, input_class_score, input_anchors, output_boxes, output_classes, output_scores, num_detection, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>, kBatchSize, kNumCoordBox));</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span> </div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> }</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> </div><div class="line"><a name="l00257"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_c_p_p_detection_post_process_layer.xhtml#ad1717410afd0be936c6213a63c8005fb"> 257</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_c_p_p_detection_post_process_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">CPPDetectionPostProcessLayer::run</a>()</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>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_classes = _info.<a class="code" href="classarm__compute_1_1_detection_post_process_layer_info.xhtml#adda82c28c368106734620f105bb0e1e3">num_classes</a>();</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> max_detections = _info.<a class="code" href="classarm__compute_1_1_detection_post_process_layer_info.xhtml#a23e83519067d74d4f1855d38741151eb">max_detections</a>();</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> </div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  DecodeCenterSizeBoxes(_input_box_encoding, _input_anchors, _info, &_decoded_boxes);</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="comment">// Decode scores if necessary</span></div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="keywordflow">if</span>(_dequantize_scores)</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  {</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx_c = 0; idx_c < _num_classes_with_background; ++idx_c)</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>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx_b = 0; idx_b < _num_boxes; ++idx_b)</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  {</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  *(reinterpret_cast<float *>(_decoded_scores.<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>(idx_c, idx_b)))) =</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <a class="code" href="namespacearm__compute.xhtml#ac1d8253f8b422e143ab989ad2a4d29dd">dequantize_qasymm8</a>(*(reinterpret_cast<qasymm8_t *>(_input_scores-><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>(idx_c, idx_b)))), _input_scores-><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#a3f3e1a3200223e6a304a533b1016e749">quantization_info</a>());</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>  }</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>  <span class="comment">// Regular NMS</span></div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keywordflow">if</span>(_info.<a class="code" href="classarm__compute_1_1_detection_post_process_layer_info.xhtml#ae1c2cc7a6c3db74d8be5f6e23aa84476">use_regular_nms</a>())</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  {</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  std::vector<int> result_idx_boxes_after_nms;</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  std::vector<int> result_classes_after_nms;</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  std::vector<float> result_scores_after_nms;</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  std::vector<unsigned int> sorted_indices;</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span> </div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> c = 0; c < num_classes; ++c)</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  {</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="comment">// For each boxes get scores of the boxes for the class c</span></div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < _num_boxes; ++i)</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  {</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  *(reinterpret_cast<float *>(_class_scores.<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>(i)))) =</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  *(reinterpret_cast<float *>(_input_scores_to_use-><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>(c + 1, i)))); <span class="comment">// i * _num_classes_with_background + c + 1</span></div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  }</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span> </div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <span class="comment">// Run Non-maxima Suppression</span></div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  _nms.<a class="code" href="classarm__compute_1_1_i_c_p_p_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</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="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < _info.<a class="code" href="classarm__compute_1_1_detection_post_process_layer_info.xhtml#a36e65a7f80a07a2393e6a1cadd974740">detection_per_class</a>(); ++i)</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  {</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="keyword">const</span> <span class="keyword">auto</span> selected_index = *(reinterpret_cast<int *>(_selected_indices.<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>(i))));</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keywordflow">if</span>(selected_index == -1)</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  {</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="comment">// Nms will return -1 for all the last M-elements not valid</span></div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keywordflow">break</span>;</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>  result_idx_boxes_after_nms.emplace_back(selected_index);</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  result_scores_after_nms.emplace_back((reinterpret_cast<float *>(_class_scores.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a24954cca5108a24706441fd99a7fb04c">buffer</a>()))[selected_index]);</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  result_classes_after_nms.emplace_back(c);</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>  }</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span> </div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="comment">// We select the max detection numbers of the highest score of all classes</span></div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keyword">const</span> <span class="keyword">auto</span> num_selected = result_scores_after_nms.size();</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keyword">const</span> <span class="keyword">auto</span> num_output = std::min<unsigned int>(max_detections, num_selected);</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span> </div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="comment">// Sort selected indices based on result scores</span></div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  sorted_indices.resize(num_selected);</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  std::iota(sorted_indices.begin(), sorted_indices.end(), 0);</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  std::partial_sort(sorted_indices.data(),</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  sorted_indices.data() + num_output,</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  sorted_indices.data() + num_selected,</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  [&](<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> first, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> second)</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> </div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keywordflow">return</span> result_scores_after_nms[first] > result_scores_after_nms[second];</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  });</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> </div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  SaveOutputs(&_decoded_boxes, result_idx_boxes_after_nms, result_scores_after_nms, result_classes_after_nms, sorted_indices,</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  num_output, max_detections, _output_boxes, _output_classes, _output_scores, _num_detection);</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  }</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  <span class="comment">// Fast NMS</span></div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  {</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_classes_per_box = std::min<unsigned int>(_info.<a class="code" href="classarm__compute_1_1_detection_post_process_layer_info.xhtml#ad54d768454ff1000504546898078d0de">max_classes_per_detection</a>(), _info.<a class="code" href="classarm__compute_1_1_detection_post_process_layer_info.xhtml#adda82c28c368106734620f105bb0e1e3">num_classes</a>());</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  std::vector<float> max_scores;</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  std::vector<int> box_indices;</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  std::vector<int> max_score_classes;</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> </div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a> = 0; <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a> < _num_boxes; ++<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>)</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  {</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  std::vector<float> box_scores;</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> c = 0; c < num_classes; ++c)</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  {</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  box_scores.emplace_back(*(reinterpret_cast<float *>(_input_scores_to_use-><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>(c + 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>)))));</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>  std::vector<unsigned int> max_score_indices;</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  max_score_indices.resize(_info.<a class="code" href="classarm__compute_1_1_detection_post_process_layer_info.xhtml#adda82c28c368106734620f105bb0e1e3">num_classes</a>());</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  std::iota(max_score_indices.data(), max_score_indices.data() + _info.<a class="code" href="classarm__compute_1_1_detection_post_process_layer_info.xhtml#adda82c28c368106734620f105bb0e1e3">num_classes</a>(), 0);</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  std::partial_sort(max_score_indices.data(),</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  max_score_indices.data() + num_classes_per_box,</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  max_score_indices.data() + num_classes,</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  [&](<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> first, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> second)</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="keywordflow">return</span> box_scores[first] > box_scores[second];</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  });</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span> </div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < num_classes_per_box; ++i)</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  {</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> score_to_add = box_scores[max_score_indices[i]];</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  *(reinterpret_cast<float *>(_class_scores.<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>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a> * num_classes_per_box + i)))) = score_to_add;</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  max_scores.emplace_back(score_to_add);</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  box_indices.emplace_back(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a>);</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  max_score_classes.emplace_back(max_score_indices[i]);</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>  }</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="comment">// Run Non-maxima Suppression</span></div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  _nms.<a class="code" href="classarm__compute_1_1_i_c_p_p_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  std::vector<unsigned int> selected_indices;</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < max_detections; ++i)</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  {</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="comment">// NMS returns M valid indices, the not valid tail is filled with -1</span></div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="keywordflow">if</span>(*(reinterpret_cast<int *>(_selected_indices.<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>(i)))) == -1)</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  {</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="comment">// Nms will return -1 for all the last M-elements not valid</span></div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  }</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  selected_indices.emplace_back(*(reinterpret_cast<int *>(_selected_indices.<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>(i)))));</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  }</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="comment">// We select the max detection numbers of the highest score of all classes</span></div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="keyword">const</span> <span class="keyword">auto</span> num_output = std::min<unsigned int>(_info.<a class="code" href="classarm__compute_1_1_detection_post_process_layer_info.xhtml#a23e83519067d74d4f1855d38741151eb">max_detections</a>(), selected_indices.size());</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>  SaveOutputs(&_decoded_boxes, box_indices, max_scores, max_score_classes, selected_indices,</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  num_output, max_detections, _output_boxes, _output_classes, _output_scores, _num_detection);</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  }</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span> }</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> } <span class="comment">// namespace arm_compute</span></div><div class="ttc" id="namespacearm__compute_xhtml_a0bee325b210f81bb89fe1f9e15badf9c"><div class="ttname"><a href="namespacearm__compute.xhtml#a0bee325b210f81bb89fe1f9e15badf9c">arm_compute::is_data_type_quantized</a></div><div class="ttdeci">bool is_data_type_quantized(DataType dt)</div><div class="ttdoc">Check if a given data type is of quantized type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01117">Utils.h:1117</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_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="namespacearm__compute_xhtml_ac1d8253f8b422e143ab989ad2a4d29dd"><div class="ttname"><a href="namespacearm__compute.xhtml#ac1d8253f8b422e143ab989ad2a4d29dd">arm_compute::dequantize_qasymm8</a></div><div class="ttdeci">float dequantize_qasymm8(uint8_t value, const INFO_TYPE &qinfo)</div><div class="ttdoc">Dequantize a value given an unsigned 8-bit asymmetric quantization scheme.</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00338">QuantizationInfo.h:338</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a178f0d3d87f959e00a743328d95359d2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">arm_compute::ITensorInfo::dimension</a></div><div class="ttdeci">virtual size_t dimension(size_t index) const =0</div><div class="ttdoc">Return the size of the requested dimension.</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_aa76b4a6e74940dabc5b7fc6b2dab3545"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">arm_compute::test::validation::b</a></div><div class="ttdeci">SimpleTensor< float > b</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_2_d_f_t_8cpp_source.xhtml#l00157">DFT.cpp:157</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_post_process_layer_info_xhtml_a23e83519067d74d4f1855d38741151eb"><div class="ttname"><a href="classarm__compute_1_1_detection_post_process_layer_info.xhtml#a23e83519067d74d4f1855d38741151eb">arm_compute::DetectionPostProcessLayerInfo::max_detections</a></div><div class="ttdeci">unsigned int max_detections() const</div><div class="ttdoc">Get max detections.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01134">Types.h:1134</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_ab7f9a52dc65a6a76f1576d99828fa1ea"><div class="ttname"><a href="_error_8h.xhtml#ab7f9a52dc65a6a76f1576d99828fa1ea">ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR(cond, msg,...)</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#l00227">Error.h:227</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#l00204">Error.h:204</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7cfb31af63202568efef5214acfbf3ba"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">arm_compute::ITensorInfo::data_type</a></div><div class="ttdeci">virtual DataType data_type() const =0</div><div class="ttdoc">Data type used for each element of the tensor.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_p_p_detection_post_process_layer_xhtml_a08744faa3f68379fa82bcbd96bbec691"><div class="ttname"><a href="classarm__compute_1_1_c_p_p_detection_post_process_layer.xhtml#a08744faa3f68379fa82bcbd96bbec691">arm_compute::CPPDetectionPostProcessLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input_box_encoding, const ITensorInfo *input_class_score, const ITensorInfo *input_anchors, ITensorInfo *output_boxes, ITensorInfo *output_classes, ITensorInfo *output_scores, ITensorInfo *num_detection, DetectionPostProcessLayerInfo info=DetectionPostProcessLayerInfo())</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of CPPDetectionPostProcessL...</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_detection_post_process_layer_8cpp_source.xhtml#l00241">CPPDetectionPostProcessLayer.cpp:241</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#l00792">Validate.h:792</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="classarm__compute_1_1_c_p_p_detection_post_process_layer_xhtml_a39f8e99aae48c88ae3b3d98692c4107f"><div class="ttname"><a href="classarm__compute_1_1_c_p_p_detection_post_process_layer.xhtml#a39f8e99aae48c88ae3b3d98692c4107f">arm_compute::CPPDetectionPostProcessLayer::CPPDetectionPostProcessLayer</a></div><div class="ttdeci">CPPDetectionPostProcessLayer(std::shared_ptr< IMemoryManager > memory_manager=nullptr)</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_detection_post_process_layer_8cpp_source.xhtml#l00184">CPPDetectionPostProcessLayer.cpp:184</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#l00455">Error.h:455</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="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_detection_post_process_layer_info_xhtml_ae1c2cc7a6c3db74d8be5f6e23aa84476"><div class="ttname"><a href="classarm__compute_1_1_detection_post_process_layer_info.xhtml#ae1c2cc7a6c3db74d8be5f6e23aa84476">arm_compute::DetectionPostProcessLayerInfo::use_regular_nms</a></div><div class="ttdeci">bool use_regular_nms() const</div><div class="ttdoc">Get if use regular nms.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01164">Types.h:1164</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2020 ARM Limited.</div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</a></div></div> |
| <div class="ttc" id="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#l00202">Helpers.inl:202</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_a47d74e4e51f9b1a636c4831bd747a97c"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">arm_compute::Tensor::info</a></div><div class="ttdeci">ITensorInfo * info() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00033">Tensor.cpp:33</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::Format::S32</a></div><div class="ttdoc">1 channel, 1 S32 per channel</div></div> |
| <div class="ttc" id="classarm__compute_1_1_memory_group_xhtml_a6fc0a49304c152c20a0f6df0634fb3cd"><div class="ttname"><a href="classarm__compute_1_1_memory_group.xhtml#a6fc0a49304c152c20a0f6df0634fb3cd">arm_compute::MemoryGroup::manage</a></div><div class="ttdeci">void manage(IMemoryManageable *obj) override</div><div class="ttdoc">Sets a object to be managed by the given memory group.</div><div class="ttdef"><b>Definition:</b> <a href="_memory_group_8h_source.xhtml#l00079">MemoryGroup.h:79</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_c_p_p_detection_post_process_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_c_p_p_detection_post_process_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::CPPDetectionPostProcessLayer::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_post_process_layer_8cpp_source.xhtml#l00257">CPPDetectionPostProcessLayer.cpp:257</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="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::DataType::QASYMM8</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number unsigned</div></div> |
| <div class="ttc" id="classarm__compute_1_1_coordinates_xhtml"><div class="ttname"><a href="classarm__compute_1_1_coordinates.xhtml">arm_compute::Coordinates</a></div><div class="ttdoc">Coordinates of an item.</div><div class="ttdef"><b>Definition:</b> <a href="_coordinates_8h_source.xhtml#l00037">Coordinates.h:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</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#l00959">Types.h:959</a></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="_error_8h_xhtml"><div class="ttname"><a href="_error_8h.xhtml">Error.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a3f3e1a3200223e6a304a533b1016e749"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">arm_compute::ITensorInfo::quantization_info</a></div><div class="ttdeci">virtual QuantizationInfo quantization_info() const =0</div><div class="ttdoc">Get the quantization settings (scale and offset) of the tensor.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_post_process_layer_info_xhtml_ad54d768454ff1000504546898078d0de"><div class="ttname"><a href="classarm__compute_1_1_detection_post_process_layer_info.xhtml#ad54d768454ff1000504546898078d0de">arm_compute::DetectionPostProcessLayerInfo::max_classes_per_detection</a></div><div class="ttdeci">unsigned int max_classes_per_detection() const</div><div class="ttdoc">Get max_classes per detection.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01139">Types.h:1139</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_c_p_p_non_maximum_suppression_xhtml_ac36104d178e32cb6e30af07e69d978d4"><div class="ttname"><a href="classarm__compute_1_1_c_p_p_non_maximum_suppression.xhtml#ac36104d178e32cb6e30af07e69d978d4">arm_compute::CPPNonMaximumSuppression::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *bboxes, const ITensorInfo *scores, const ITensorInfo *indices, unsigned int max_output_size, const float score_threshold, const float nms_threshold)</div><div class="ttdoc">Static function to check if given arguments will lead to a valid configuration of CPPNonMaximumSuppre...</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_non_maximum_suppression_8cpp_source.xhtml#l00040">CPPNonMaximumSuppression.cpp:40</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="classarm__compute_1_1_c_p_p_detection_post_process_layer_xhtml_ab8136561c7a7d555b3a2c3bf0614986c"><div class="ttname"><a href="classarm__compute_1_1_c_p_p_detection_post_process_layer.xhtml#ab8136561c7a7d555b3a2c3bf0614986c">arm_compute::CPPDetectionPostProcessLayer::configure</a></div><div class="ttdeci">void configure(const ITensor *input_box_encoding, const ITensor *input_score, const ITensor *input_anchors, ITensor *output_boxes, ITensor *output_classes, ITensor *output_scores, ITensor *num_detection, DetectionPostProcessLayerInfo info=DetectionPostProcessLayerInfo())</div><div class="ttdoc">Configure the detection output layer CPP function.</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_detection_post_process_layer_8cpp_source.xhtml#l00191">CPPDetectionPostProcessLayer.cpp:191</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_post_process_layer_info_xhtml_a36e65a7f80a07a2393e6a1cadd974740"><div class="ttname"><a href="classarm__compute_1_1_detection_post_process_layer_info.xhtml#a36e65a7f80a07a2393e6a1cadd974740">arm_compute::DetectionPostProcessLayerInfo::detection_per_class</a></div><div class="ttdeci">unsigned int detection_per_class() const</div><div class="ttdoc">Get detection per class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01144">Types.h:1144</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_post_process_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_detection_post_process_layer_info.xhtml">arm_compute::DetectionPostProcessLayerInfo</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#l01092">Types.h:1092</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_detection_post_process_layer_info_xhtml_adda82c28c368106734620f105bb0e1e3"><div class="ttname"><a href="classarm__compute_1_1_detection_post_process_layer_info.xhtml#adda82c28c368106734620f105bb0e1e3">arm_compute::DetectionPostProcessLayerInfo::num_classes</a></div><div class="ttdeci">unsigned 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#l01159">Types.h:1159</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_a24954cca5108a24706441fd99a7fb04c"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#a24954cca5108a24706441fd99a7fb04c">arm_compute::Tensor::buffer</a></div><div class="ttdeci">uint8_t * buffer() const override</div><div class="ttdoc">Interface to be implemented by the child class to return a pointer to CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00043">Tensor.cpp:43</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a1c69762a42ab8add645d0a949b6f4b1f"><div class="ttname"><a href="_error_8h.xhtml#a1c69762a42ab8add645d0a949b6f4b1f">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond, msg)</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#l00244">Error.h:244</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_c_p_p_non_maximum_suppression_xhtml_ad8387a0ef4a1e0f7382c06146857e4be"><div class="ttname"><a href="classarm__compute_1_1_c_p_p_non_maximum_suppression.xhtml#ad8387a0ef4a1e0f7382c06146857e4be">arm_compute::CPPNonMaximumSuppression::configure</a></div><div class="ttdeci">void configure(const ITensor *bboxes, const ITensor *scores, ITensor *indices, unsigned int max_output_size, const float score_threshold, const float nms_threshold)</div><div class="ttdoc">Configure the function to perform non maximal suppression.</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_non_maximum_suppression_8cpp_source.xhtml#l00031">CPPNonMaximumSuppression.cpp:31</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</a></div></div> |
| <div class="ttc" id="arm__compute_2core_2_helpers_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_helpers_8h.xhtml">Helpers.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a5002bf7ec46d52971f9526e94172cfee"><div class="ttname"><a href="namespacearm__compute.xhtml#a5002bf7ec46d52971f9526e94172cfee">arm_compute::execute_window_loop</a></div><div class="ttdeci">void execute_window_loop(const Window &w, L &&lambda_function, Ts &&... iterators)</div><div class="ttdoc">Iterate through the passed window, automatically adjusting the iterators and calling the lambda_funct...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00123">Helpers.inl:123</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_c_p_p_simple_function_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_c_p_p_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::ICPPSimpleFunction::run</a></div><div class="ttdeci">void run() override final</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_c_p_p_simple_function_8cpp_source.xhtml#l00035">ICPPSimpleFunction.cpp:35</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="_c_l_2_convolution_layer_8cpp_source.xhtml#l00182">ConvolutionLayer.cpp:182</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="_c_p_p_detection_post_process_layer_8h_xhtml"><div class="ttname"><a href="_c_p_p_detection_post_process_layer_8h.xhtml">CPPDetectionPostProcessLayer.h</a></div></div> |
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