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<a href="validation_2reference_2_normalization_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) 2017 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="tests_2validation_2reference_2_normalization_layer_8h.xhtml">NormalizationLayer.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="arm__compute_2core_2_types_8h.xhtml">arm_compute/core/Types.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="tests_2validation_2_fixed_point_8h.xhtml">tests/validation/FixedPoint.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> </div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></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="keyword">namespace </span>test</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> {</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="keyword">namespace </span>validation</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>reference</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> <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">template <typename T, typename std::enable_if<is_floating_point<T>::value</a>, <span class="keywordtype">int</span>><a class="code" href="namespacecaffe__data__extractor.xhtml#a7aead736a07eaf25623ad7bfa1f0ee2d">::type</a>></div><div class="line"><a name="l00038"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a041ee28e793d018db2379eb8eb3d1722"> 38</a></span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<T></a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a041ee28e793d018db2379eb8eb3d1722">normalization_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<T></a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, <a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml">NormalizationLayerInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>)</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> {</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="comment">// Create reference</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<T></a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>{ src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>(), src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>(), 1, src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a35ccf2eb0c18a15feab2db98b307b78b">fixed_point_position</a>() };</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="comment">// Compute reference</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keyword">const</span> uint32_t norm_size = info.<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml#a0549be3702c05e6ec1ada69a6d08e349">norm_size</a>();</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <a class="code" href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59ca">NormType</a> <a class="code" href="namespacecaffe__data__extractor.xhtml#a7aead736a07eaf25623ad7bfa1f0ee2d">type</a> = info.<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml#a39f6445d0b790034f0d8fac36f2eb7f5">type</a>();</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keywordtype">float</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab146b9cbab6e73e7588b240dc709fe01">beta</a> = info.<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml#a55fe6a30749e41ce31c2bb969a5aa25e">beta</a>();</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  uint32_t kappa = info.<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml#a4df91dc0be2437a7d1bfd6d8df72baa8">kappa</a>();</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>  <span class="keyword">const</span> <span class="keywordtype">int</span> cols = src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>()[0];</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> rows = src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>()[1];</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> depth = src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>()[2];</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordtype">int</span> upper_dims = src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().total_size() / (cols * rows);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> </div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordtype">float</span> coeff = info.<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml#a6f541aab23799f6c61d9c8d7ca9fe15c">scale_coeff</a>();</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordtype">int</span> radius_cols = norm_size / 2;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="comment">// IN_MAP_1D and CROSS_MAP normalize over a single axis only</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordtype">int</span> radius_rows = (<a class="code" href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59caa6ff8bd96743aae9fd283cd822b84278e">NormType::IN_MAP_2D</a> == <a class="code" href="namespacecaffe__data__extractor.xhtml#a7aead736a07eaf25623ad7bfa1f0ee2d">type</a>) ? norm_size / 2 : 0;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">if</span>(type == <a class="code" href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59caa980fef040549733973683b1a868f96e5">NormType::CROSS_MAP</a>)</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  {</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="comment">// Remove also depth from upper dimensions since it is the dimension we</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="comment">// want to use for normalization</span></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  upper_dims /= depth;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> r = 0; r < upper_dims; ++r)</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  {</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < rows; ++i)</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  {</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> k = 0; k < cols; ++k)</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>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> l = 0; l < depth; ++l)</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  {</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keywordtype">float</span> accumulated_scale = 0.f;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> j = -radius_cols; j <= radius_cols; ++j)</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  {</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> z = l + j;</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>(z >= 0 && z < depth)</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>  <span class="keyword">const</span> T <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a> = src[k + i * cols + z * rows * cols + r * cols * rows * depth];</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  accumulated_scale += value * <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</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>  }</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="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>[k + i * cols + l * rows * cols + r * cols * rows * depth] = kappa + accumulated_scale * coeff;</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  }</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  }</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>  }</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="keywordflow">else</span></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  {</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> r = 0; r < upper_dims; ++r)</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  {</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < rows; ++i)</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  {</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> k = 0; k < cols; ++k)</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  {</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keywordtype">float</span> accumulated_scale = 0.f;</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> </div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> j = -radius_rows; j <= radius_rows; ++j)</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  {</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> y = i + j;</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> l = -radius_cols; l <= radius_cols; ++l)</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>  <span class="keyword">const</span> <span class="keywordtype">int</span> x = k + l;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> </div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keywordflow">if</span>((x >= 0 && y >= 0) && (x < cols && y < rows))</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  {</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keyword">const</span> T <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a> = src[x + y * cols + r * cols * rows];</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  accumulated_scale += value * <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a>;</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  }</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  }</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  }</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>[k + i * cols + r * cols * rows] = kappa + accumulated_scale * coeff;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  }</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  }</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  }</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  }</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> </div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keywordflow">if</span>(beta == 1.f)</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  {</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.num_elements(); ++i)</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  {</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>[i] = src[i] / <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>[i];</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> <span class="keywordflow">if</span>(beta == 0.5f)</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  {</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.num_elements(); ++i)</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  {</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>[i] = src[i] / std::sqrt(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>[i]);</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  }</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  {</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.num_elements(); ++i)</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  {</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>[i] = src[i] * <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#aa4e01e9be9adcc40a69a4da48fa83a43">std::exp</a>(<a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#a8c8ce35c61b4f71cccec28d18161eaa1">std::log</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>[i]) * -beta);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  }</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> </div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> }</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">template <typename T, typename std::enable_if<std::is_integral<T>::value</a>, <span class="keywordtype">int</span>><a class="code" href="namespacecaffe__data__extractor.xhtml#a7aead736a07eaf25623ad7bfa1f0ee2d">::type</a>></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<T></a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a041ee28e793d018db2379eb8eb3d1722">normalization_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<T></a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, <a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml">NormalizationLayerInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>)</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>  <span class="keyword">using namespace </span>fixed_point_arithmetic;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> </div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="comment">// Create reference</span></div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<T></a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>{ src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>(), src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">data_type</a>(), 1, src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a35ccf2eb0c18a15feab2db98b307b78b">fixed_point_position</a>() };</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> </div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="comment">// Compute reference</span></div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> fixed_point_position = src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a35ccf2eb0c18a15feab2db98b307b78b">fixed_point_position</a>();</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> </div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keyword">const</span> uint32_t norm_size = info.<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml#a0549be3702c05e6ec1ada69a6d08e349">norm_size</a>();</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <a class="code" href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59ca">NormType</a> <a class="code" href="namespacecaffe__data__extractor.xhtml#a7aead736a07eaf25623ad7bfa1f0ee2d">type</a> = info.<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml#a39f6445d0b790034f0d8fac36f2eb7f5">type</a>();</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  fixed_point<T> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab146b9cbab6e73e7588b240dc709fe01">beta</a>(info.<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml#a55fe6a30749e41ce31c2bb969a5aa25e">beta</a>(), fixed_point_position);</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  fixed_point<T> kappa(info.<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml#a4df91dc0be2437a7d1bfd6d8df72baa8">kappa</a>(), fixed_point_position);</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> </div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> cols = src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>()[0];</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> rows = src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>()[1];</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> depth = src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>()[2];</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keywordtype">int</span> upper_dims = src.<a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().total_size() / (cols * rows);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> </div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  fixed_point<T> coeff(info.<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml#a6f541aab23799f6c61d9c8d7ca9fe15c">scale_coeff</a>(), fixed_point_position);</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keywordtype">int</span> radius_cols = norm_size / 2;</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>  <span class="comment">// IN_MAP_1D and CROSS_MAP normalize over a single axis only</span></div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keywordtype">int</span> radius_rows = (<a class="code" href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59caa6ff8bd96743aae9fd283cd822b84278e">NormType::IN_MAP_2D</a> == <a class="code" href="namespacecaffe__data__extractor.xhtml#a7aead736a07eaf25623ad7bfa1f0ee2d">type</a>) ? norm_size / 2 : 0;</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> </div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keywordflow">if</span>(type == <a class="code" href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59caa980fef040549733973683b1a868f96e5">NormType::CROSS_MAP</a>)</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  {</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="comment">// Remove also depth from upper dimensions since it is the dimension we</span></div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="comment">// want to use for normalization</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  upper_dims /= depth;</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="keywordflow">for</span>(<span class="keywordtype">int</span> r = 0; r < upper_dims; ++r)</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  {</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < rows; ++i)</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  {</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> k = 0; k < cols; ++k)</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  {</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> l = 0; l < depth; ++l)</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>  fixed_point<T> accumulated_scale(0.f, fixed_point_position);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> </div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> j = -radius_cols; j <= radius_cols; ++j)</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  {</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> z = l + j;</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> </div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keywordflow">if</span>(z >= 0 && z < depth)</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  {</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keyword">const</span> T <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a> = src[k + i * cols + z * rows * cols + r * cols * rows * depth];</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keyword">const</span> fixed_point<T> fp_value(value, fixed_point_position, <span class="keyword">true</span>);</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  accumulated_scale = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#a9d91c0affa9bc1921abc949791c514c0">add</a>(accumulated_scale, <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#a8d71db736fe80cae37a9c94c57b34ed6">mul</a>(fp_value, fp_value));</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>  }</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span> </div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  accumulated_scale = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#a9d91c0affa9bc1921abc949791c514c0">add</a>(kappa, <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#a8d71db736fe80cae37a9c94c57b34ed6">mul</a>(accumulated_scale, coeff));</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>[k + i * cols + l * rows * cols + r * cols * rows * depth] = accumulated_scale.raw();</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>  }</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  }</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  }</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  }</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  {</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> r = 0; r < upper_dims; ++r)</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  {</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < rows; ++i)</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  {</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> k = 0; k < cols; ++k)</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>  fixed_point<T> accumulated_scale(0.f, fixed_point_position);</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> </div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> j = -radius_rows; j <= radius_rows; ++j)</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  {</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> y = i + j;</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>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> l = -radius_cols; l <= radius_cols; ++l)</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="keyword">const</span> <span class="keywordtype">int</span> x = k + l;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span> </div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keywordflow">if</span>((x >= 0 && y >= 0) && (x < cols && y < rows))</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  {</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keyword">const</span> T <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a> = src[x + y * cols + r * cols * rows];</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keyword">const</span> fixed_point<T> fp_value(value, fixed_point_position, <span class="keyword">true</span>);</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  accumulated_scale = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#a9d91c0affa9bc1921abc949791c514c0">add</a>(accumulated_scale, <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#a8d71db736fe80cae37a9c94c57b34ed6">mul</a>(fp_value, fp_value));</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  }</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  }</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  }</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> </div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  accumulated_scale = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#a9d91c0affa9bc1921abc949791c514c0">add</a>(kappa, <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#a8d71db736fe80cae37a9c94c57b34ed6">mul</a>(accumulated_scale, coeff));</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>[k + i * cols + r * cols * rows] = accumulated_scale.raw();</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  }</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  }</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  }</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  }</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span> </div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keywordflow">if</span>(info.<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml#a55fe6a30749e41ce31c2bb969a5aa25e">beta</a>() == 1.f)</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  {</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.num_elements(); ++i)</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  {</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  fixed_point<T> res = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#a9d53af9692ab2f7ae6fc0017faeb46f0">div</a>(fixed_point<T>(src[i], fixed_point_position, <span class="keyword">true</span>), fixed_point<T>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>[i], fixed_point_position, <span class="keyword">true</span>));</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>[i] = res.raw();</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  }</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  }</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  {</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="keyword">const</span> fixed_point<T> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab146b9cbab6e73e7588b240dc709fe01">beta</a>(info.<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml#a55fe6a30749e41ce31c2bb969a5aa25e">beta</a>(), fixed_point_position);</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> </div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.num_elements(); ++i)</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>  fixed_point<T> res = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#af782da2c5016738c96c16fee5e17670f">pow</a>(fixed_point<T>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>[i], fixed_point_position, <span class="keyword">true</span>), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ab146b9cbab6e73e7588b240dc709fe01">beta</a>);</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  res = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#a9d53af9692ab2f7ae6fc0017faeb46f0">div</a>(fixed_point<T>(src[i], fixed_point_position, <span class="keyword">true</span>), res);</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>[i] = res.raw();</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  }</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  }</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span> </div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>;</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> </div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span> <span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<float></a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a041ee28e793d018db2379eb8eb3d1722">normalization_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<float></a> &src, <a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml">NormalizationLayerInfo</a> info);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> <span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<half></a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a041ee28e793d018db2379eb8eb3d1722">normalization_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<half></a> &src, <a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml">NormalizationLayerInfo</a> info);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> <span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<qint8_t></a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a041ee28e793d018db2379eb8eb3d1722">normalization_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<qint8_t></a> &src, <a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml">NormalizationLayerInfo</a> info);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span> <span class="keyword">template</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<qint16_t></a> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a041ee28e793d018db2379eb8eb3d1722">normalization_layer</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">SimpleTensor<qint16_t></a> &src, <a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml">NormalizationLayerInfo</a> info);</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> } <span class="comment">// namespace reference</span></div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span> } <span class="comment">// namespace validation</span></div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> } <span class="comment">// namespace test</span></div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span> } <span class="comment">// namespace arm_compute</span></div><div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_a8d71db736fe80cae37a9c94c57b34ed6"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#a8d71db736fe80cae37a9c94c57b34ed6">arm_compute::test::fixed_point_arithmetic::detail::mul</a></div><div class="ttdeci">fixed_point< T > mul(fixed_point< T > x, fixed_point< T > y)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00904">FixedPoint.h:904</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ab146b9cbab6e73e7588b240dc709fe01"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ab146b9cbab6e73e7588b240dc709fe01">arm_compute::test::validation::beta</a></div><div class="ttdeci">beta</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00112">GEMM.cpp:112</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ab146b9cbab6e73e7588b240dc709fe01"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ab146b9cbab6e73e7588b240dc709fe01">arm_compute::test::validation::beta</a></div><div class="ttdeci">beta</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_g_e_m_m_8cpp_source.xhtml#l00113">GEMM.cpp:113</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_aa4e01e9be9adcc40a69a4da48fa83a43"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#aa4e01e9be9adcc40a69a4da48fa83a43">arm_compute::test::fixed_point_arithmetic::detail::exp</a></div><div class="ttdeci">fixed_point< T > exp(fixed_point< T > x)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00924">FixedPoint.h:924</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a9a3e72153aeb3ed212e9c3698774e881"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a9a3e72153aeb3ed212e9c3698774e881">arm_compute::test::SimpleTensor::data_type</a></div><div class="ttdeci">DataType data_type() const override</div><div class="ttdoc">Data type of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00282">SimpleTensor.h:282</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_normalization_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_normalization_layer_info.xhtml">arm_compute::NormalizationLayerInfo</a></div><div class="ttdoc">Normalization Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00708">Types.h:708</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_normalization_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_normalization_layer_info.xhtml">arm_compute::NormalizationLayerInfo</a></div><div class="ttdoc">Normalization Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00757">Types.h:757</a></div></div>
<div class="ttc" id="tests_2validation_2reference_2_normalization_layer_8h_xhtml"><div class="ttname"><a href="tests_2validation_2reference_2_normalization_layer_8h.xhtml">NormalizationLayer.h</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_normalization_layer_info_xhtml_a39f6445d0b790034f0d8fac36f2eb7f5"><div class="ttname"><a href="classarm__compute_1_1_normalization_layer_info.xhtml#a39f6445d0b790034f0d8fac36f2eb7f5">arm_compute::NormalizationLayerInfo::type</a></div><div class="ttdeci">NormType type() const </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00725">Types.h:725</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_normalization_layer_info_xhtml_a0549be3702c05e6ec1ada69a6d08e349"><div class="ttname"><a href="classarm__compute_1_1_normalization_layer_info.xhtml#a0549be3702c05e6ec1ada69a6d08e349">arm_compute::NormalizationLayerInfo::norm_size</a></div><div class="ttdeci">uint32_t norm_size() const </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00729">Types.h:729</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_normalization_layer_info_xhtml_a39f6445d0b790034f0d8fac36f2eb7f5"><div class="ttname"><a href="classarm__compute_1_1_normalization_layer_info.xhtml#a39f6445d0b790034f0d8fac36f2eb7f5">arm_compute::NormalizationLayerInfo::type</a></div><div class="ttdeci">NormType type() const </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00774">Types.h:774</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_normalization_layer_info_xhtml_a0549be3702c05e6ec1ada69a6d08e349"><div class="ttname"><a href="classarm__compute_1_1_normalization_layer_info.xhtml#a0549be3702c05e6ec1ada69a6d08e349">arm_compute::NormalizationLayerInfo::norm_size</a></div><div class="ttdeci">uint32_t norm_size() const </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00778">Types.h:778</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_aba5871b3e4a65d057ec1c28fce8b00ba"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">arm_compute::test::SimpleTensor::shape</a></div><div class="ttdeci">TensorShape shape() const override</div><div class="ttdoc">Shape of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00245">SimpleTensor.h:245</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_a041ee28e793d018db2379eb8eb3d1722"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a041ee28e793d018db2379eb8eb3d1722">arm_compute::test::validation::reference::normalization_layer</a></div><div class="ttdeci">SimpleTensor< T > normalization_layer(const SimpleTensor< T > &src, NormalizationLayerInfo info)</div><div class="ttdef"><b>Definition:</b> <a href="validation_2reference_2_normalization_layer_8cpp_source.xhtml#l00038">NormalizationLayer.cpp:38</a></div></div>
<div class="ttc" id="tests_2validation_2_fixed_point_8h_xhtml"><div class="ttname"><a href="tests_2validation_2_fixed_point_8h.xhtml">FixedPoint.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a096668313a9a819d54a2e65ec21ff0cc"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">arm_compute::test::validation::info</a></div><div class="ttdeci">src info() -> set_format(Format::S16)</div></div>
-<div class="ttc" id="classarm__compute_1_1_normalization_layer_info_xhtml_a4df91dc0be2437a7d1bfd6d8df72baa8"><div class="ttname"><a href="classarm__compute_1_1_normalization_layer_info.xhtml#a4df91dc0be2437a7d1bfd6d8df72baa8">arm_compute::NormalizationLayerInfo::kappa</a></div><div class="ttdeci">float kappa() const </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00741">Types.h:741</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_normalization_layer_info_xhtml_a4df91dc0be2437a7d1bfd6d8df72baa8"><div class="ttname"><a href="classarm__compute_1_1_normalization_layer_info.xhtml#a4df91dc0be2437a7d1bfd6d8df72baa8">arm_compute::NormalizationLayerInfo::kappa</a></div><div class="ttdeci">float kappa() const </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00790">Types.h:790</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">This file contains all available output stages for GEMMLowp on OpenCL. </div><div class="ttdef"><b>Definition:</b> <a href="01__library_8dox_source.xhtml#l00001">01_library.dox:1</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_a8c8ce35c61b4f71cccec28d18161eaa1"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#a8c8ce35c61b4f71cccec28d18161eaa1">arm_compute::test::fixed_point_arithmetic::detail::log</a></div><div class="ttdeci">fixed_point< T > log(fixed_point< T > x)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00929">FixedPoint.h:929</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_a9d91c0affa9bc1921abc949791c514c0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#a9d91c0affa9bc1921abc949791c514c0">arm_compute::test::fixed_point_arithmetic::detail::add</a></div><div class="ttdeci">fixed_point< T > add(fixed_point< T > x, fixed_point< T > y)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00894">FixedPoint.h:894</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_adbf67dcee294e673cf796f1ed8aeb6a4"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">arm_compute::test::validation::dst</a></div><div class="ttdeci">CLTensor dst</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_8cpp_source.xhtml#l00123">Convolution.cpp:123</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_normalization_layer_info_xhtml_a6f541aab23799f6c61d9c8d7ca9fe15c"><div class="ttname"><a href="classarm__compute_1_1_normalization_layer_info.xhtml#a6f541aab23799f6c61d9c8d7ca9fe15c">arm_compute::NormalizationLayerInfo::scale_coeff</a></div><div class="ttdeci">float scale_coeff() const </div><div class="ttdoc">Return the scaling factor of the normalization function. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00760">Types.h:760</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_adbf67dcee294e673cf796f1ed8aeb6a4"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">arm_compute::test::validation::dst</a></div><div class="ttdeci">CLTensor dst</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_8cpp_source.xhtml#l00137">Convolution.cpp:137</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_normalization_layer_info_xhtml_a6f541aab23799f6c61d9c8d7ca9fe15c"><div class="ttname"><a href="classarm__compute_1_1_normalization_layer_info.xhtml#a6f541aab23799f6c61d9c8d7ca9fe15c">arm_compute::NormalizationLayerInfo::scale_coeff</a></div><div class="ttdeci">float scale_coeff() const </div><div class="ttdoc">Return the scaling factor of the normalization function. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00809">Types.h:809</a></div></div>
<div class="ttc" id="hwc_8hpp_xhtml_a0f61d63b009d0880a89c843bd50d8d76"><div class="ttname"><a href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a></div><div class="ttdeci">void * value</div><div class="ttdef"><b>Definition:</b> <a href="hwc_8hpp_source.xhtml#l00278">hwc.hpp:278</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml">arm_compute::test::SimpleTensor</a></div><div class="ttdoc">Simple tensor object that stores elements in a consecutive chunk of memory. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00059">SimpleTensor.h:59</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_normalization_layer_info_xhtml_a55fe6a30749e41ce31c2bb969a5aa25e"><div class="ttname"><a href="classarm__compute_1_1_normalization_layer_info.xhtml#a55fe6a30749e41ce31c2bb969a5aa25e">arm_compute::NormalizationLayerInfo::beta</a></div><div class="ttdeci">float beta() const </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00737">Types.h:737</a></div></div>
+<div class="ttc" id="classarm__compute_1_1_normalization_layer_info_xhtml_a55fe6a30749e41ce31c2bb969a5aa25e"><div class="ttname"><a href="classarm__compute_1_1_normalization_layer_info.xhtml#a55fe6a30749e41ce31c2bb969a5aa25e">arm_compute::NormalizationLayerInfo::beta</a></div><div class="ttdeci">float beta() const </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00786">Types.h:786</a></div></div>
<div class="ttc" id="namespacecaffe__data__extractor_xhtml_a7aead736a07eaf25623ad7bfa1f0ee2d"><div class="ttname"><a href="namespacecaffe__data__extractor.xhtml#a7aead736a07eaf25623ad7bfa1f0ee2d">caffe_data_extractor.type</a></div><div class="ttdeci">type</div><div class="ttdef"><b>Definition:</b> <a href="caffe__data__extractor_8py_source.xhtml#l00019">caffe_data_extractor.py:19</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_a9d53af9692ab2f7ae6fc0017faeb46f0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#a9d53af9692ab2f7ae6fc0017faeb46f0">arm_compute::test::fixed_point_arithmetic::detail::div</a></div><div class="ttdeci">fixed_point< T > div(fixed_point< T > x, fixed_point< T > y)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00909">FixedPoint.h:909</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_af782da2c5016738c96c16fee5e17670f"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#af782da2c5016738c96c16fee5e17670f">arm_compute::test::fixed_point_arithmetic::detail::pow</a></div><div class="ttdeci">fixed_point< T > pow(fixed_point< T > x, fixed_point< T > a)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00944">FixedPoint.h:944</a></div></div>
<div class="ttc" id="arm__compute_2core_2_types_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_types_8h.xhtml">Types.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_simple_tensor_xhtml_a35ccf2eb0c18a15feab2db98b307b78b"><div class="ttname"><a href="classarm__compute_1_1test_1_1_simple_tensor.xhtml#a35ccf2eb0c18a15feab2db98b307b78b">arm_compute::test::SimpleTensor::fixed_point_position</a></div><div class="ttdeci">int fixed_point_position() const override</div><div class="ttdoc">The number of bits for the fractional part of the fixed point numbers. </div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00257">SimpleTensor.h:257</a></div></div>
-<div class="ttc" id="namespacearm__compute_xhtml_ad4bb8dabdbf8ad75e34220cc666b59ca"><div class="ttname"><a href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59ca">arm_compute::NormType</a></div><div class="ttdeci">NormType</div><div class="ttdoc">The normalization type used for the normalization layer. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00419">Types.h:419</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_ad4bb8dabdbf8ad75e34220cc666b59ca"><div class="ttname"><a href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59ca">arm_compute::NormType</a></div><div class="ttdeci">NormType</div><div class="ttdoc">The normalization type used for the normalization layer. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00442">Types.h:442</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad4bb8dabdbf8ad75e34220cc666b59caa980fef040549733973683b1a868f96e5"><div class="ttname"><a href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59caa980fef040549733973683b1a868f96e5">arm_compute::NormType::CROSS_MAP</a></div><div class="ttdoc">Normalization applied cross maps. </div></div>
-<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a6743f0a130e8311e6f5b1a23df102472"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">arm_compute::test::validation::src</a></div><div class="ttdeci">convolution configure & src</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_8cpp_source.xhtml#l00133">Convolution.cpp:133</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a6743f0a130e8311e6f5b1a23df102472"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">arm_compute::test::validation::src</a></div><div class="ttdeci">convolution configure & src</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_8cpp_source.xhtml#l00147">Convolution.cpp:147</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad4bb8dabdbf8ad75e34220cc666b59caa6ff8bd96743aae9fd283cd822b84278e"><div class="ttname"><a href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59caa6ff8bd96743aae9fd283cd822b84278e">arm_compute::NormType::IN_MAP_2D</a></div><div class="ttdoc">Normalization applied within the same map in 2D region. </div></div>
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<li class="navelem"><a class="el" href="dir_59425e443f801f1f2fd8bbe4959a3ccf.xhtml">tests</a></li><li class="navelem"><a class="el" href="dir_e7c7b16542faa38cb4655ff1750d3604.xhtml">validation</a></li><li class="navelem"><a class="el" href="dir_46fdb196cebdbffe77dac340cde62f29.xhtml">reference</a></li><li class="navelem"><a class="el" href="validation_2reference_2_normalization_layer_8cpp.xhtml">NormalizationLayer.cpp</a></li>
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