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<div id="projectname">Compute Library
-  <span id="projectnumber">v17.06</span>
+  <span id="projectnumber">18.03</span>
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<div class="title">ActivationLayer.cpp</div> </div>
</div><!--header-->
<div class="contents">
-<a href="benchmark_2_n_e_o_n_2_activation_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="_globals_8h.xhtml">Globals.h</a>"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#include "<a class="code" href="_n_e_o_n_2_helper_8h.xhtml">NEON/Helper.h</a>"</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include "<a class="code" href="_n_e_accessor_8h.xhtml">NEON/NEAccessor.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="_tensor_library_8h.xhtml">TensorLibrary.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="benchmark_2_datasets_8h.xhtml">benchmark/Datasets.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="_profiler_8h.xhtml">benchmark/Profiler.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="_wall_clock_timer_8h.xhtml">benchmark/WallClockTimer.h</a>"</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_helpers_8h.xhtml">arm_compute/core/Helpers.h</a>"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</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="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include "<a class="code" href="_n_e_activation_layer_8h.xhtml">arm_compute/runtime/NEON/functions/NEActivationLayer.h</a>"</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2runtime_2_tensor_8h.xhtml">arm_compute/runtime/Tensor.h</a>"</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor">#include "<a class="code" href="_tensor_allocator_8h.xhtml">arm_compute/runtime/TensorAllocator.h</a>"</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="preprocessor">#include "benchmark/benchmark_api.h"</span></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="keyword">using namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a>;</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1test.xhtml">arm_compute::test</a>;</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1test_1_1benchmark.xhtml">arm_compute::test::benchmark</a>;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1test_1_1neon.xhtml">arm_compute::test::neon</a>;</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> </div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="preprocessor">#include "<a class="code" href="_activation_layer_8h.xhtml">benchmark/common/ActivationLayer.h</a>"</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="keyword">namespace</span></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">using</span> ActivationLayerAlexNetF32 = <a class="code" href="classarm__compute_1_1test_1_1benchmark_1_1_activation_layer.xhtml">ActivationLayer<AlexNetActivationLayerDataset, Tensor, NEAccessor, NEActivationLayer></a>;</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="keyword">using</span> ActivationLayerAlexNetQS8 = <a class="code" href="classarm__compute_1_1test_1_1benchmark_1_1_activation_layer.xhtml">ActivationLayer<AlexNetActivationLayerDataset, Tensor, NEAccessor, NEActivationLayer, DataType::QS8></a>;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="keyword">using</span> ActivationLayerLeNet5 = <a class="code" href="classarm__compute_1_1test_1_1benchmark_1_1_activation_layer.xhtml">ActivationLayer<LeNet5ActivationLayerDataset, Tensor, NEAccessor, NEActivationLayer, DataType::F32></a>;</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="keyword">using</span> ActivationLayerGoogLeNet = <a class="code" href="classarm__compute_1_1test_1_1benchmark_1_1_activation_layer.xhtml">ActivationLayer<GoogLeNetActivationLayerDataset, Tensor, NEAccessor, NEActivationLayer, DataType::F32></a>;</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> } <span class="comment">// namespace</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> <span class="comment">// F32</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> BENCHMARK_DEFINE_F(ActivationLayerAlexNetF32, neon_alexnet)</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> (::benchmark::State &<a class="code" href="benchmark_2_c_l_2_activation_layer_8cpp.xhtml#a7ff4fc3bbf305206c6f2b97d4e6681a0">state</a>)</div><div class="line"><a name="l00058"></a><span class="lineno"><a class="line" href="benchmark_2_n_e_o_n_2_activation_layer_8cpp.xhtml#af5df1344678ebbcc23857a8a93ccdf60"> 58</a></span> {</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordflow">while</span>(<a class="code" href="benchmark_2_c_l_2_activation_layer_8cpp.xhtml#a7ff4fc3bbf305206c6f2b97d4e6681a0">state</a>.KeepRunning())</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  {</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="comment">// Run function</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  profiler.start();</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  act_layer.run();</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  profiler.stop();</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> }</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> BENCHMARK_REGISTER_F(ActivationLayerAlexNetF32, neon_alexnet)</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> ->Threads(1)</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> ->Apply(DataSetArgBatched<AlexNetActivationLayerDataset, 0, 1, 4, 8>);</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> BENCHMARK_REGISTER_F(ActivationLayerAlexNetF32, neon_alexnet)</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> ->Threads(1)</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> ->Apply(DataSetArgBatched<AlexNetActivationLayerDataset, 1, 1, 4, 8>);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> BENCHMARK_REGISTER_F(ActivationLayerAlexNetF32, neon_alexnet)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> ->Threads(1)</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> ->Apply(DataSetArgBatched<AlexNetActivationLayerDataset, 2, 1, 4, 8>);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> BENCHMARK_REGISTER_F(ActivationLayerAlexNetF32, neon_alexnet)</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> ->Threads(1)</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> ->Apply(DataSetArgBatched<AlexNetActivationLayerDataset, 3, 1, 4, 8>);</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> BENCHMARK_REGISTER_F(ActivationLayerAlexNetF32, neon_alexnet)</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> ->Threads(1)</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> ->Apply(DataSetArgBatched<AlexNetActivationLayerDataset, 4, 1, 4, 8>);</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> <span class="comment">// QS8</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> BENCHMARK_DEFINE_F(ActivationLayerAlexNetQS8, neon_alexnet)</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> (::benchmark::State &<a class="code" href="benchmark_2_c_l_2_activation_layer_8cpp.xhtml#a7ff4fc3bbf305206c6f2b97d4e6681a0">state</a>)</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> {</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keywordflow">while</span>(<a class="code" href="benchmark_2_c_l_2_activation_layer_8cpp.xhtml#a7ff4fc3bbf305206c6f2b97d4e6681a0">state</a>.KeepRunning())</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>  <span class="comment">// Run function</span></div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  profiler.start();</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  act_layer.run();</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  profiler.stop();</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> }</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> BENCHMARK_REGISTER_F(ActivationLayerAlexNetQS8, neon_alexnet)</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> ->Threads(1)</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> ->Apply(DataSetArgBatched<AlexNetActivationLayerDataset, 0, 1, 4, 8>);</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> BENCHMARK_REGISTER_F(ActivationLayerAlexNetQS8, neon_alexnet)</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> ->Threads(1)</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> ->Apply(DataSetArgBatched<AlexNetActivationLayerDataset, 1, 1, 4, 8>);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> BENCHMARK_REGISTER_F(ActivationLayerAlexNetQS8, neon_alexnet)</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> ->Threads(1)</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> ->Apply(DataSetArgBatched<AlexNetActivationLayerDataset, 2, 1, 4, 8>);</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> BENCHMARK_REGISTER_F(ActivationLayerAlexNetQS8, neon_alexnet)</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> ->Threads(1)</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> ->Apply(DataSetArgBatched<AlexNetActivationLayerDataset, 3, 1, 4, 8>);</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> BENCHMARK_REGISTER_F(ActivationLayerAlexNetQS8, neon_alexnet)</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> ->Threads(1)</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> ->Apply(DataSetArgBatched<AlexNetActivationLayerDataset, 4, 1, 4, 8>);</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> </div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> BENCHMARK_DEFINE_F(ActivationLayerLeNet5, neon_lenet5)</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> (::benchmark::State &<a class="code" href="benchmark_2_c_l_2_activation_layer_8cpp.xhtml#a7ff4fc3bbf305206c6f2b97d4e6681a0">state</a>)</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="keywordflow">while</span>(<a class="code" href="benchmark_2_c_l_2_activation_layer_8cpp.xhtml#a7ff4fc3bbf305206c6f2b97d4e6681a0">state</a>.KeepRunning())</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>  <span class="comment">// Run function</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  profiler.start();</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  act_layer.run();</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  profiler.stop();</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> </div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> BENCHMARK_REGISTER_F(ActivationLayerLeNet5, neon_lenet5)</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> ->Threads(1)</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> ->Apply(DataSetArgBatched<LeNet5ActivationLayerDataset, 0, 1, 4, 8>);</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> </div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> BENCHMARK_DEFINE_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> (::benchmark::State &<a class="code" href="benchmark_2_c_l_2_activation_layer_8cpp.xhtml#a7ff4fc3bbf305206c6f2b97d4e6681a0">state</a>)</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> {</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keywordflow">while</span>(<a class="code" href="benchmark_2_c_l_2_activation_layer_8cpp.xhtml#a7ff4fc3bbf305206c6f2b97d4e6681a0">state</a>.KeepRunning())</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  {</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="comment">// Run function</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  profiler.start();</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  act_layer.run();</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  profiler.stop();</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> }</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> ->Threads(1)</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 0, 1, 4, 8>);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> ->Threads(1)</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 1, 1, 4, 8>);</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> ->Threads(1)</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 2, 1, 4, 8>);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> ->Threads(1)</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 3, 1, 4, 8>);</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> ->Threads(1)</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 4, 1, 4, 8>);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> ->Threads(1)</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 5, 1, 4, 8>);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> ->Threads(1)</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 6, 1, 4, 8>);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span> ->Threads(1)</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 7, 1, 4, 8>);</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> ->Threads(1)</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 8, 1, 4, 8>);</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> ->Threads(1)</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 9, 1, 4, 8>);</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> ->Threads(1)</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 10, 1, 4, 8>);</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> ->Threads(1)</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 11, 1, 4, 8>);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> ->Threads(1)</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 12, 1, 4, 8>);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> ->Threads(1)</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 13, 1, 4, 8>);</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> ->Threads(1)</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 14, 1, 4, 8>);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> ->Threads(1)</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 15, 1, 4, 8>);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> ->Threads(1)</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 16, 1, 4, 8>);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> ->Threads(1)</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 17, 1, 4, 8>);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> ->Threads(1)</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 18, 1, 4, 8>);</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> ->Threads(1)</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 19, 1, 4, 8>);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> ->Threads(1)</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 20, 1, 4, 8>);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> ->Threads(1)</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 21, 1, 4, 8>);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span> ->Threads(1)</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 22, 1, 4, 8>);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> ->Threads(1)</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 23, 1, 4, 8>);</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> ->Threads(1)</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 24, 1, 4, 8>);</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> ->Threads(1)</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 25, 1, 4, 8>);</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> ->Threads(1)</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 26, 1, 4, 8>);</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> ->Threads(1)</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 27, 1, 4, 8>);</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> ->Threads(1)</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 28, 1, 4, 8>);</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> ->Threads(1)</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 29, 1, 4, 8>);</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span> ->Threads(1)</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 30, 1, 4, 8>);</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> ->Threads(1)</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 31, 1, 4, 8>);</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> BENCHMARK_REGISTER_F(ActivationLayerGoogLeNet, neon_googlenet)</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span> ->Threads(1)</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> ->Apply(DataSetArgBatched<GoogLeNetActivationLayerDataset, 32, 1, 4, 8>);</div><div class="ttc" id="_profiler_8h_xhtml"><div class="ttname"><a href="_profiler_8h.xhtml">Profiler.h</a></div></div>
-<div class="ttc" id="arm__compute_2runtime_2_tensor_8h_xhtml"><div class="ttname"><a href="arm__compute_2runtime_2_tensor_8h.xhtml">Tensor.h</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1test_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml">arm_compute::test</a></div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_g_e_m_m_8h_source.xhtml#l00039">GEMM.h:39</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1test_1_1benchmark_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1benchmark.xhtml">arm_compute::test::benchmark</a></div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_g_e_m_m_8h_source.xhtml#l00041">GEMM.h:41</a></div></div>
-<div class="ttc" id="_tensor_library_8h_xhtml"><div class="ttname"><a href="_tensor_library_8h.xhtml">TensorLibrary.h</a></div></div>
+<a href="benchmark_2_n_e_o_n_2_activation_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) 2017-2018 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "<a class="code" href="_tensor_shape_8h.xhtml">arm_compute/core/TensorShape.h</a>"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</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="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include "<a class="code" href="_n_e_activation_layer_8h.xhtml">arm_compute/runtime/NEON/functions/NEActivationLayer.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="runtime_2_tensor_8h.xhtml">arm_compute/runtime/Tensor.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_tensor_allocator_8h.xhtml">arm_compute/runtime/TensorAllocator.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="_accessor_8h.xhtml">tests/NEON/Accessor.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="benchmark_2fixtures_2_activation_layer_fixture_8h.xhtml">tests/benchmark/fixtures/ActivationLayerFixture.h</a>"</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "<a class="code" href="_alex_net_activation_layer_dataset_8h.xhtml">tests/datasets/system_tests/alexnet/AlexNetActivationLayerDataset.h</a>"</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="_goog_le_net_inception_v1_activation_layer_dataset_8h.xhtml">tests/datasets/system_tests/googlenet/inceptionv1/GoogLeNetInceptionV1ActivationLayerDataset.h</a>"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include "<a class="code" href="_goog_le_net_inception_v4_activation_layer_dataset_8h.xhtml">tests/datasets/system_tests/googlenet/inceptionv4/GoogLeNetInceptionV4ActivationLayerDataset.h</a>"</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include "<a class="code" href="_le_net5_activation_layer_dataset_8h.xhtml">tests/datasets/system_tests/lenet5/LeNet5ActivationLayerDataset.h</a>"</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include "<a class="code" href="_mobile_net_activation_layer_dataset_8h.xhtml">tests/datasets/system_tests/mobilenet/MobileNetActivationLayerDataset.h</a>"</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor">#include "<a class="code" href="_squeeze_net_activation_layer_dataset_8h.xhtml">tests/datasets/system_tests/squeezenet/SqueezeNetActivationLayerDataset.h</a>"</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#include "<a class="code" href="_v_g_g16_activation_layer_dataset_8h.xhtml">tests/datasets/system_tests/vgg/vgg16/VGG16ActivationLayerDataset.h</a>"</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="preprocessor">#include "<a class="code" href="_y_o_l_o_v2_activation_layer_dataset_8h.xhtml">tests/datasets/system_tests/yolo/v2/YOLOV2ActivationLayerDataset.h</a>"</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="preprocessor">#include "<a class="code" href="tests_2framework_2_macros_8h.xhtml">tests/framework/Macros.h</a>"</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="preprocessor">#include "<a class="code" href="_datasets_8h.xhtml">tests/framework/datasets/Datasets.h</a>"</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="preprocessor">#include "<a class="code" href="_type_printer_8h.xhtml">utils/TypePrinter.h</a>"</span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> {</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="keyword">namespace </span>test</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> {</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="keyword">namespace</span></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="preprocessor">#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="keyword">const</span> <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a> = <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"DataType"</span>, { <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">DataType::QS8</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9">DataType::QS16</a> });</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="keyword">const</span> <span class="keyword">auto</span> data_types_mobilenet = <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"DataType"</span>, { <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">DataType::QS8</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9">DataType::QS16</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a> });</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="preprocessor">#else </span><span class="comment">/* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */</span><span class="preprocessor"></span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> <span class="keyword">const</span> <span class="keyword">auto</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">data_types</a> = <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"DataType"</span>, { <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">DataType::QS8</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9">DataType::QS16</a> });</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> <span class="keyword">const</span> <span class="keyword">auto</span> data_types_mobilenet = <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"DataType"</span>, { <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">DataType::QS8</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9">DataType::QS16</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a> });</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="preprocessor">#endif </span><span class="comment">/* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */</span><span class="preprocessor"></span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> } <span class="comment">// namespace</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"><a class="line" href="namespacearm__compute_1_1test.xhtml#aeded391cb7ec7a44c41eb23544265894"> 58</a></span> <span class="keyword">using</span> <a class="code" href="classarm__compute_1_1test_1_1_activation_layer_fixture.xhtml">NEActivationLayerFixture</a> = <a class="code" href="classarm__compute_1_1test_1_1_activation_layer_fixture.xhtml">ActivationLayerFixture<Tensor, NEActivationLayer, Accessor></a>;</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="tests_2framework_2_macros_8h.xhtml#acd09bed517e43d28823e69494f259835">TEST_SUITE</a>(NEON)</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> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(AlexNetActivationLayer, <a class="code" href="classarm__compute_1_1test_1_1_activation_layer_fixture.xhtml">NEActivationLayerFixture</a>, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::AlexNetActivationLayerDataset(), data_types),</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("Batches", 1)));</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> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(LeNet5ActivationLayer, NEActivationLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::LeNet5ActivationLayerDataset(), data_types),</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("Batches", 1)));</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> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(MobileNetActivationLayer, NEActivationLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::MobileNetActivationLayerDataset(), data_types_mobilenet),</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("Batches", 1)));</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(GoogLeNetInceptionV1ActivationLayer, NEActivationLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::GoogLeNetInceptionV1ActivationLayerDataset(), data_types),</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("Batches", 1)));</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> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(GoogLeNetInceptionV4ActivationLayer, NEActivationLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::GoogLeNetInceptionV4ActivationLayerDataset(), data_types),</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("Batches", 1)));</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="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(SqueezeNetActivationLayer, NEActivationLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::SqueezeNetActivationLayerDataset(), data_types),</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("Batches", 1)));</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> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(VGG16ActivationLayer, NEActivationLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::VGG16ActivationLayerDataset(), data_types),</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("Batches", 1)));</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> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(YOLOV2ActivationLayer, NEActivationLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::ALL,</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::YOLOV2ActivationLayerDataset(), data_types),</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("Batches", 1)));</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> <a class="code" href="tests_2framework_2_macros_8h.xhtml#acd09bed517e43d28823e69494f259835">TEST_SUITE</a>(NIGHTLY)</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(AlexNetActivationLayer, NEActivationLayerFixture, framework::<a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">DatasetMode</a>::NIGHTLY,</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">combine</a>(datasets::AlexNetActivationLayerDataset(), data_types),</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  framework::dataset::<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">make</a>("Batches", { 4, 8 })));</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> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(LeNet5ActivationLayer, <a class="code" href="classarm__compute_1_1test_1_1_activation_layer_fixture.xhtml">NEActivationLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="code" href="classarm__compute_1_1test_1_1datasets_1_1_le_net5_activation_layer_dataset.xhtml">datasets::LeNet5ActivationLayerDataset</a>(), data_types),</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"Batches"</span>, { 4, 8 })));</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> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(MobileNetActivationLayer, <a class="code" href="classarm__compute_1_1test_1_1_activation_layer_fixture.xhtml">NEActivationLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="code" href="classarm__compute_1_1test_1_1datasets_1_1_mobile_net_activation_layer_dataset.xhtml">datasets::MobileNetActivationLayerDataset</a>(), data_types_mobilenet),</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"Batches"</span>, { 4, 8 })));</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> </div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(GoogLeNetInceptionV1ActivationLayer, <a class="code" href="classarm__compute_1_1test_1_1_activation_layer_fixture.xhtml">NEActivationLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="code" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_activation_layer_dataset.xhtml">datasets::GoogLeNetInceptionV1ActivationLayerDataset</a>(), data_types),</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"Batches"</span>, { 4, 8 })));</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> </div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(GoogLeNetInceptionV4ActivationLayer, <a class="code" href="classarm__compute_1_1test_1_1_activation_layer_fixture.xhtml">NEActivationLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="code" href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_activation_layer_dataset.xhtml">datasets::GoogLeNetInceptionV4ActivationLayerDataset</a>(), data_types),</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"Batches"</span>, { 4, 8 })));</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> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(SqueezeNetActivationLayer, <a class="code" href="classarm__compute_1_1test_1_1_activation_layer_fixture.xhtml">NEActivationLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="code" href="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_activation_layer_dataset.xhtml">datasets::SqueezeNetActivationLayerDataset</a>(), data_types),</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"Batches"</span>, { 4, 8 })));</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(VGG16ActivationLayer, <a class="code" href="classarm__compute_1_1test_1_1_activation_layer_fixture.xhtml">NEActivationLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="code" href="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_activation_layer_dataset.xhtml">datasets::VGG16ActivationLayerDataset</a>(), data_types),</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"Batches"</span>, { 2 })));</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> </div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> <a class="code" href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">REGISTER_FIXTURE_DATA_TEST_CASE</a>(YOLOV2ActivationLayer, <a class="code" href="classarm__compute_1_1test_1_1_activation_layer_fixture.xhtml">NEActivationLayerFixture</a>, <a class="code" href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">framework::DatasetMode::NIGHTLY</a>,</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">framework::dataset::combine</a>(<a class="code" href="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_activation_layer_dataset.xhtml">datasets::YOLOV2ActivationLayerDataset</a>(), data_types),</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <a class="code" href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">framework::dataset::make</a>(<span class="stringliteral">"Batches"</span>, { 4, 8 })));</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> <a class="code" href="tests_2framework_2_macros_8h.xhtml#a603cb7f45efd81606e51686da9aeebd9">TEST_SUITE_END</a>()</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> <a class="code" href="tests_2framework_2_macros_8h.xhtml#a603cb7f45efd81606e51686da9aeebd9">TEST_SUITE_END</a>()</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> } <span class="comment">// namespace test</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> } <span class="comment">// namespace arm_compute</span></div><div class="ttc" id="_alex_net_activation_layer_dataset_8h_xhtml"><div class="ttname"><a href="_alex_net_activation_layer_dataset_8h.xhtml">AlexNetActivationLayerDataset.h</a></div></div>
+<div class="ttc" id="_accessor_8h_xhtml"><div class="ttname"><a href="_accessor_8h.xhtml">Accessor.h</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a48d877702a2957f5a932c43a357866f9">arm_compute::DataType::QS16</a></div></div>
+<div class="ttc" id="benchmark_2fixtures_2_activation_layer_fixture_8h_xhtml"><div class="ttname"><a href="benchmark_2fixtures_2_activation_layer_fixture_8h.xhtml">ActivationLayerFixture.h</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a11cde4d3551db3f9498d339a67189543">arm_compute::DataType::QS8</a></div></div>
+<div class="ttc" id="_v_g_g16_activation_layer_dataset_8h_xhtml"><div class="ttname"><a href="_v_g_g16_activation_layer_dataset_8h.xhtml">VGG16ActivationLayerDataset.h</a></div></div>
+<div class="ttc" id="_y_o_l_o_v2_activation_layer_dataset_8h_xhtml"><div class="ttname"><a href="_y_o_l_o_v2_activation_layer_dataset_8h.xhtml">YOLOV2ActivationLayerDataset.h</a></div></div>
<div class="ttc" id="_tensor_allocator_8h_xhtml"><div class="ttname"><a href="_tensor_allocator_8h.xhtml">TensorAllocator.h</a></div></div>
-<div class="ttc" id="_wall_clock_timer_8h_xhtml"><div class="ttname"><a href="_wall_clock_timer_8h.xhtml">WallClockTimer.h</a></div></div>
-<div class="ttc" id="_n_e_accessor_8h_xhtml"><div class="ttname"><a href="_n_e_accessor_8h.xhtml">NEAccessor.h</a></div></div>
-<div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></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_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_1test_1_1datasets_1_1_le_net5_activation_layer_dataset_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1datasets_1_1_le_net5_activation_layer_dataset.xhtml">arm_compute::test::datasets::LeNet5ActivationLayerDataset</a></div><div class="ttdef"><b>Definition:</b> <a href="_le_net5_activation_layer_dataset_8h_source.xhtml#l00040">LeNet5ActivationLayerDataset.h:40</a></div></div>
+<div class="ttc" id="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_activation_layer_dataset_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1datasets_1_1_v_g_g16_activation_layer_dataset.xhtml">arm_compute::test::datasets::VGG16ActivationLayerDataset</a></div><div class="ttdef"><b>Definition:</b> <a href="_v_g_g16_activation_layer_dataset_8h_source.xhtml#l00040">VGG16ActivationLayerDataset.h:40</a></div></div>
+<div class="ttc" id="_le_net5_activation_layer_dataset_8h_xhtml"><div class="ttname"><a href="_le_net5_activation_layer_dataset_8h.xhtml">LeNet5ActivationLayerDataset.h</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1framework_1_1dataset_xhtml_a352791fb808d42a82ad70df5efa3508b"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a352791fb808d42a82ad70df5efa3508b">arm_compute::test::framework::dataset::make</a></div><div class="ttdeci">std::enable_if< is_container< T >::value, ContainerDataset< T > >::type make(std::string name, T &&values)</div><div class="ttdoc">Helper function to create a ContainerDataset. </div><div class="ttdef"><b>Definition:</b> <a href="_container_dataset_8h_source.xhtml#l00141">ContainerDataset.h:141</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="00__introduction_8dox_source.xhtml#l00001">00_introduction.dox:1</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::Format::F16</a></div><div class="ttdoc">1 channel, 1 F16 per channel </div></div>
+<div class="ttc" id="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_activation_layer_dataset_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1datasets_1_1_squeeze_net_activation_layer_dataset.xhtml">arm_compute::test::datasets::SqueezeNetActivationLayerDataset</a></div><div class="ttdef"><b>Definition:</b> <a href="_squeeze_net_activation_layer_dataset_8h_source.xhtml#l00040">SqueezeNetActivationLayerDataset.h:40</a></div></div>
+<div class="ttc" id="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_activation_layer_dataset_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1datasets_1_1_y_o_l_o_v2_activation_layer_dataset.xhtml">arm_compute::test::datasets::YOLOV2ActivationLayerDataset</a></div><div class="ttdef"><b>Definition:</b> <a href="_y_o_l_o_v2_activation_layer_dataset_8h_source.xhtml#l00092">YOLOV2ActivationLayerDataset.h:92</a></div></div>
+<div class="ttc" id="tests_2framework_2_macros_8h_xhtml_acd09bed517e43d28823e69494f259835"><div class="ttname"><a href="tests_2framework_2_macros_8h.xhtml#acd09bed517e43d28823e69494f259835">TEST_SUITE</a></div><div class="ttdeci">#define TEST_SUITE(SUITE_NAME)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2framework_2_macros_8h_source.xhtml#l00034">Macros.h:34</a></div></div>
<div class="ttc" id="_n_e_activation_layer_8h_xhtml"><div class="ttname"><a href="_n_e_activation_layer_8h.xhtml">NEActivationLayer.h</a></div></div>
-<div class="ttc" id="_globals_8h_xhtml"><div class="ttname"><a href="_globals_8h.xhtml">Globals.h</a></div></div>
-<div class="ttc" id="benchmark_2_c_l_2_activation_layer_8cpp_xhtml_a7ff4fc3bbf305206c6f2b97d4e6681a0"><div class="ttname"><a href="benchmark_2_c_l_2_activation_layer_8cpp.xhtml#a7ff4fc3bbf305206c6f2b97d4e6681a0">state</a></div><div class="ttdeci">::benchmark::State & state</div><div class="ttdef"><b>Definition:</b> <a href="benchmark_2_c_l_2_activation_layer_8cpp_source.xhtml#l00057">ActivationLayer.cpp:57</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1test_1_1neon_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1neon.xhtml">arm_compute::test::neon</a></div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_helper_8h_source.xhtml#l00036">Helper.h:36</a></div></div>
-<div class="ttc" id="benchmark_2_datasets_8h_xhtml"><div class="ttname"><a href="benchmark_2_datasets_8h.xhtml">Datasets.h</a></div></div>
-<div class="ttc" id="classarm__compute_1_1test_1_1benchmark_1_1_activation_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1benchmark_1_1_activation_layer.xhtml">arm_compute::test::benchmark::ActivationLayer</a></div><div class="ttdef"><b>Definition:</b> <a href="_activation_layer_8h_source.xhtml#l00044">ActivationLayer.h:44</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="_n_e_o_n_2_helper_8h_xhtml"><div class="ttname"><a href="_n_e_o_n_2_helper_8h.xhtml">Helper.h</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_xhtml_aab9a2ff74a27ae837d32a79a38952228"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#aab9a2ff74a27ae837d32a79a38952228">arm_compute::test::data_types</a></div><div class="ttdeci">const auto data_types</div><div class="ttdef"><b>Definition:</b> <a href="benchmark_2_c_l_2_depthwise_convolution_layer_8cpp_source.xhtml#l00040">DepthwiseConvolutionLayer.cpp:40</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1framework_xhtml_a7edf31944a6144ffd493d2f9a7bfb5cf"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cf">arm_compute::test::framework::DatasetMode</a></div><div class="ttdeci">DatasetMode</div><div class="ttdoc">Possible dataset modes. </div><div class="ttdef"><b>Definition:</b> <a href="_dataset_modes_8h_source.xhtml#l00040">DatasetModes.h:40</a></div></div>
+<div class="ttc" id="_squeeze_net_activation_layer_dataset_8h_xhtml"><div class="ttname"><a href="_squeeze_net_activation_layer_dataset_8h.xhtml">SqueezeNetActivationLayerDataset.h</a></div></div>
+<div class="ttc" id="_datasets_8h_xhtml"><div class="ttname"><a href="_datasets_8h.xhtml">Datasets.h</a></div></div>
+<div class="ttc" id="_type_printer_8h_xhtml"><div class="ttname"><a href="_type_printer_8h.xhtml">TypePrinter.h</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>
+<div class="ttc" id="_mobile_net_activation_layer_dataset_8h_xhtml"><div class="ttname"><a href="_mobile_net_activation_layer_dataset_8h.xhtml">MobileNetActivationLayerDataset.h</a></div></div>
+<div class="ttc" id="tests_2framework_2_macros_8h_xhtml"><div class="ttname"><a href="tests_2framework_2_macros_8h.xhtml">Macros.h</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_xhtml_a2be51cbb1f1e5aaf0a5baeccdb2e3354"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a2be51cbb1f1e5aaf0a5baeccdb2e3354">arm_compute::test::REGISTER_FIXTURE_DATA_TEST_CASE</a></div><div class="ttdeci">REGISTER_FIXTURE_DATA_TEST_CASE(MobileNetBatchNormalizationLayer, CLBatchNormalizationLayerFixture, framework::DatasetMode::ALL, framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::MobileNetBatchNormalizationLayerDataset(), framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))), data_types), framework::dataset::make("Batches", 1)))</div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1framework_1_1dataset_xhtml_a6f4fa4bb0583f29e77138fb1e7d77411"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1framework_1_1dataset.xhtml#a6f4fa4bb0583f29e77138fb1e7d77411">arm_compute::test::framework::dataset::combine</a></div><div class="ttdeci">CartesianProductDataset< T, U > combine(T &&dataset1, U &&dataset2)</div><div class="ttdoc">Helper function to create a CartesianProductDataset. </div><div class="ttdef"><b>Definition:</b> <a href="_cartesian_product_dataset_8h_source.xhtml#l00157">CartesianProductDataset.h:157</a></div></div>
+<div class="ttc" id="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_activation_layer_dataset_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v4_activation_layer_dataset.xhtml">arm_compute::test::datasets::GoogLeNetInceptionV4ActivationLayerDataset</a></div><div class="ttdef"><b>Definition:</b> <a href="_goog_le_net_inception_v4_activation_layer_dataset_8h_source.xhtml#l00040">GoogLeNetInceptionV4ActivationLayerDataset.h:40</a></div></div>
+<div class="ttc" id="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_activation_layer_dataset_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1datasets_1_1_goog_le_net_inception_v1_activation_layer_dataset.xhtml">arm_compute::test::datasets::GoogLeNetInceptionV1ActivationLayerDataset</a></div><div class="ttdef"><b>Definition:</b> <a href="_goog_le_net_inception_v1_activation_layer_dataset_8h_source.xhtml#l00040">GoogLeNetInceptionV1ActivationLayerDataset.h:40</a></div></div>
+<div class="ttc" id="classarm__compute_1_1test_1_1datasets_1_1_mobile_net_activation_layer_dataset_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1datasets_1_1_mobile_net_activation_layer_dataset.xhtml">arm_compute::test::datasets::MobileNetActivationLayerDataset</a></div><div class="ttdef"><b>Definition:</b> <a href="_mobile_net_activation_layer_dataset_8h_source.xhtml#l00038">MobileNetActivationLayerDataset.h:38</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1test_1_1framework_xhtml_a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1framework.xhtml#a7edf31944a6144ffd493d2f9a7bfb5cfad791cd54143e331164a2b216451a5dd3">arm_compute::test::framework::DatasetMode::NIGHTLY</a></div></div>
+<div class="ttc" id="classarm__compute_1_1test_1_1_activation_layer_fixture_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_activation_layer_fixture.xhtml">arm_compute::test::ActivationLayerFixture</a></div><div class="ttdoc">Fixture that can be used for NEON and CL. </div><div class="ttdef"><b>Definition:</b> <a href="benchmark_2fixtures_2_activation_layer_fixture_8h_source.xhtml#l00039">ActivationLayerFixture.h:39</a></div></div>
+<div class="ttc" id="_goog_le_net_inception_v1_activation_layer_dataset_8h_xhtml"><div class="ttname"><a href="_goog_le_net_inception_v1_activation_layer_dataset_8h.xhtml">GoogLeNetInceptionV1ActivationLayerDataset.h</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="_activation_layer_8h_xhtml"><div class="ttname"><a href="_activation_layer_8h.xhtml">ActivationLayer.h</a></div></div>
+<div class="ttc" id="runtime_2_tensor_8h_xhtml"><div class="ttname"><a href="runtime_2_tensor_8h.xhtml">Tensor.h</a></div></div>
+<div class="ttc" id="_goog_le_net_inception_v4_activation_layer_dataset_8h_xhtml"><div class="ttname"><a href="_goog_le_net_inception_v4_activation_layer_dataset_8h.xhtml">GoogLeNetInceptionV4ActivationLayerDataset.h</a></div></div>
+<div class="ttc" id="_tensor_shape_8h_xhtml"><div class="ttname"><a href="_tensor_shape_8h.xhtml">TensorShape.h</a></div></div>
+<div class="ttc" id="tests_2framework_2_macros_8h_xhtml_a603cb7f45efd81606e51686da9aeebd9"><div class="ttname"><a href="tests_2framework_2_macros_8h.xhtml#a603cb7f45efd81606e51686da9aeebd9">TEST_SUITE_END</a></div><div class="ttdeci">#define TEST_SUITE_END()</div><div class="ttdef"><b>Definition:</b> <a href="tests_2framework_2_macros_8h_source.xhtml#l00039">Macros.h:39</a></div></div>
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